diff --git a/plugins/ARfitStudio/index.md b/plugins/ARfitStudio/index.md index 45ad6ca..4b77864 100644 --- a/plugins/ARfitStudio/index.md +++ b/plugins/ARfitStudio/index.md @@ -3,7 +3,6 @@ layout: default title: ARfitStudio long_title: ARfitStudio parent: Plugins -has_children: true nav_order: 18 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/ARfitStudio). diff --git "a/plugins/EEG-BIDS/EEG\342\200\220BIDS-docs.md" "b/plugins/EEG-BIDS/EEG\342\200\220BIDS-docs.md" index ed53c0d..ac5c81c 100644 --- "a/plugins/EEG-BIDS/EEG\342\200\220BIDS-docs.md" +++ "b/plugins/EEG-BIDS/EEG\342\200\220BIDS-docs.md" @@ -1,7 +1,7 @@ --- layout: default -title: EEG‐BIDS-docs -long_title: EEG‐BIDS-docs +title: EEG-BIDS +long_title: EEG-BIDS parent: EEG-BIDS grand_parent: Plugins --- diff --git a/plugins/EEG-BIDS/index.md b/plugins/EEG-BIDS/index.md index 5361652..0eac903 100644 --- a/plugins/EEG-BIDS/index.md +++ b/plugins/EEG-BIDS/index.md @@ -16,7 +16,7 @@ The EEG-BIDS (formerly known as **BIDS-MATLAB-tools**) repository contains a col # Documentation -Refer to the [wiki documentation](https://github.com/sccn/EEG-BIDS/wiki). +Refer to the [wiki documentation](https://github.com/sccn/EEG-BIDS/wiki) or the submenus of this plugin if you are on the EEGLAB website. # EEG-BIDS vs other BIDS software diff --git a/plugins/ICLabel/index.md b/plugins/ICLabel/index.md index d341c68..34a276c 100644 --- a/plugins/ICLabel/index.md +++ b/plugins/ICLabel/index.md @@ -3,7 +3,6 @@ layout: default title: ICLabel long_title: ICLabel parent: Plugins -has_children: true nav_order: 0 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/ICLabel). diff --git a/plugins/NFT/Chapter_01_Getting_Started_with_NFT.md b/plugins/NFT/Chapter_01_Getting_Started_with_NFT.md index 8ab63b7..a0fd0db 100644 --- a/plugins/NFT/Chapter_01_Getting_Started_with_NFT.md +++ b/plugins/NFT/Chapter_01_Getting_Started_with_NFT.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter_01_Getting_Started_with_NFT -long_title: Chapter_01_Getting_Started_with_NFT +title: NFT +long_title: NFT parent: NFT grand_parent: Plugins --- diff --git a/plugins/NFT/Chapter_02_Head_Modeling_from_MR_Images.md b/plugins/NFT/Chapter_02_Head_Modeling_from_MR_Images.md index 4ce0543..aa99119 100644 --- a/plugins/NFT/Chapter_02_Head_Modeling_from_MR_Images.md +++ b/plugins/NFT/Chapter_02_Head_Modeling_from_MR_Images.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter_02_Head_Modeling_from_MR_Images -long_title: Chapter_02_Head_Modeling_from_MR_Images +title: NFT +long_title: NFT parent: NFT grand_parent: Plugins --- diff --git a/plugins/NFT/Chapter_03_Forward_Model_Generation.md b/plugins/NFT/Chapter_03_Forward_Model_Generation.md index a8e9faa..8ed4d3f 100644 --- a/plugins/NFT/Chapter_03_Forward_Model_Generation.md +++ b/plugins/NFT/Chapter_03_Forward_Model_Generation.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter_03_Forward_Model_Generation -long_title: Chapter_03_Forward_Model_Generation +title: NFT +long_title: NFT parent: NFT grand_parent: Plugins --- diff --git a/plugins/NFT/Chapter_04_NFT_Examples.md b/plugins/NFT/Chapter_04_NFT_Examples.md index a02a6f9..221a250 100644 --- a/plugins/NFT/Chapter_04_NFT_Examples.md +++ b/plugins/NFT/Chapter_04_NFT_Examples.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter_04_NFT_Examples -long_title: Chapter_04_NFT_Examples +title: NFT +long_title: NFT parent: NFT grand_parent: Plugins --- diff --git a/plugins/NFT/Chapter_05_NFT_Commands_and_Functions.md b/plugins/NFT/Chapter_05_NFT_Commands_and_Functions.md index fbc871a..f11ab5e 100644 --- a/plugins/NFT/Chapter_05_NFT_Commands_and_Functions.md +++ b/plugins/NFT/Chapter_05_NFT_Commands_and_Functions.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter_05_NFT_Commands_and_Functions -long_title: Chapter_05_NFT_Commands_and_Functions +title: NFT +long_title: NFT parent: NFT grand_parent: Plugins --- diff --git a/plugins/NFT/NFT_Appendix_A.md b/plugins/NFT/NFT_Appendix_A.md index a5aba26..74ce037 100644 --- a/plugins/NFT/NFT_Appendix_A.md +++ b/plugins/NFT/NFT_Appendix_A.md @@ -1,7 +1,7 @@ --- layout: default -title: NFT_Appendix_A -long_title: NFT_Appendix_A +title: NFT +long_title: NFT parent: NFT grand_parent: Plugins --- diff --git a/plugins/NFT/NFT_Appendix_B.md b/plugins/NFT/NFT_Appendix_B.md index f968f39..c78da53 100644 --- a/plugins/NFT/NFT_Appendix_B.md +++ b/plugins/NFT/NFT_Appendix_B.md @@ -1,7 +1,7 @@ --- layout: default -title: NFT_Appendix_B -long_title: NFT_Appendix_B +title: NFT +long_title: NFT parent: NFT grand_parent: Plugins --- diff --git a/plugins/NFT/NFT_Appendix_C.md b/plugins/NFT/NFT_Appendix_C.md index 4192cc6..8ad9a5e 100644 --- a/plugins/NFT/NFT_Appendix_C.md +++ b/plugins/NFT/NFT_Appendix_C.md @@ -1,7 +1,7 @@ --- layout: default -title: NFT_Appendix_C -long_title: NFT_Appendix_C +title: NFT +long_title: NFT parent: NFT grand_parent: Plugins --- diff --git a/plugins/NIMA/index.md b/plugins/NIMA/index.md index e8bc44b..1c76007 100644 --- a/plugins/NIMA/index.md +++ b/plugins/NIMA/index.md @@ -3,7 +3,6 @@ layout: default title: NIMA long_title: NIMA parent: Plugins -has_children: true nav_order: 14 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/NIMA). diff --git a/plugins/PACT/index.md b/plugins/PACT/index.md index 5024b36..77fec8d 100644 --- a/plugins/PACT/index.md +++ b/plugins/PACT/index.md @@ -3,7 +3,6 @@ layout: default title: PACT long_title: PACT parent: Plugins -has_children: true nav_order: 15 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/PACT). diff --git a/plugins/PACTools/index.md b/plugins/PACTools/index.md index da04002..ab810ec 100644 --- a/plugins/PACTools/index.md +++ b/plugins/PACTools/index.md @@ -3,7 +3,6 @@ layout: default title: PACTools long_title: PACTools parent: Plugins -has_children: true nav_order: 17 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/PACTools). diff --git a/plugins/PowPowCAT/index.md b/plugins/PowPowCAT/index.md index 11da3f7..57cfd1d 100644 --- a/plugins/PowPowCAT/index.md +++ b/plugins/PowPowCAT/index.md @@ -3,7 +3,6 @@ layout: default title: PowPowCAT long_title: PowPowCAT parent: Plugins -has_children: true nav_order: 19 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/PowPowCAT). diff --git a/plugins/SIFT/Chapter-1.-Downloads.md b/plugins/SIFT/Chapter-1.-Downloads.md index 87b5fc7..3e2719a 100644 --- a/plugins/SIFT/Chapter-1.-Downloads.md +++ b/plugins/SIFT/Chapter-1.-Downloads.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter-1.-Downloads -long_title: Chapter-1.-Downloads +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/Chapter-2.-Introduction.md b/plugins/SIFT/Chapter-2.-Introduction.md index e6d36d8..dc6b351 100644 --- a/plugins/SIFT/Chapter-2.-Introduction.md +++ b/plugins/SIFT/Chapter-2.-Introduction.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter-2.-Introduction -long_title: Chapter-2.-Introduction +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/Chapter-3-and-4-Theory.md b/plugins/SIFT/Chapter-3-and-4-Theory.md index c2e3320..6acb571 100644 --- a/plugins/SIFT/Chapter-3-and-4-Theory.md +++ b/plugins/SIFT/Chapter-3-and-4-Theory.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter-3-and-4-Theory -long_title: Chapter-3-and-4-Theory +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/Chapter-5.-Computing-connectivity.md b/plugins/SIFT/Chapter-5.-Computing-connectivity.md index 04c2941..aeb3eb4 100644 --- a/plugins/SIFT/Chapter-5.-Computing-connectivity.md +++ b/plugins/SIFT/Chapter-5.-Computing-connectivity.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter-5.-Computing-connectivity -long_title: Chapter-5.-Computing-connectivity +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/Chapter-5.1.-SIFT-install.md b/plugins/SIFT/Chapter-5.1.-SIFT-install.md index d7d9efd..beb7c2a 100644 --- a/plugins/SIFT/Chapter-5.1.-SIFT-install.md +++ b/plugins/SIFT/Chapter-5.1.-SIFT-install.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter-5.1.-SIFT-install -long_title: Chapter-5.1.-SIFT-install +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/Chapter-5.2.-Loading-and-preparing-the-data.md b/plugins/SIFT/Chapter-5.2.-Loading-and-preparing-the-data.md index 422ab1b..9192b25 100644 --- a/plugins/SIFT/Chapter-5.2.-Loading-and-preparing-the-data.md +++ b/plugins/SIFT/Chapter-5.2.-Loading-and-preparing-the-data.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter-5.2.-Loading-and-preparing-the-data -long_title: Chapter-5.2.-Loading-and-preparing-the-data +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/Chapter-5.3.-SIFT-preprocessing.md b/plugins/SIFT/Chapter-5.3.-SIFT-preprocessing.md index 6f71858..b55b23f 100644 --- a/plugins/SIFT/Chapter-5.3.-SIFT-preprocessing.md +++ b/plugins/SIFT/Chapter-5.3.-SIFT-preprocessing.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter-5.3.-SIFT-preprocessing -long_title: Chapter-5.3.-SIFT-preprocessing +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/Chapter-5.4.-Model-Fitting-and-Validation.md b/plugins/SIFT/Chapter-5.4.-Model-Fitting-and-Validation.md index 763944f..b231bd0 100644 --- a/plugins/SIFT/Chapter-5.4.-Model-Fitting-and-Validation.md +++ b/plugins/SIFT/Chapter-5.4.-Model-Fitting-and-Validation.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter-5.4.-Model-Fitting-and-Validation -long_title: Chapter-5.4.-Model-Fitting-and-Validation +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/Chapter-5.5.-Connectivity-Estimation.md b/plugins/SIFT/Chapter-5.5.-Connectivity-Estimation.md index 7978c7a..89ddd85 100644 --- a/plugins/SIFT/Chapter-5.5.-Connectivity-Estimation.md +++ b/plugins/SIFT/Chapter-5.5.-Connectivity-Estimation.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter-5.5.-Connectivity-Estimation -long_title: Chapter-5.5.-Connectivity-Estimation +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/Chapter-6.-Visualization.md b/plugins/SIFT/Chapter-6.-Visualization.md index 6f22cc4..5728eca 100644 --- a/plugins/SIFT/Chapter-6.-Visualization.md +++ b/plugins/SIFT/Chapter-6.-Visualization.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter-6.-Visualization -long_title: Chapter-6.-Visualization +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/Chapter-7.-Statistics-in-SIFT.md b/plugins/SIFT/Chapter-7.-Statistics-in-SIFT.md index e76936b..9b4e10f 100644 --- a/plugins/SIFT/Chapter-7.-Statistics-in-SIFT.md +++ b/plugins/SIFT/Chapter-7.-Statistics-in-SIFT.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter-7.-Statistics-in-SIFT -long_title: Chapter-7.-Statistics-in-SIFT +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/Chapter-8.-Conclusions-and-Acknowledgements.md b/plugins/SIFT/Chapter-8.-Conclusions-and-Acknowledgements.md index eb1f50f..945dc32 100644 --- a/plugins/SIFT/Chapter-8.-Conclusions-and-Acknowledgements.md +++ b/plugins/SIFT/Chapter-8.-Conclusions-and-Acknowledgements.md @@ -1,7 +1,7 @@ --- layout: default -title: Chapter-8.-Conclusions-and-Acknowledgements -long_title: Chapter-8.-Conclusions-and-Acknowledgements +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/Function-Reference.md b/plugins/SIFT/Function-Reference.md index ab8d626..6fadd59 100644 --- a/plugins/SIFT/Function-Reference.md +++ b/plugins/SIFT/Function-Reference.md @@ -1,7 +1,7 @@ --- layout: default -title: Function-Reference -long_title: Function-Reference +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/References.md b/plugins/SIFT/References.md index 9747947..a9e3633 100644 --- a/plugins/SIFT/References.md +++ b/plugins/SIFT/References.md @@ -1,7 +1,7 @@ --- layout: default -title: References -long_title: References +title: SIFT +long_title: SIFT parent: SIFT grand_parent: Plugins --- diff --git a/plugins/SIFT/index.md b/plugins/SIFT/index.md index 6fd0dae..88b3e8e 100644 --- a/plugins/SIFT/index.md +++ b/plugins/SIFT/index.md @@ -56,9 +56,9 @@ SIFT makes use of routines from (or is inspired by) the following open-source pa - [BCILAB](http://sccn.ucsd.edu/wiki/BCILAB) (Kothe et al) -## Official Website +## Documentation -[SIFT page in the SCCN wiki](http://sccn.ucsd.edu/wiki/SIFT) +See the [SIFT wiki](http://sccn.ucsd.edu/wiki/SIFT) or use the submenus if you are looking at this page on the EEGLAB website. ## Citation diff --git a/plugins/amica/AMICA-Compilation-instructions.md b/plugins/amica/AMICA-Compilation-instructions.md index f2021b6..2477d17 100644 --- a/plugins/amica/AMICA-Compilation-instructions.md +++ b/plugins/amica/AMICA-Compilation-instructions.md @@ -1,7 +1,7 @@ --- layout: default -title: AMICA-Compilation-instructions -long_title: AMICA-Compilation-instructions +title: amica +long_title: amica parent: amica grand_parent: Plugins --- @@ -20,7 +20,7 @@ grand_parent: Plugins 5. The files impi.dll and libfabric.dll should be copied to executable folder when running outside OneAPI command window. Search OneAPI mpi directory for locations. -# THow to compile with Intel Fortran on Mac +# How to compile with Intel Fortran on Mac 0. These are old instructions. Try using Intel OneAPI modifiying the commands similar to the instructions for Windows above. 1. Install Intel Fortran compiler for Mac/Linux (free demo). diff --git a/plugins/amica/AMICA-introduction.md b/plugins/amica/AMICA-introduction.md index 112e139..0dc257d 100644 --- a/plugins/amica/AMICA-introduction.md +++ b/plugins/amica/AMICA-introduction.md @@ -1,7 +1,7 @@ --- layout: default -title: AMICA-Introduction -long_title: AMICA-Introduction +title: amica +long_title: amica parent: amica grand_parent: Plugins --- diff --git a/plugins/amica/Amica-Download.md b/plugins/amica/Amica-Download.md index ce1a016..9fa17eb 100644 --- a/plugins/amica/Amica-Download.md +++ b/plugins/amica/Amica-Download.md @@ -1,7 +1,7 @@ --- layout: default -title: AMICA-Downloa -long_title: AMICA-Downloa +title: amica +long_title: amica parent: amica grand_parent: Plugins --- diff --git a/plugins/amica/Amica-Mathematical-Explanation.md b/plugins/amica/Amica-Mathematical-Explanation.md index c5a3339..e3cd7e0 100644 --- a/plugins/amica/Amica-Mathematical-Explanation.md +++ b/plugins/amica/Amica-Mathematical-Explanation.md @@ -1,7 +1,7 @@ --- layout: default -title: AMICA-Mathematical-Explanation -long_title: AMICA-Mathematical-Explanation +title: amica +long_title: amica parent: amica grand_parent: Plugins --- diff --git a/plugins/amica/Entropy-and-Mutual-Information.md b/plugins/amica/Entropy-and-Mutual-Information.md index 01e0153..49d272d 100644 --- a/plugins/amica/Entropy-and-Mutual-Information.md +++ b/plugins/amica/Entropy-and-Mutual-Information.md @@ -1,7 +1,7 @@ --- layout: default -title: Entropy-and-Mutual-Information -long_title: Entropy-and-Mutual-Information +title: amica +long_title: amica parent: amica grand_parent: Plugins --- diff --git a/plugins/amica/Generalized-Gaussian-Probability-Density-Function.md b/plugins/amica/Generalized-Gaussian-Probability-Density-Function.md index 064e677..f23ffb1 100644 --- a/plugins/amica/Generalized-Gaussian-Probability-Density-Function.md +++ b/plugins/amica/Generalized-Gaussian-Probability-Density-Function.md @@ -1,7 +1,7 @@ --- layout: default -title: Generalized-Gaussian-Probability-Density-Function -long_title: Generalized-Gaussian-Probability-Density-Function +title: amica +long_title: amica parent: amica grand_parent: Plugins --- diff --git a/plugins/amica/Linear-Representations-and-Basis-Vectors.md b/plugins/amica/Linear-Representations-and-Basis-Vectors.md index eab4e7a..c5203e0 100644 --- a/plugins/amica/Linear-Representations-and-Basis-Vectors.md +++ b/plugins/amica/Linear-Representations-and-Basis-Vectors.md @@ -1,7 +1,7 @@ --- layout: default -title: Linear-Representations-and-Basis-Vectors -long_title: Linear-Representations-and-Basis-Vectors +title: amica +long_title: amica parent: amica grand_parent: Plugins --- diff --git a/plugins/amica/Random-Variables-and-Probability-Density-Functions.md b/plugins/amica/Random-Variables-and-Probability-Density-Functions.md index 5be1d64..f24e262 100644 --- a/plugins/amica/Random-Variables-and-Probability-Density-Functions.md +++ b/plugins/amica/Random-Variables-and-Probability-Density-Functions.md @@ -1,7 +1,7 @@ --- layout: default -title: Random-Variables-and-Probability-Density-Functions -long_title: Random-Variables-and-Probability-Density-Functions +title: amica +long_title: amica parent: amica grand_parent: Plugins --- diff --git a/plugins/amica/Supplementary-functions.md b/plugins/amica/Supplementary-functions.md index c517055..cd46dcc 100644 --- a/plugins/amica/Supplementary-functions.md +++ b/plugins/amica/Supplementary-functions.md @@ -1,7 +1,7 @@ --- layout: default -title: Supplementary-functions -long_title: Supplementary-functions +title: amica +long_title: amica parent: amica grand_parent: Plugins --- diff --git a/plugins/clean_rawdata/Documentation.md b/plugins/clean_rawdata/Documentation.md index 259bbbd..789728b 100644 --- a/plugins/clean_rawdata/Documentation.md +++ b/plugins/clean_rawdata/Documentation.md @@ -1,12 +1,12 @@ --- layout: default -title: Documentation -long_title: Documentation +title: clean_rawdata +long_title: clean_rawdata parent: clean_rawdata grand_parent: Plugins --- -Historical Background (01/07/2020 updated) ------------------------------------------- +Historical Background +--------------------- ASR was originally developed by [Christian Kothe](https://intheon.io/team/#christian-kothe), now Intheon CTO. It @@ -80,8 +80,8 @@ people need to guarantee the result replicatability (I do), I uploaded my final version here. Use 'availableRAM_GB' option as described below. [clean_rawdata1.10](files/Mmiyakoshi-clean-rawdata.zip) -Reference (07/09/2020 update) ------------------------------ +References +------------ - [Plechawska-Wojcik M, Kaczorowska M, Zapala D. (2019). The artifact subspace reconstruction (ASR) for EEG signal correction. A diff --git a/plugins/cleanline/index.md b/plugins/cleanline/index.md index a50471e..b1d5216 100644 --- a/plugins/cleanline/index.md +++ b/plugins/cleanline/index.md @@ -3,11 +3,12 @@ layout: default title: cleanline long_title: cleanline parent: Plugins -has_children: true nav_order: 5 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/cleanline). +![Screenshot 2024-08-02 at 09 14 11](https://github.com/user-attachments/assets/4e5be390-d091-4bdf-9ddc-e6fff8b692bb) + # CleanLine Welcome to the CleanLine plugin for EEGLAB! @@ -22,12 +23,6 @@ Toolbox (www.chronux.org). CleanLine also makes use of the arg() functionality from Christian Kothe's BCILAB toolbox (sccn.ucsd.edu/wiki/BCILAB) -# CleanLine versions -V1 - Original version by Tim Mullen - -V2 - Include a rewrite by Kay Robbins with integration by Arnaud Delorme and -testing by Makoto Miyakoshi - # Instalation Installation of CleanLine is simple: @@ -277,6 +272,11 @@ amplitude amps{i}) **g** Parameter structure. Function call can be replicated exactly by calling >> cleanline(EEG,g); +# CleanLine versions +V1 - Original version by Tim Mullen + +V2 - Include a rewrite by Kay Robbins with integration by Arnaud Delorme and +testing by Makoto Miyakoshi diff --git a/plugins/dipfit/index.md b/plugins/dipfit/index.md index 26ff8eb..dd90f21 100644 --- a/plugins/dipfit/index.md +++ b/plugins/dipfit/index.md @@ -1,9 +1,8 @@ --- layout: default -title: dipfit -long_title: dipfit +title: Dipfit +long_title: Dipfit parent: Plugins -has_children: true nav_order: 1 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/dipfit). diff --git a/plugins/eegstats/index.md b/plugins/eegstats/index.md index db0cc10..2071a42 100644 --- a/plugins/eegstats/index.md +++ b/plugins/eegstats/index.md @@ -3,7 +3,6 @@ layout: default title: eegstats long_title: eegstats parent: Plugins -has_children: true nav_order: 9 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/eegstats). diff --git a/plugins/fMRIb/index.md b/plugins/fMRIb/index.md index 77cca16..674b050 100644 --- a/plugins/fMRIb/index.md +++ b/plugins/fMRIb/index.md @@ -3,7 +3,6 @@ layout: default title: fMRIb long_title: fMRIb parent: Plugins -has_children: true nav_order: 11 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/fMRIb). @@ -62,19 +61,14 @@ Niazy RK, Beckmann CF, Iannetti GD, Brady JM, Smitha SM. Neuroimage. 2005, 28(3) # Version history -## Changes in V2.0 - -When the last gradient pulse sequence is too near the end of the EEG +* V2.0 -- When the last gradient pulse sequence is too near the end of the EEG recording, FASTR properly generates a warning but then crashes. 2) When a flat reference channel is in the dataset, FASTR's adaptive noise cancellation option tries to correct it, resulting in NaNs. 3) Pulse artifact subtraction function tries to correct flat reference channels resulting in crash. 4) Pulse artifact subtraction function trying to correct ECG channel despite being told not to do so. 5) FASTR's adaptive noise cancellation option tries to correct ECG channel -despite being told not to do so. - -See https://sccn.ucsd.edu/bugzilla/show_bug.cgi?id=1520 +despite being told not to do so. See https://sccn.ucsd.edu/bugzilla/show_bug.cgi?id=1520 -## Changes in V2.1 -Fix version display issue in EEGLAB, no other changes. +* V2.1 -- Fix version display issue in EEGLAB, no other changes. diff --git a/plugins/firfilt/index.md b/plugins/firfilt/index.md index 11b3c86..cdb8584 100644 --- a/plugins/firfilt/index.md +++ b/plugins/firfilt/index.md @@ -3,7 +3,6 @@ layout: default title: firfilt long_title: firfilt parent: Plugins -has_children: true nav_order: 23 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/firfilt). diff --git a/plugins/get_chanlocs/Documentation.md b/plugins/get_chanlocs/Documentation.md index 191ad87..7c09f40 100644 --- a/plugins/get_chanlocs/Documentation.md +++ b/plugins/get_chanlocs/Documentation.md @@ -1,7 +1,7 @@ --- layout: default -title: Documentation -long_title: Documentation +title: get_chanlocs +long_title: get_chanlocs parent: get_chanlocs grand_parent: Plugins --- diff --git a/plugins/groupSIFT/index.md b/plugins/groupSIFT/index.md index 52bedd4..5d7fa91 100644 --- a/plugins/groupSIFT/index.md +++ b/plugins/groupSIFT/index.md @@ -3,7 +3,6 @@ layout: default title: groupSIFT long_title: groupSIFT parent: Plugins -has_children: true nav_order: 24 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/groupSIFT). diff --git a/plugins/imat/index.md b/plugins/imat/index.md index 5d1f562..8eb1c76 100644 --- a/plugins/imat/index.md +++ b/plugins/imat/index.md @@ -3,7 +3,6 @@ layout: default title: imat long_title: imat parent: Plugins -has_children: true nav_order: 12 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/imat). diff --git a/plugins/limo/1.-Preprocessing.md b/plugins/limo/1.-Preprocessing.md deleted file mode 100644 index 373e2b2..0000000 --- a/plugins/limo/1.-Preprocessing.md +++ /dev/null @@ -1,120 +0,0 @@ ---- -layout: default -title: 1.-Preprocessing -long_title: 1.-Preprocessing -parent: LIMO -grand_parent: Plugins ---- -# Data for the tutorial - -This tutorial aims at teaching how to use [LIMO MEEG](https://github.com/LIMO-EEG-Toolbox/limo_tools/wiki), in conjunction with [EEGLAB STUDY](https://sccn.ucsd.edu/wiki/Chapter_02:_STUDY_Creation). To get started, you must be familiar with [EEGLAB](https://sccn.ucsd.edu/eeglab/index.php). Also, make sure LIMO tools subdirectories are listed in the Matlab path. - -The data used in this tutorial come from [Wakeman and Henson (2015)](https://www.nature.com/articles/sdata20151). In this experiment, MEG-EEG data were collected while subjects viewed famous, unfamiliar and scrambled faces. Each image was repeated twice (immediately in 50% of cases and 5–10 stimuli apart for the other 50%) and subjects pressed one of two keys with their left or right index finger indicating how symmetric they regarded each image relative to the average. - -The data were prepared (i.e. EEG extracted, timing corrected, electrode position re-oriented, event recorded) by Dung Truong, Ramon Martinez & Arnaud Delorme and can be downloaded from [OpenNeuro](https://openneuro.org/datasets/ds002718/versions/1.0.2). - -# Data pre-processing pipeline script - -The data are organized according to the [Brain Imaging Data Structure](https://bids.neuroimaging.io/), in particular the [EEG BIDS extension](https://www.nature.com/articles/s41597-019-0104-8). It is worthwhile spending a bit of time looking at how files are organized and named, as we will follow this convention throughout. EEGLAB also has dedicated [BIDS tools called bids-matlab-tools](https://github.com/sccn/bids-matlab-tools) to create files, export and import BIDS dataset. This is available using the EEGLAB plugin manager and must be installed before running the code below. - -Once you have downloaded the data, you can run the code below - can copy and paste to a file (changing Xs with your specific path) to get descent pre-processed data. Alternatively download the [code located here](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/code/henson_wakeman_preprocess.m). Be patient, this step will take several hours. - -```matlab -% start EEGLAB -clear variables -[ALLEEG, EEG, CURRENTSET, ALLCOM] = eeglab; - -% import BIDS -bidsfolder = 'F:\WakemanHenson_Faces\eeg'; -[STUDY, ALLEEG] = pop_importbids(bidsfolder, 'bidsevent','on','bidschanloc','on', ... - 'eventtype', 'trial_type', 'outputdir' ,fullfile(bidsfolder,'newderivatives'), 'studyName','Face_detection'); -ALLEEG = pop_select( ALLEEG, 'nochannel',{'061','062','063','064'}); -CURRENTSTUDY = 1; EEG = ALLEEG; CURRENTSET = 1:length(EEG); - -% reorient if using previous version of the data -EEG = pop_chanedit(EEG,'nosedir','+Y'); - -% Clean data - just the bad channels -EEG = pop_clean_rawdata( EEG,'FlatlineCriterion',5,'ChannelCriterion',0.8,... - 'LineNoiseCriterion',4,'Highpass',[0.25 0.75] ,... - 'BurstCriterion','off','WindowCriterion','off','BurstRejection','off',... - 'Distance','Euclidian','WindowCriterionTolerances','off' ); - -% Rereference using average reference -EEG = pop_reref( EEG,[],'interpchan',[]); - -% Run ICA and flag artifactual components using IClabel -for s=1:size(EEG,2) - EEG(s) = pop_runica(EEG(s), 'icatype','runica','concatcond','on','options',{'pca',EEG(s).nbchan-1}); - EEG(s) = pop_iclabel(EEG(s),'default'); - EEG(s) = pop_icflag(EEG(s),[NaN NaN;0.8 1;0.8 1;NaN NaN;NaN NaN;NaN NaN;NaN NaN]); - EEG(s) = pop_subcomp(EEG(s), find(EEG(s).reject.gcompreject), 0); -end - -% clear data using ASR - just the bad epochs -EEG = pop_clean_rawdata( EEG,'FlatlineCriterion','off','ChannelCriterion','off',... - 'LineNoiseCriterion','off','Highpass','off','BurstCriterion',20,... - 'WindowCriterion',0.25,'BurstRejection','on','Distance','Euclidian',... - 'WindowCriterionTolerances',[-Inf 7] ); - -% Extract data epochs (no baseline removed) -EEG = pop_epoch( EEG,{'famous_new','famous_second_early','famous_second_late','scrambled_new','scrambled_second_early','scrambled_second_late','unfamiliar_new','unfamiliar_second_early','unfamiliar_second_late'},... - [-0.5 1] ,'epochinfo','yes'); -EEG = eeg_checkset(EEG); -EEG = pop_saveset(EEG, 'savemode', 'resave'); -ALLEEG = EEG; - -% Create study design -STUDY = std_checkset(STUDY, ALLEEG); -STUDY = std_makedesign(STUDY, EEG, 1, 'name','STUDY.FaceRepetition','delfiles','off','defaultdesign','off','variable1','type','values1',{}); -eeglab redraw -``` - -# Loading the pre-processed EEGLAB STUDY - -After data preprocessing, data should be clean, epochs marked, and a STUDY created. Load the study from the EEGLAB menu (File --> Load existing study - [figure 1](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/1.jpg)) and check that all the data are there (Study --> edit study info - [figure 2](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/1.jpg)). In total there are 18 subjects, named sub-002 to sub-019. - -![Figure 1. Loading EEGLAB STUDY](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/1.jpg) -_Figure 1. Loading EEGLAB STUDY_ - -![Figure 2. Wakeman_Henson STUDY](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/2.jpg) -_Figure 2. Wakeman_Henson STUDY_ - -# Precompute STUDY measures - -LIMO MEEG models data using a hierarchical approach with a general linear model at the subject level and then testing, at the group level, parameters obtained with robust methods. You can think of it as generating averages per condition at the subject level and do statistics on those averages at the group level. The difference (and advantage) is that subject-specific baselines are removed, among trial variance accounted for, and bad subjects are accounted for. For more details see the [San Diego 2016 lecture as pdf](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/2016_SanDiego_StatisticalanalysisofEEGdata.pdf) and/or on [YouTube](https://youtu.be/KfnN51frbuI). - -## Step 1 – precompute single-trial measure(s) - -No matter the design, using LIMO MEEG means we need single trials to obtain condition related parameters for each subject. EEGLAB will export all trial data measures and, depending on the design, will pass on only relevant ones to LIMO EEG (even if this is only the mean as for comparing spectra between groups). - -Create ERPs and/or Spectra and/or ERSP (Study --> Precompute channel measures – [figure 3]((https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/3.jpg))). Note you could set limits to your epoch at this stage (using ‘timelim’ and ‘freqlim’) or limit the statistical analysis at the next stage (which is what we will do). For ERPs, baseline correction can be added using `[-200 0]`. Note that ERSP can take a long time. You can start with ERP and Spectra only (of course if you do that, you will not be able to use LIMO on ERSP in the next sections). - -![Figure 3. Precompute channel measures](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/3.jpg) -_Figure 3. Precompute channel measures_ - -All single trials (erp, spectrum, ersp) can also be generated in command line using: - -```matlab -% Precompute ERP and Spectrum measures -[STUDY, EEG] = std_precomp(STUDY, EEG, {}, 'savetrials','on','interp','on','recompute','on',... - 'erp','on','erpparams', {'rmbase' [-200 0]}, 'spec','on',... - 'ersp','on','itc','on', 'specparams',{'specmode','fft','logtrials','off'}); -``` - -## Step 2 – create your design - -From a set of available conditions and trial information, there are many options available. In this tutorial, we’ll review the most common ones. For example, we can select the independent variable "face_type" that takes 3 categorical values (famous, scambled and unfamiliar faces). We will start by using this simple design and create more complex ones as we go along the tutorial (Study --> Select/Edit study designs(s) – [figure 4](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/4.jpg)). - -![Figure 4. Select/Edit study design(s)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/4.jpg) -_Figure 4. Select/Edit study design(s)_ - -Every design can be generated from here, and the following sections will show you each time a different design. For now, let’s have a look at the ‘List of factors’ ([figure 5](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/5.jpg)). This lists all categorical variables from which we can make a design (this list appears if you select "list factors" for the independent variable). This list of factors is the list LIMO will use at the first level. In the next section, we will do a 1-way ANOVA testing the effect of familiarity (all famous vs. all unfamiliar vs. all scrambled faces). - -![Figure 5. List of categorical variables](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/5.jpg) -_Figure 5. List of categorical variables_ - -# Note on using LIMO default and sampling rate - -By default, LIMO EEG uses a Weighted least Squares approach for each trial, which means you should aim to have more trials than time frames (for ERP and ERSP) or frequency frames (for Spectrum), while not mandatory. The data here are at 250Hz which is fine. - \ No newline at end of file diff --git a/plugins/limo/10.-Two-sample-t-tests.md b/plugins/limo/10.-Two-sample-t-tests.md deleted file mode 100644 index af3eb52..0000000 --- a/plugins/limo/10.-Two-sample-t-tests.md +++ /dev/null @@ -1,27 +0,0 @@ ---- -layout: default -title: 10.-Two-sample-t-tests -long_title: 10.-Two-sample-t-tests -parent: LIMO -grand_parent: Plugins ---- -Given the (non-significant) group effect observed in the between subjects’ ANOVA with repeated factor, we can also do a simple two samples t-tests between groups 1 and 2. From the 2nd level GUI ([figure 9](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/9.jpg)), click on two samples t-test, and select the beta files for group 1 and group 2. Let’s select parameter 1, i.e. famous faces ([figure 38](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/38.jpg)). Parameter question dialogue is repeated because you could for instance compare groups from 2 different studies, i.e. with different 1st level designs. - -![Figure 38. Regression](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/38.jpg) -_Figure 38. Selection of files for 2 samples t-test._ - -These steps can be executed in command line as: -```matlab -chanlocs = XXX\derivatives\limo_gp_level_chanlocs.mat'; -Files{1} = ‘XXX\LIMO_Face_detection\Beta_files_Gp1_Faces_GLM_Channels_Frequency_WLS.txt'; -Files{2} = 'XXX\LIMO_Face_detection\Beta_files_Gp2_Faces_GLM_Channels_Frequency_WLS.txt'; -LIMOPath = limo_random_select('two-samples t-test',chanlocs,'LIMOfiles',Files,... - 'analysis_type','Full scalp analysis', 'type','Channels','parameter',[1;1],'nboot',1000,'tfce',0); -``` - -You should obtain results display on figure 39 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/39a.jpg), [Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/39b.jpg) and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/39c.jpg). - -![Figure 39. Regression ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/39a.jpg) -![Figure 39. Regression Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/39b.jpg) -![Figure 39. Regression ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/39c.jpg) -_Figure 39. Results of the 2 samples t-test young vs old for famous faces._ diff --git a/plugins/limo/11.-Regression-among-subjects.md b/plugins/limo/11.-Regression-among-subjects.md deleted file mode 100644 index aefbdf7..0000000 --- a/plugins/limo/11.-Regression-among-subjects.md +++ /dev/null @@ -1,41 +0,0 @@ ---- -layout: default -title: 11.-Regression-among-subjects -long_title: 11.-Regression-among-subjects -parent: LIMO -grand_parent: Plugins ---- -In the [between subjects’ ANOVA with repeated factor](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/Between-subjects%E2%80%99-ANOVAs-with-repeated-factors), we artificially split subjects into young and old subjects. Such post-hoc splitting is not recommended and typically create spurious results. Instead, we could test how much age influences face perception. For this, we will use the contrast faces vs scrambled computed previously in the [one-sample t-test](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/5.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level)). - -You can either create the age variable from the participants.tsv file, or we can load values from STUDY. Don’t worry about the missing values, LIMO will simply remove those subjects for which there are no values. - -In command window type: -```matlab -age_regressor = cell2mat(arrayfun(@(x) x.age,STUDY.datasetinfo,'UniformOutput',false))'; -save('age_regressor.mat','age_regressor') -``` - -From the second level GUI ([figure 9](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/9.jpg)), select regression, full scalp analysis and pick-up the contrast faces>scrambled computed for one-sample t-test (should be con4), finally select the age_regressor.mat we just created ([figure 40](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/40.jpg)). WARNING: it is suggested to set the bootstrap to 0 as this is very long to compute since we use here an Iterative Reweighted Least Squares (IRLS) solution that weights subjects for each electrode and time frame. - -![Figure 40. Regression](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/40.jpg) -_Figure 40. Create a 2nd level regression analysis with age._ - -These steps can be executed in command line as: -```matlab -chanlocs = XXX\derivatives\limo_gp_level_chanlocs.mat'; -LIMOPath = limo_random_select('regression',chanlocs,'LIMOfiles',... - 'XXX\LIMO_Face_detection\con4_files_FaceRepAll_GLM_Channels_Time_WLS.txt', ... - 'analysis type','Full scalp analysis', type','Channels','nboot',0,'tfce',0,'regressor',... - 'XXX\LIMO_Face_detection\2nd_level\regression\age_regressor.mat',... - 'zscore','Yes','skip design check','Yes') -``` - -Results can be appreciated using ‘image all’ and ‘course plot’. Here, because we have a continuous variable we can visualize how subjects data vary along with the variable of interest (figure 41 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/41a.jpg), [Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/41b.jpg), [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/41c.jpg)). You can either look at the raw data, modelled (best option here) or adjusted. - -![Figure 41. Regression ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/41a.jpg) -![Figure 41. Regression Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/41b.jpg) -![Figure 41. Regression ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/41c.jpg) -_Figure 41. Results of the regression analysis of age on the contrast faces>scrambled._ - - - diff --git a/plugins/limo/12.-Regression-at-the-trial-level.md b/plugins/limo/12.-Regression-at-the-trial-level.md deleted file mode 100644 index fec5adb..0000000 --- a/plugins/limo/12.-Regression-at-the-trial-level.md +++ /dev/null @@ -1,55 +0,0 @@ ---- -layout: default -title: 12.-Regression-at-the-trial-level -long_title: 12.-Regression-at-the-trial-level -parent: LIMO -grand_parent: Plugins ---- -In previous analyses, the repetition levels were either averaged or used as a categorical variable. Here, we instead used the time between each repetition of the same stimulus – thus for a given subject we have 3 conditions (familiar faces, unfamiliar faces and scrambled faces) and one continuous variable (the distance between the repeat of a stimulus type). - -# 1st level - -For this design, we consider 3 conditions: familiar faces, scrambled faces, and unfamiliar faces; along with one continuous variable: the distance between repetitions (in trials). From STUDY, create a new design and let’s call it ‘Repetition’ and then click ‘new’ to add conditions ([figure 42](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/42.jpg)). - -![Figure 42. Regression design](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/42.jpg) -_Figure 42. Building a STUDY with a continuous variable._ - -Given that we are interested in repetition, why adding the face type? By modelling repetition as a single variable, we expect trials to covary the same whatever the type of stimulus. Since we know faces activates more than scrambled faces, the slope of the regression will be affected by this difference – and so this is important to also include the conditions. One can also split the time regressor with face type, which will split the time regressor into three continuous regressors and we can recombine them using a contrast. This is available when estimating limo parameters (figure 43 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/43a.jpg), [Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/43b.jpg), and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/43c.jpg)). - -![Figure 43. Estimate Regression design](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/43a.jpg) -![Figure 43. Estimate Regression design](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/43b.jpg) -![Figure 43. Estimate Regression design](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/43c.jpg) -_Figure 43. Estimate parameters - splitting continuous variable by the categorical one._ - -After estimating the models, you can check the design matrix using limo --> results --> review design ([figure 44](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/44.jpg)), showing the time between trials split for each condition. - -![Figure 44. Design martrix](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/44.jpg) -_Figure 44. Design matrix for one subject._ - -You can create and estimate this design with these command lines - -```matlab -STUDY = std_makedesign(STUDY, ALLEEG, 2, 'name','Face_time','delfiles','off','defaultdesign','off',... - 'variable1','face_type','values1',{'famous','scrambled','unfamiliar'},'vartype1','categorical',... - 'variable2','time_dist','values2',[],'vartype2','continuous',... - 'subjselect',{'sub-002','sub-003','sub-004','sub-005','sub-006','sub-007','sub-008','sub-009','sub-010','sub-011','sub-012','sub-013','sub-014','sub-015','sub-016','sub-017','sub-018','sub-019'}); -[STUDY, EEG] = pop_savestudy( STUDY, EEG, 'savemode','resave'); -STUDY = pop_limo(STUDY, ALLEEG, 'method','WLS','measure','daterp','timelim',[-50 650],'erase','on','splitreg','on','interaction','off'); -``` - - -# 2nd level - -To test the effect of time (i.e. repetition), we are using a one-way repeated measure ANOVA. This will test if there is a difference based on the stimulus type in the way repetition influence linearly EEG activity. For that from the 2nd level GUI ([figure 9](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/9.jpg)), select a new directory, load channel location file, click on ANOVA, choose repeated measures, full scalp, enter model parameters: 1 for group and 3 to indicate one factor of three levels and select the beta parameters list for the repetition design – using parameters 4 5 6 [see one-way ANOVA tutorial for details](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/3.--One-way-repeated-measures-ANOVA-revised-(Famous,-Unfamiliar,-Scrambled-faces-as-1st-level-contrasts)). Next, go the limo results and create a new contrast, we test here if this effect differs for familiar faces using a contrast [2 -1 -1] ([figure 45](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/45.jpg)). - -![Figure 45. One-way ANOVA](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/45.jpg) -_Figure 45. One-way ANOVA on repetition regression parameters._ - -Results of the ANOVA show when/where repetition effects differ between condition (the regression of the trial distance on trials for famous faces vs scrambled faces vs unfamiliar faces), and results from the contrast that this is driven by a repetition effect for famous faces. - -![Figure 46. One-way ANOVA](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/46a.jpg) -![Figure 46. One-way ANOVA](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/46b.jpg) -![Figure 46. One-way ANOVA](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/46c.jpg) -_Figure 46. One-way ANOVA and contrasts results for repetition regression parameters._ - - diff --git a/plugins/limo/2.-One-way-repeated-measures-ANOVA-(Famous,-Unfamiliar,-Scrambled-faces-as-conditions).md b/plugins/limo/2.-One-way-repeated-measures-ANOVA-(Famous,-Unfamiliar,-Scrambled-faces-as-conditions).md deleted file mode 100644 index c8746f9..0000000 --- a/plugins/limo/2.-One-way-repeated-measures-ANOVA-(Famous,-Unfamiliar,-Scrambled-faces-as-conditions).md +++ /dev/null @@ -1,166 +0,0 @@ ---- -layout: default -title: 2.-One-way-repeated-measures-ANOVA-(Famous,-Unfamiliar,-Scrambled-faces-as-conditions) -long_title: 2.-One-way-repeated-measures-ANOVA-(Famous,-Unfamiliar,-Scrambled-faces-as-conditions) -parent: LIMO -grand_parent: Plugins ---- -Here we will use the three basic conditions to run a group level ANOVA. LIMO runs a [hierarchical model](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/2016_SanDiego_StatisticalanalysisofEEGdata.pdf), first a GLM at the subject level (first level), second a GLM at the group level (second level). Under some assumptions about the data, this is equivalent to running mixed model analysis on all trials for all subjects with subjects as random effects -- but much faster to calculate. - -# First level analysis -We first create the design – Rename design 1 as ANOVA_Faces and then Delete current variables ([figure 6](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/6.jpg)). Click ‘New’ to add variables of interest – here select face type ([figure 7](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/7.jpg)). Press OK to add those variables, and OK again to close the design selection. You are ready to create the design in LIMO MEEG and compute the model parameters (figure 8 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/8a.jpg), [spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/8b.jpg) or [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/8c.jpg)). Input the data type (ERP, Spectrum or ERSP) and possibly restrict the time or frequency range. The default method (Weighted Least Squares) is the preferred options, as long as you have more trials than data frames. - -![Figure 6. Edit design](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/6.jpg) -_Figure 6. Edit design_ - -![Figure 7. Create new design](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/7.jpg) -_Figure 7. Create new design_ - -This can be executed in command line as: -``` matlab -% 1st level analysis - specify the design -% We ignore the repetition levels using the variable 'face_type' -STUDY = std_makedesign(STUDY, ALLEEG, 1, 'name','ANOVA_Faces','delfiles','off','defaultdesign','off',... - 'variable1','face_type','values1',{'famous','scrambled','unfamiliar'},'vartype1','categorical',... - 'subjselect',{'sub-002','sub-003','sub-004','sub-005','sub-006','sub-007','sub-008','sub-009',... - 'sub-010','sub-011','sub-012','sub-013','sub-014','sub-015','sub-016','sub-017','sub-018','sub-019'}); -[STUDY, EEG] = pop_savestudy( STUDY, EEG, 'savemode','resave'); -``` - -![Figure 8a. LIMO Estimate model parameters for ERP using Weighted Least Squares (WLS)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/8a.jpg) -``` matlab -STUDY = pop_limo(STUDY, ALLEEG, 'method','WLS','measure','daterp','timelim',[-50 650],... - 'erase','on','splitreg','off','interaction','off'); -``` - -![Figure 8b. LIMO Estimate model parameters for Spectrum using Weighted Least Squares (WLS)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/8b.jpg) -``` matlab -STUDY = pop_limo(STUDY, ALLEEG, 'method','WLS','measure','datspec','freqlim',[3 45],... - 'erase','on','splitreg','off','interaction','off'); -``` - -![Figure 8c. LIMO Estimate model parameters for ERSP using Weighted Least Squares (WLS)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/8c.jpg) -``` matlab -STUDY = pop_limo(STUDY, ALLEEG, 'method','WLS','measure','dattimef',... - 'timelim',[-50 650],'freqlim',[3 45],'erase','on','splitreg','off','interaction','off'); -``` -_Figure 8. Estimate model parameters_ - -After a few minutes, all subjects are processed: design is build and parameter estimated. All analyses appear within each subject derivatives folder – inside a folder with the name of the design, the measure analysed, space, and the method used, for instance -`/derivatives/sub-002/eeg/ ANOVA_Faces_GLM_Channels_Time_WLS` - -Inside are the extracted data (Yr.mat), the modelled data (Yhat.mat), model parameters (Betas.mat), model residuals (Res.mat), model fit (R2.mat), statistical effect (condition_effect_1.mat) and model information itself (LIMO.mat). - -There are also a few other useful files: -- LIMO_face_detection is a folder with the txt files listing all the files used and generated, these are useful for group level analyses -- LIMO_face_detection/limo_batch_report is a folder that contains the psom batch files and list which files failed in any – also contains the psom pipeline. - -The above analyses can be executed in command line as: - -# 2nd level analysis - -For each subject, there are 4 model parameters (Betas): familiar, scrambled, unfamiliar (stored in the order it appears when you make the design) and the last parameter is the subject-specific constant. Doing a 1-way ANOVA consists simply in entering these beta values (files) and setting this as a single factor with 3 levels. - -Click on Study --> Linear MOdeling of EEG Data --> 2nd level analysis ([figure 9](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/9.jpg)). - -![Figure 9. Calling LIMO 2nd level](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/9.jpg) -_Figure 9. Calling LIMO 2nd level_ - -1. *load the group level channel location file* – this should be located at the root of the derivatives folder ([figure 10](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/10.jpg)) -2. make a new directory (‘1way_ANOVA’) to save this new analysis and select this as a *working directory* ([figure 11](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/11.jpg)) -3. click on *ANOVA/ANCOVA* ([figure 12](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/12.jpg)) and fill the information as needed: Full scalp analysis --> Repeated measure ANOVA --> 1 group --> 1 factor of 3 levels -4. select *beta files*, enter which beta to analyse [1 2 3], and name that factor ‘faces’ ([figure 13](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/13.jpg)) - -![Figure 10. load group level channel location file](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/10.jpg) -_Figure 10. load group level channel location file_ - -![Figure 11. Select working directory](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/11.jpg) -_Figure 11. Select working directory_ - -![Figure 12. Setting up a 1 Way repeated measure ANOVA](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/12.jpg) -_Figure 12. Setting up a 1 Way repeated measure ANOVA_ - -![Figure 13. Select beta files (here shown for ERP, i.e. Channels Time WLS) and parameters](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/13.jpg) -_Figure 13. Select beta files (here shown for ERP, i.e. Channels Time WLS) and parameters_ - -The design matrix should then pop up ([figure 14](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/14.jpg)), and answer ‘yes’ to start the analysis. - -![Figure 14. 1-way ANOVA Design matrix (1 column per condition + constant term)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/14.jpg) -_Figure 14. 1-way ANOVA Design matrix (1 column per condition + constant term)_ - -These steps can be executed in command line as: -```matlab -% 2nd level analysis - ANOVA on Beta parameters 1 2 3 -chanlocs = [STUDY.filepath filesep 'limo_gp_level_chanlocs.mat']; -mkdir([STUDY.filepath filesep '1-way-ANOVA']) -cd([STUDY.filepath filesep '1-way-ANOVA']) -% ERP -limo_random_select('Repeated Measures ANOVA',chanlocs,'LIMOfiles',... - {[STUDY.filepath filesep 'LIMO_Face_detection' filesep 'Beta_files_ANOVA_Faces_GLM_Channels_Time_WLS.txt']},... - 'analysis_type','Full scalp analysis','parameters',{[1 2 3]},... - 'factor names',{'face'},'type','Channels','nboot',1000,'tfce',0,'skip design check','yes'); -% Spectrum -limo_random_select('Repeated Measures ANOVA',chanlocs,'LIMOfiles',... - {[STUDY.filepath filesep 'LIMO_Face_detection' filesep 'Beta_files_ANOVA_Faces_GLM_Channels_Frequency_WLS.txt']},... - 'analysis_type','Full scalp analysis','parameters',{[1 2 3]},... - 'factor names',{'face'},'type','Channels','nboot',1000,'tfce',0,'skip design check','yes'); -% ERSP -limo_random_select('Repeated Measures ANOVA',chanlocs,'LIMOfiles',... - {[STUDY.filepath filesep 'LIMO_Face_detection' filesep 'Beta_files_ANOVA_Faces_GLM_Channels_Time-Frequency_WLS.txt']},... - 'analysis_type','Full scalp analysis','parameters',{[1 2 3]},... - 'factor names',{'face'},'type','Channels','nboot',1000,'tfce',0,'skip design check','yes'); -``` - -Exit the 2nd level GUI and call LIMO results by pressing the "Plot" button) which will ask what to plot and will automatically propose the ANOVA result. - -![Plot button -- calls to plot results](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/pre15.jpg) - -By default, the bootstrap option is 'off' and thus the ANOVA should compute very quickly. In the 1-way ANOVA folders, several files have been created: the data (Yr.mat), the statistical effects (Rep_ANOVA_Main_effect_1_faces.mat) and model information itself (LIMO.mat). - -Choose the multiple comparison method (e.g. clustering) and this will then compute the bootstrap (does that only once). After a while the figure is updated. A new H0 folder is there, with the resampling table (boot_table.mat), the null data (centered_data.mat), and the bootstrapped statistical results under H0. Results for the different clusters are also reported in the command window. - -![Figure 15. 1-way ANOVA ERP Results for famous faces vs scrambled vs unfamiliar](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/15a.jpg) -![Figure 15. 1-way ANOVA Spectrum Results for famous faces vs scrambled vs unfamiliar](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/15b.jpg) -![Figure 15. 1-way ANOVA ERSP Results for famous faces vs scrambled vs unfamiliar](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/15c.jpg) -_Figure 15. 1-way ANOVA Results for famous faces vs scrambled vs unfamiliar_ - -## Contrasts - -At this stage, the ANOVA results tell us where and when those three conditions differ. To check which condition differs from the other, post-hoc contrasts can be performed testing between pairs of conditions: familiar faces vs. scrambled faces [1 -1 0], unfamiliar faces vs scrambled faces [0 -1 1] and familiar faces vs unfamiliar faces [1 0 -1]. More complex contrasts are also possible, let’s try *faces vs scrambled [0.5 -1 0.5]* which compare images containing faces with images not containing faces. This is the same type of contrast/post-hoc analysis that you would run in your statistical software. - -From the main LIMO MEEG GUI or the result GUI, click on contrast manager and select the "LIMO.mat" file created for the ANOVA. The contrast manager shows the design matrix and we can enter the contrast to compute ([figure 16](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/16.jpg)) and click ‘Done’. The contrast and associated bootstrap are then computed. Note that these analyses differ from performing ad-hoc t-tests (see below), mostly because (i) they are computed within the repeated ANOVA model, i.e. accounting for all conditions and (ii) they are not directional relying on a F statistic (ie saved as ess* files). - -![Figure 16. Contrast manager ](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/16.jpg) -_Figure 16. Contrast manager showing the design matrix (in white: familiar faces, scrambled faces, unfamiliar faces, constant) and the contrast familiar faces > scrambled faces._ - -This step can be executed in command line as: - -```matlab -% add contrast famous+unfamiliar>scrambled -limo_contrast(fullfile(pwd,'Yr.mat'),fullfile(pwd,'LIMO.mat'), 3 ,[0.5 -1 0.5]); % compute a new contrast -limo_contrast(fullfile(pwd,'Yr.mat'),fullfile(pwd,'LIMO.mat'), 4); % do the bootstrap of the last contrast -``` - -Results can then be viewed again by selecting the newly created ‘ess*.mat’ file using ‘image all’ and using the ‘course plot’ which shows the time course of the difference (figure 17 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/17a.jpg), [Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/17b.jpg) and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/17c.jpg)). - -![Figure 17a. Contrast ERP results ](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/17a.jpg) -```matlab -limo_eeg(5,pwd) % channel*time imagesc for both effects and contrast -limo_display_results(3,'ess_1.mat',pwd,0.05,2,fullfile(pwd,'LIMO.mat'),0,'channels',49); % course plot -saveas(gcf, 'contrast_timecourse.fig'); close(gcf) -``` -![Figure 17b. Contrast Specrum results ](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/17b.jpg) -```matlab -limo_eeg(5,pwd) % channel*time imagesc for both effects and contrast -limo_display_results(3,'ess_1.mat',pwd,0.05,2,fullfile(pwd,'LIMO.mat'),0,'channels',49); % course plot -saveas(gcf, 'contrast_specprofile.fig'); close(gcf) -``` - -![Figure 17c. Contrast ERSP results ](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/17c.jpg) -```matlab -limo_eeg(5,pwd) % channel*time imagesc for both effects and contrast -limo_display_results(3,'ess_1.mat',pwd,0.05,2,fullfile(pwd,'LIMO.mat'),0,'channels',49); % course plot -saveas(gcf, 'contrast_timecourse.fig'); close(gcf) -``` -_Figure 17. Results for the contrast [0.5 -1 0.5 0] i.e. (faces - scrambled)≠0_ - diff --git a/plugins/limo/2.-Within-Subject-Categorical-Designs.md b/plugins/limo/2.-Within-Subject-Categorical-Designs.md deleted file mode 100644 index 2f2069f..0000000 --- a/plugins/limo/2.-Within-Subject-Categorical-Designs.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -layout: default -title: 2.-Within-Subject-Categorical-Designs -long_title: 2.-Within-Subject-Categorical-Designs -parent: LIMO -grand_parent: Plugins ---- -- [1 way repeated measures ANOVA (Famous, Unfamiliar, Scrambled faces as conditions)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/2.-One-way-repeated-measures-ANOVA-(Famous,-Unfamiliar,-Scrambled-faces-as-conditions))) -- [One way repeated measures ANOVA revised (Famous, Unfamiliar, Scrambled faces as 1st level contrasts)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/3.--One-way-repeated-measures-ANOVA-revised-(Famous,-Unfamiliar,-Scrambled-faces-as-1st-level-contrasts)) -- [Summary statistics to measure and report effects and effect sizes](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/4.-Summary-statistics:-Effects-and-Effect-sizes) -- [One sample t-test (contrasting Full Faces vs Scrambled Faces at the subject level)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/5.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level)) -- [Summary statistics of differences](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/6.-Summary-statistics-of-differences) -- [Two-ways ANOVA (Faces x Repetition)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/7.-Two-ways-ANOVA-(Faces-x-Repetition)) -- [Paired t-test (Famous vs Unfamiliar)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/8.-Paired-t-test-(Famous-vs-Unfamiliar)) \ No newline at end of file diff --git a/plugins/limo/3.--One-way-repeated-measures-ANOVA-revised-(Famous,-Unfamiliar,-Scrambled-faces-as-1st-level-contrasts).md b/plugins/limo/3.--One-way-repeated-measures-ANOVA-revised-(Famous,-Unfamiliar,-Scrambled-faces-as-1st-level-contrasts).md deleted file mode 100644 index 3727f53..0000000 --- a/plugins/limo/3.--One-way-repeated-measures-ANOVA-revised-(Famous,-Unfamiliar,-Scrambled-faces-as-1st-level-contrasts).md +++ /dev/null @@ -1,117 +0,0 @@ ---- -layout: default -title: 3.--One-way-repeated-measures-ANOVA-revised-(Famous,-Unfamiliar,-Scrambled-faces-as-1st-level-contrasts) -long_title: 3.--One-way-repeated-measures-ANOVA-revised-(Famous,-Unfamiliar,-Scrambled-faces-as-1st-level-contrasts) -parent: LIMO -grand_parent: Plugins ---- -In the [previous analysis](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/2.-One-way-repeated-measures-ANOVA-(Famous,-Unfamiliar,-Scrambled-faces-as-conditions)), at the 1st level, we selected ‘face_type’ ([figure 7](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/7.jpg)) as our variable. By doing so, beta parameters reflect the average height of each face type. We know that there is also a repetition effect – and if one repetition differs a lot more than the others that average can be biased. **It is therefore recommended to always create a full design (all known effects) and pool conditions to create contrasts**. - -# 1st level - -For this experiment, we have 9 conditions: familiar faces 1st time, familiar faces 2nd time, familiar faces 3rd time, scrambled faces 1st time, scrambled faces 2nd time, scrambled faces 3rd time, unfamiliar faces 1st time, unfamiliar faces 2nd time, unfamiliar faces 3rd time. We here pool the repetition levels to create only 3 conditions: familiar, scrambled, unfamiliar. The design will be with 6 conditions, but 3 contrasts will also be created – those contrasts are the averages of repetition levels beta parameters (so we expect very similar results, but not identical). - -From STUDY, create a new design and let’s call it ‘FaceRepAll’ and then click ‘new’ to add conditions [figure 18](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/18.jpg). - -![Figure 18. New design pooling conditions](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/18.jpg) -_Figure 18. New design pooling conditions_ - -The variable of interest is ‘trial_type’ which contains the 9 experimental conditions. Instead of just selecting those conditions, here let’s combine the repetition levels. Select ‘famous new’, ‘famous second early’ and ‘famous second late’ and click combine selected values ([figure 19](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/19.jpg)). Repeat for scrambled and unfamiliar faces. This creates 3 new values ([figure 20](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/20.jpg)) which you now select for your design. - -![Figure 19. New design using trial type](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/19.jpg) -_Figure 19. New design using trial type_ - -![Figure 20. Use combined values of trial type](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/20.jpg) -_Figure 20. Use combined values of trial type_ - -Now estimate model parameters. Input the data type (figure 8 [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/8a.jpg), [Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/8b.jpg), [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/8c.jpg)) and possibly restrict the time or frequency range. The default method (Weighted Least Squares) is the preferred options, as long as you have more trials than data frames. In addition to the beta parameters, we now have 3 *con*.mat* files corresponding to the pooled repetition levels. - -``` matlab -% 1st level analysis - specify the design -% Note we use the variable 'type' and use cells within a cell array to -% indicate grouping which means contrasts will be computed pooling those levels -STUDY = std_makedesign(STUDY, ALLEEG, 2, 'name','FaceRepAll','delfiles','off','defaultdesign','off',... - 'variable1','type','values1',{{'famous_new','famous_second_early','famous_second_late'},... - {'scrambled_new','scrambled_second_early','scrambled_second_late'},... - {'unfamiliar_new','unfamiliar_second_early','unfamiliar_second_late'}},'vartype1','categorical',... - 'subjselect',{'sub-002','sub-003','sub-004','sub-005','sub-006','sub-007','sub-008','sub-009',... - 'sub-010','sub-011','sub-012','sub-013','sub-014','sub-015','sub-016','sub-017','sub-018','sub-019'}); -[STUDY, EEG] = pop_savestudy( STUDY, EEG, 'savemode','resave'); - -% 1st level analysis - estimate parameters -% ERP -[STUDY] = pop_limo(STUDY, ALLEEG, 'method','WLS','measure','daterp','timelim',[-50 650], ... - 'erase','on','splitreg','off','interaction','off'); -% Spectrum -[STUDY] = pop_limo(STUDY, ALLEEG, 'method','WLS','measure','daterp','freqlim',[3 45], ... - 'erase','on','splitreg','off','interaction','off'); -% ERSP -[STUDY] = pop_limo(STUDY, ALLEEG, 'method','WLS','measure','daterp','timelim',[-50 650],'freqlim',[3 45], ... - 'erase','on','splitreg','off','interaction','off'); -``` - -# 2nd level - -Click on Study --> Linear MOdeling of EEG Data --> 2nd level analysis ([figure 9](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/9.jpg)). - -1. load the group level channel location file – this should be located at the root of the derivatives folder ([figure 10](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/10.jpg)) -2. make a new directory (‘1way_ANOVA_revised’) to save this new analysis and select this as a working directory ([figure 11](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/11.jpg)) -3. click on ANOVA/ANCOVA ([figure 12](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/12.jpg)) and fill the information as needed: Full scalp analysis --> Repeated measure ANOVA --> 1 group --> 1 factor of 3 levels -4. select con files, iteratively for each level ([figure 21](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/21.jpg)), and name that factor ‘faces’ - -![Figure 21. Select con files for the ANOVA](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/21.jpg) -_Figure 21. Select con files (here shown for ERP, i.e. Channels Time WLS) for the ANOVA_ - -The design matrix should then pop up and answer ‘yes’ to start the analysis. Then review the results ([figure 22](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/22.jpg)). While most effects are the same, results differ from [figure 15](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/15.jpg). - -![Figure 22a. 1-way ANOVA results ](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/22a.jpg) -``` matlab -chanlocs = [STUDY.filepath filesep 'limo_gp_level_chanlocs.mat']; -con1_files = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'con_1_files_FaceRepAll_GLM_Channels_Time_WLS.txt'); -con2_files = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'con_2_files_FaceRepAll_GLM_Channels_Time_WLS.txt'); -con3_files = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'con_3_files_FaceRepAll_GLM_Channels_Time_WLS.txt'); - -mkdir([STUDY.filepath filesep '1-way-ANOVA-revised']) -cd([STUDY.filepath filesep '1-way-ANOVA-revised']) -limo_random_select('Repeated Measures ANOVA',chanlocs,'LIMOfiles', {con1_files,con2_files,con3_files},... - 'analysis_type','Full scalp analysis','parameters',{[1 1 1]},... - 'factor names',{'face'},'type','Channels','nboot',1000,'tfce',0,'skip design check','yes'); - -limo_eeg(5,pwd) % channel*time imagesc -``` -![Figure 22b. 1-way ANOVA results ](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/22b.jpg) -``` matlab -chanlocs = [STUDY.filepath filesep 'limo_gp_level_chanlocs.mat']; -con1_files = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'con_1_files_FaceRepAll_GLM_Channels_Frequency_WLS.txt'); -con2_files = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'con_2_files_FaceRepAll_GLM_Channels_Frequency_WLS.txt'); -con3_files = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'con_3_files_FaceRepAll_GLM_Channels_Frequency_WLS.txt'); - -mkdir([STUDY.filepath filesep '1-way-ANOVA-revised']) -cd([STUDY.filepath filesep '1-way-ANOVA-revised']) -limo_random_select('Repeated Measures ANOVA',chanlocs,'LIMOfiles', {con1_files,con2_files,con3_files},... - 'analysis_type','Full scalp analysis','parameters',{[1 1 1]},... - 'factor names',{'face'},'type','Channels','nboot',1000,'tfce',0,'skip design check','yes'); - -limo_eeg(5,pwd) % channel*freq imagesc -``` -![Figure 22c. 1-way ANOVA results ](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/22c.jpg) -``` matlab -chanlocs = [STUDY.filepath filesep 'limo_gp_level_chanlocs.mat']; -con1_files = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'con_1_files_FaceRepAll_GLM_Channels_Time-Frequency_WLS.txt'); -con2_files = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'con_2_files_FaceRepAll_GLM_Channels_Time-Frequency_WLS.txt'); -con3_files = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'con_3_files_FaceRepAll_GLM_Channels_Time-Frequency_WLS.txt'); - -mkdir([STUDY.filepath filesep '1-way-ANOVA-revised']) -cd([STUDY.filepath filesep '1-way-ANOVA-revised']) -limo_random_select('Repeated Measures ANOVA',chanlocs,'LIMOfiles', {con1_files,con2_files,con3_files},... - 'analysis_type','Full scalp analysis','parameters',{[1 1 1]},... - 'factor names',{'face'},'type','Channels','nboot',1000,'tfce',0,'skip design check','yes'); - -limo_eeg(5,pwd) % channel*time*freq 'imagesc' like -``` -_Figure 22. 1-way ANOVA results based on 1st level contrasts_ - - - - - diff --git a/plugins/limo/4.-Summary-statistics:-Effects-and-Effect-sizes.md b/plugins/limo/4.-Summary-statistics:-Effects-and-Effect-sizes.md deleted file mode 100644 index 399f4a5..0000000 --- a/plugins/limo/4.-Summary-statistics:-Effects-and-Effect-sizes.md +++ /dev/null @@ -1,126 +0,0 @@ ---- -layout: default -title: 4.-Summary-statistics:-Effects-and-Effect-sizes -long_title: 4.-Summary-statistics:-Effects-and-Effect-sizes -parent: LIMO -grand_parent: Plugins ---- -# Statistics course plot - -When it comes to statistical results, ‘image all’ gives you all F and p-values, and clusters (if computed), ‘course plot’ also shows the time course of the effects for a specified channel. In a 1-way ANOVA, this would typically be only 2 curves for 3 conditions, because the F test involves only two simultaneous contrasts (if we know A>B and B>C then we know A>C and the actual statistical test doesn’t need to compute all differences). For a contrast, this is only 1 curve, testing if the (trimmed) mean difference equal to 0 (e.g. [figure 17](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/17.jpg)). - -Here, after computing a one-way ANOVA, we can check this is the case, type: `load('LIMO.mat'); LIMO.design.C{1}` and the result is a matrix which tested for mean differences between familiar and unfamiliar faces and between scrambled and unfamiliar faces ([1 0 -1; 0 1 -1]), giving in the course plot 2 curves – (figure 23 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/23a.jpg), [spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/23b.jpg) and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/23c.jpg)). - -![Figure 23. New design pooling conditions](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/23a.jpg) -```matlab -limo_display_results(3,'Rep_ANOVA_Main_effect_1_face.mat',pwd,0.05,2,... - fullfile(pwd,'LIMO.mat'),0,'channels',49,'sumstats','mean'); % course plot -saveas(gcf, 'Rep_ANOVA_Main_effect_timecourse.fig'); close(gcf) -``` -![Figure 23. New design pooling conditions](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/23b.jpg) -```matlab -limo_display_results(3,'Rep_ANOVA_Main_effect_1_face.mat',pwd,0.05,2,... - fullfile(pwd,'LIMO.mat'),0,'channels',49,'sumstats','mean'); % spectrum plot -saveas(gcf, 'Rep_ANOVA_Main_effect_spectrum.fig'); close(gcf) -``` -![Figure 23. New design pooling conditions](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/23c.jpg) -```matlab -limo_display_results(3,'Rep_ANOVA_Main_effect_1_face.mat',pwd,0.05,2,... - fullfile(pwd,'LIMO.mat'),0,'channels',49,'restrict','time','dimvalue',5,'sumstats','mean'); % course plot -saveas(gcf, 'Rep_ANOVA_Main_effect_5Hztimecourse.fig'); close(gcf) -limo_display_results(3,'Rep_ANOVA_Main_effect_1_face.mat',pwd,0.05,2,... - fullfile(pwd,'LIMO.mat'),0,'channels',49,'restrict','frequency','dimvalue',180,'sumstats','mean'); % course plot -saveas(gcf, 'Rep_ANOVA_Main_effect_180msSpectrum.fig'); close(gcf) -``` -_Figure 23. Course plot of the ANOVA_ - -It is however also important to relate results to the data. For that, we can compute averages and check on effect sizes. This can be achieved from the 2nd level menu ([figure 9](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/9.jpg)) through the ‘basic stats’ submenu. - -# Statistics effect sizes - -The 2nd level ANOVA is computed on beta parameters, or a linear combination, i.e. contrasts. The means with confidence intervals can be computed using LIMO, chosing **Central tendency and CI**. Repeated measures ANOVA being computed on **means**, here we compute the mean of each contrasts (i.e. selecting the list for con1, and repeating for con2 and con3) – you can choose do compute for all channels or just one ([figure 24](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/24.jpg)). Use **plot central tendency and differences** to visualize the results – figure 25 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/25a.jpg), [spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/25b.jpg) and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/25c.jpg). - -![Figure 24. Central tendency and CI](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/24.jpg) -_Figure 24. GUI for Central tendency and CI, selecting data type (e.g. con), summary statistics (e.g. mean), files or list (.txt) of files and the channels (e.g. full brain)._ - -![Figure 25. Mean con values ](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/25a.jpg) -![Figure 25. Mean con values ](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/25b.jpg) -```matlab -Yr = load('Yr.mat'); % ANOVA data are channel*[freq/time]frames*subjects*conditions -chanlocs = [STUDY.filepath filesep 'limo_gp_level_chanlocs.mat']; -mkdir('average_betas'); cd('average_betas'); -limo_central_tendency_and_ci(squeeze(Yr.Yr(:,:,:,1)), 'Mean',[],fullfile(pwd,'famous.mat')) -limo_central_tendency_and_ci(squeeze(Yr.Yr(:,:,:,2)), 'Mean',[],fullfile(pwd,'scrambled.mat')) -limo_central_tendency_and_ci(squeeze(Yr.Yr(:,:,:,3)), 'Mean',[],fullfile(pwd,'unfamiliar.mat')) -limo_add_plots('channel',49,fullfile(fileparts(pwd),'LIMO.mat'),... - {fullfile(pwd,'famous_Mean.mat'), fullfile(pwd,'scrambled_Mean.mat'), fullfile(pwd,'unfamiliar_Mean.mat')}) -title('mean betas channel 49'); saveas(gcf, 'Rep_ANOVA_Main_effect_Betas.fig'); close(gcf) -``` - -![Figure 25. Mean con values ](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/25c.jpg) -```matlab -Yr = load('Yr.mat'); % ANOVA data are channel*[freq/time]frames*subjects*conditions -chanlocs = [STUDY.filepath filesep 'limo_gp_level_chanlocs.mat']; -mkdir('average_betas'); cd('average_betas'); -limo_central_tendency_and_ci(squeeze(Yr.Yr(:,:,:,:,1)), 'Mean',[],fullfile(pwd,'famous.mat')) -limo_central_tendency_and_ci(squeeze(Yr.Yr(:,:,:,:,2)), 'Mean',[],fullfile(pwd,'scrambled.mat')) -limo_central_tendency_and_ci(squeeze(Yr.Yr(:,:,:,:,3)), 'Mean',[],fullfile(pwd,'unfamiliar.mat')) -limo_add_plots('channel',49,'restrict','time','dimvalue',5,fullfile(fileparts(pwd),'LIMO.mat'),... - {fullfile(pwd,'famous_Mean.mat'), fullfile(pwd,'scrambled_Mean.mat'), fullfile(pwd,'unfamiliar_Mean.mat')}) -title('mean betas channel 49 @5Hz'); saveas(gcf, 'Rep_ANOVA_Main_effect_Betas5Hz.fig'); close(gcf) -limo_add_plots('channel',49,'restrict','frequency','dimvalue',180,fullfile(fileparts(pwd),'LIMO.mat'),... - {fullfile(pwd,'famous_Mean.mat'), fullfile(pwd,'scrambled_Mean.mat'), fullfile(pwd,'unfamiliar_Mean.mat')}) -title('mean betas channel 49 @180ms'); saveas(gcf, 'Rep_ANOVA_Main_effect_Betas180ms.fig'); close(gcf) -``` -_Figure 25. Mean con values._ - - -# Raw data effect sizes - -While computing the means or trimmed means of betas/cons (1) reflects the computations done at the 2nd level, and (2) allows to understand results, it doesn’t show the underlying data and raw effect sizes, needed to interpret results. This means we want to compute Central tendency and CI on ‘Raw Data’ (understand pre-processed – [figure 26](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/26.jpg)). Select a list of LIMO files, and select to pool conditions – [1 2 3] for famous faces, [4 5 6] for scrambled faces, and [7 8 9] for unfamiliar faces. You have the option to just do it for a channel of interest or the full brain analysis, and then use your estimators. Here, we want to see the **means of each condition, using the weights from each trials** (since at the 1st level we used WLS). This needs to be repeated for every condition. Using ‘plot central tendencies and differences’ of the 2nd level menu or the ‘course plot’ of the result menu, you can plot those averages. Results are shown figure 27 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/27a.jpg), [Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/27b.jpg) and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/27c.jpg). - -![Figure 27. Mean ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/27a.jpg) -```matlab -LIMOfiles = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'LIMO_files_FaceRepAll_GLM_Channels_Time_WLS.txt'); -mkdir('ERPs'); cd('ERPs'); -limo_central_tendency_and_ci(LIMOfiles, [1 2 3], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'famous.mat')) -limo_central_tendency_and_ci(LIMOfiles, [4 5 6], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'scrambled.mat')) -limo_central_tendency_and_ci(LIMOfiles, [7 8 9], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'unfamiliar.mat')) -limo_add_plots('channel',49,fullfile(fileparts(pwd),'LIMO.mat'),... - {fullfile(pwd,'famous_Mean_of_Weighted mean.mat'),fullfile(pwd,'scrambled_Mean_of_Weighted mean.mat'),... - fullfile(pwd,'unfamiliar_Mean_of_Weighted mean.mat')}); title('ERPs channel 49') -saveas(gcf, 'Rep_ANOVA_Main_effectERP.fig'); close(gcf) -``` -![Figure 27. Mean Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/27b.jpg) -```matlab -LIMOfiles = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'LIMO_files_FaceRepAll_GLM_Channels_Frequency_WLS.txt'); -mkdir('ERPs'); cd('ERPs'); -limo_central_tendency_and_ci(LIMOfiles, [1 2 3], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'famous.mat')) -limo_central_tendency_and_ci(LIMOfiles, [4 5 6], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'scrambled.mat')) -limo_central_tendency_and_ci(LIMOfiles, [7 8 9], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'unfamiliar.mat')) -limo_add_plots('channel',49,fullfile(fileparts(pwd),'LIMO.mat'),... - {fullfile(pwd,'famous_Mean_of_Weighted mean.mat'),fullfile(pwd,'scrambled_Mean_of_Weighted mean.mat'),... - fullfile(pwd,'unfamiliar_Mean_of_Weighted mean.mat')}); title('ERPs channel 49') -saveas(gcf, 'Rep_ANOVA_Main_effectSpectrum.fig'); close(gcf) -``` - -![Figure 27. Mean ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/27c.jpg) -```matlab -LIMOfiles = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'LIMO_files_FaceRepAll_GLM_Channels_Time-Frequency_WLS.txt'); -mkdir('ERSPs'); cd('ERSPs'); -limo_central_tendency_and_ci(LIMOfiles, [1 2 3], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'famous.mat')) -limo_central_tendency_and_ci(LIMOfiles, [4 5 6], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'scrambled.mat')) -limo_central_tendency_and_ci(LIMOfiles, [7 8 9], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'unfamiliar.mat')) -limo_add_plots('channel',49,'restrict','Time','dimvalue',5,fullfile(fileparts(pwd),'LIMO.mat'),... - {fullfile(pwd,'famous_Mean_of_Weighted mean.mat'),fullfile(pwd,'scrambled_Mean_of_Weighted mean.mat'),... - fullfile(pwd,'unfamiliar_Mean_of_Weighted mean.mat')}); title('ERSPs channel 49 @5Hz') -saveas(gcf, 'Rep_ANOVA_Main_effect_ERSPs5Hz.fig'); close(gcf) -limo_add_plots('channel',49,'restrict','Frequency','dimvalue',180,fullfile(fileparts(pwd),'LIMO.mat'),... - {fullfile(pwd,'famous_Mean_of_Weighted mean.mat'),fullfile(pwd,'scrambled_Mean_of_Weighted mean.mat'),... - fullfile(pwd,'unfamiliar_Mean_of_Weighted mean.mat')}); title('ERSPs channel 49 @180ms') -saveas(gcf, 'Rep_ANOVA_Main_effect_ERSPs180msz.fig'); close(gcf) -``` -_Figure 27. Mean data across subjects (using weighted trials)._ - -Note that each time you make a figure, the underlying data are returned directly in the workspace, under ‘plotted_data’ – which makes it convenient to obtain results to report. - diff --git a/plugins/limo/5.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level).md b/plugins/limo/5.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level).md deleted file mode 100644 index 68a30d2..0000000 --- a/plugins/limo/5.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level).md +++ /dev/null @@ -1,61 +0,0 @@ ---- -layout: default -title: 5.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level) -long_title: 5.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level) -parent: LIMO -grand_parent: Plugins ---- -Let’s consider again the contrast of interest (Famous+Unfamiliar) Faces vs. Scrambled faces. This can be obtained from the 1-way ANOVA analysis, using a contrast [0.5 -1 0.5] (figures [16](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/16.jpg) - [17](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/17.jpg)). This can also be obtained by computing this contrast per subject and performing a one sample t-test on this contrast. Since we have 9 conditions with the full design, the contrast is [0.5 0.5 0.5 -1 -1 -1 0.5 0.5 0.5]. To add one or many contrast, one must create a variable and save this as a file (while we could have a GUI, using a saved variable allows 1. to run many contrasts (each line is a new contrast to run) and 2. to be able to return and check this file a few weeks/months later after the analysis). - -In the command window type: -```matlab -C = [0.5 0.5 0.5 -1 -1 -1 0.5 0.5 0.5]; -save('face_contrast','C') -``` - -In general, if the research question is a difference and/or interaction, there is no point doing a 2nd level ANOVA and **it is recommended to pool conditions at the 1st level because there is less variance to account at the group level**. - -# 1st level contrast - -From the LIMO main interface call the contrast manager and select to run the analysis on all subjects. Select the list of LIMO files to update and finally the contrast file ([figure 28](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/28.jpg)). LIMO will then compute the contrast for each subject, updating the LIMO.mat files and writing con files. A new list of con files is also created. - -![Figure 28. create contrasts](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/28.jpg) -_Figure 28. GUI to create contrasts for all subjects from already computed models_ - -Rather than loading a contrast into the GUI, we can pass all this info in command line (choosing your analysis ERP/Spectrum/ERSP): -```matlab -cd(STUDY.filepath) -contrast.mat = [0.5 0.5 0.5 -1 -1 -1 0.5 0.5 0.5]; -% ERP -[~,~,contrast.LIMO_files] = limo_get_files([],[],[],... - fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'LIMO_files_FaceRepAll_GLM_Channels_Time_WLS.txt')); -% Spectrum -[~,~,contrast.LIMO_files] = limo_get_files([],[],[],... - fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'LIMO_files_FaceRepAll_GLM_Channels_Frequency_WLS.txt')); -% ERSP -[~,~,contrast.LIMO_files] = limo_get_files([],[],[],... - fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'LIMO_files_FaceRepAll_GLM_Channels_Time-Frequency_WLS.txt')); -limo_batch('contrast only',[],contrast) -``` - -# 2nd level - -From the 2nd level GUI, select ‘one sample t-test’ and the new list of con files (should be con4 if you computed the revised ANOVA model). Once computations are done, using the result GUI and image all to see the results (figure 29 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/29a.jpg), [Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/29b.jpg), and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/29c.jpg)). Results are similar to the contrast in the one-way ANOVA but the number of clusters and/or significance differ. - -![Figure 29a. ttest ersp](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/29a.jpg) -![Figure 29b. ttest ersp](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/29b.jpg) -![Figure 29c. ttest ersp](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/29c.jpg) -_Figure 29. One-sample t-test on 1st level contrast_ - -These steps can be executed in command line as: -```matlab -chanlocs = [STUDY.filepath filesep 'limo_gp_level_chanlocs.mat']; -mkdir('one_sample'); cd('one_sample'); -limo_random_select('one sample t-test',chanlocs,'LIMOfiles',... - fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'con_4_files_FaceRepAll_GLM_Channels_Time_WLS.txt'),... - 'analysis_type','Full scalp analysis', 'type','Channels','nboot',101,'tfce',0); -limo_eeg(5,pwd) -limo_display_results(3,'one_sample_ttest_parameter_1.mat',pwd,0.05,2,... - fullfile(pwd,'LIMO.mat'),0,'channels',49,'sumstats','mean'); % course plot -saveas(gcf, 'One_sample_timecourse.fig'); close(gcf) -``` \ No newline at end of file diff --git a/plugins/limo/6.-Summary-statistics-of-differences.md b/plugins/limo/6.-Summary-statistics-of-differences.md deleted file mode 100644 index 64e6523..0000000 --- a/plugins/limo/6.-Summary-statistics-of-differences.md +++ /dev/null @@ -1,70 +0,0 @@ ---- -layout: default -title: 6.-Summary-statistics-of-differences -long_title: 6.-Summary-statistics-of-differences -parent: LIMO -grand_parent: Plugins ---- -The [one sample t-test](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/5.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level)) was computed on the contrasts faces vs scrambled faces, i.e. on differences. To fully appreciate the effect, we thus have to check differences on contrasts before looking at raw data. - -Note that because we use a Bayesian confidence interval, we test directly H1 i.e. that the difference is not 0; which differs from the one-sample t-test that tests the null, if the difference is 0 (and the significant effect tells you the you should reject the hypothesis that this is 0 - still does not prove it is!). - -# Trimmed means of 1st level parameters - -We start by computing the trimmed mean (because the t-test is on trimmed means) on the 1st level contrasts as we did before (figure 30 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/30a.jpg),[Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/30b.jpg) and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/30c.jpg)), thus reflecting the t/p value maps. - -![Figure 30. TM ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/30a.jpg) -![Figure 30. TM Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/30b.jpg) -``` matlab -Yr = load('Yr.mat'); % this is the 1st level beta parameters -chanlocs = [STUDY.filepath filesep 'limo_gp_level_chanlocs.mat']; -mkdir('average_betas'); cd('average_betas'); -limo_central_tendency_and_ci(Yr.Yr, 'Trimmed mean',[],fullfile(pwd,'average.mat')); % could also use the text file as input -limo_add_plots('channel',49,fullfile(fileparts(pwd),'LIMO.mat'),... - {fullfile(pwd,'average_Trimmed_mean.mat')}); title('Average at channel 49') -saveas(gcf, 'Average.fig'); close(gcf); cd .. -``` -![Figure 30. TM ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/30c.jpg) -``` matlab -Yr = load('Yr.mat'); % this is the 1st level beta parameters -chanlocs = [STUDY.filepath filesep 'limo_gp_level_chanlocs.mat']; -mkdir('average_betas'); cd('average_betas'); -limo_central_tendency_and_ci(Yr.Yr, 'Trimmed mean',[],fullfile(pwd,'average.mat')) -limo_add_plots('channel',49,'restrict','time','dimvalue',5,fullfile(fileparts(pwd),'LIMO.mat'),... - {fullfile(pwd,'average_Trimmed_mean.mat')}); title('Average at channel 49') -saveas(gcf, 'Average5Hz.fig'); close(gcf); -limo_add_plots('channel',49,'restrict','frequency','dimvalue',180,fullfile(fileparts(pwd),'LIMO.mat'),... - {fullfile(pwd,'average_Trimmed_mean.mat')}); title('Average at channel 49') -saveas(gcf, 'Average180ms.fig'); close(gcf); -``` -_Figure 30. Trimmed mean of contrasts faces>scrambled_ - -## Trimmed mean differences of raw data - -To look at the data, we compute the **weighted mean for all famous and unfamiliar faces** (pool conditions [1 2 3 7 8 9]) and for scrambled faces (pool conditions [4 5 6]) separately and use the trimmed mean across subjects ([figure 26](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/26.jpg)). Instead of visualizing each average, we can also **Make and plot a difference**, using the *single subject's data* saved when computing averages (two files are saved the mean with CI and the single subjects, that's what we use here). When plotting this, we can now see the difference directly with its confidence interval (figure 31 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/31a.jpg),[Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/31b.jpg) and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/31c.jpg)). The saved file can of course seen again using plot central tendencies and differences. - -![Figure 31. TMD ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/31a.jpg) -![Figure 31. TMD Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/31b.jpg) -``` matlab -% weighted mean for faces and scramble, followed by the difference -mkdir('Avg_data'); cd('Avg_data'); -limo_central_tendency_and_ci(LIMOfiles, [1 2 3 7 8 9], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'faces.mat')) -limo_central_tendency_and_ci(LIMOfiles, [4 5 6], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'scrambled.mat')) -limo_plot_difference('faces_single_subjects_Weighted mean.mat','scrambled_single_subjects_Weighted mean',... - 'type','paired','name','faces_vs_scrambled','channel',49); % default 20% trimmed mean -saveas(gcf, 'Difference.fig'); close(gcf) -``` -![Figure 31. TMD ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/31c.jpg) -``` matlab -mkdir('ERSPs'); cd('ERSPs'); -limo_central_tendency_and_ci(LIMOfiles, [1 2 3 7 8 9], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'faces.mat')) -limo_central_tendency_and_ci(LIMOfiles, [4 5 6], chanlocs, 'Weighted mean', 'Mean', [],fullfile(pwd,'scrambled.mat')) -limo_plot_difference('faces_single_subjects_Weighted mean.mat','scrambled_single_subjects_Weighted mean',... - 'type','paired','name','faces_vs_scrambled','channel',49,'restrict','frequency'); % default 20% trimmed mean -saveas(gcf, 'DifferenceFreq.fig'); close(gcf) -limo_add_plots('channel',49,'restrict','time','dimvalue',[5 10 15],fullfile(fileparts(pwd),'LIMO.mat'),... - {fullfile(pwd,'faces_vs_scrambled.mat')}); % let's do channel 49 at 3 Frequencies -title('Average differences faces vs scrambled at 5-10-15Hz channel 49'); -saveas(gcf, 'Difference5-10-15Hz.fig'); close(gcf) -``` -_Figure 31. Trimmed means of weighted means for faces and scrambled faces, and their trimmed mean difference._ diff --git a/plugins/limo/7.-Two-ways-ANOVA-(Faces-x-Repetition).md b/plugins/limo/7.-Two-ways-ANOVA-(Faces-x-Repetition).md deleted file mode 100644 index fadbd2b..0000000 --- a/plugins/limo/7.-Two-ways-ANOVA-(Faces-x-Repetition).md +++ /dev/null @@ -1,46 +0,0 @@ ---- -layout: default -title: 7.-Two-ways-ANOVA-(Faces-x-Repetition) -long_title: 7.-Two-ways-ANOVA-(Faces-x-Repetition) -parent: LIMO -grand_parent: Plugins ---- -Lets’ consider now all 9 conditions: 3 types of faces (familiar, unfamiliar, scrambled) and 3 repetition levels (immediate, small delay, long delay). This is analysed using a repeated measure ANOVA. - -# 1st level analysis - -We need to compute a model for each subject, in which all 9 conditions are present. This should have been done already if you have computed the 1st level for the 1-way ANOVA revised section – if not, do it as proposed, i.e. setting up the model with contrasts ([figure 20](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/20.jpg)). - -# 2nd level analysis - -We can now compute the repeated measure ANOVA at the group level, using beta files. The model is a repeated measure ANOVA with 1 group, and 2 factors of three levels i.e. [3 3]. As you will notice, the computational time is higher, since now we have 2 additional effects to compute, not just the face effect but also the repetition effect and the interaction. Each one can be visualized using image all in the result GUI (figure 32 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/32a.jpg), [Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/32b.jpg) and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/32c.jpg) ). - -![Figure 32a. 2*2 ANOVA ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/32a.jpg) -![Figure 32b. 2*2 ANOVASpectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/32b.jpg) -![Figure 32c. 2*2 ANOVA ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/32c.jpg) -_Figure 32. Main face effect (left) and repetition effect (right)_ - -As with the one-way ANOVA, this analysis can be done in command line, here concatenating parameters into cell arrays. -```matlab -cd(STUDY.filepath) -chanlocs = [STUDY.filepath filesep 'limo_gp_level_chanlocs.mat']; -mkdir('Face-Repetition_ANOVA');cd('Face-Repetition_ANOVA') -LIMOPath = limo_random_select('Repeated Measures ANOVA',chanlocs,'LIMOfiles',... - fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'Beta_files_FaceRepAll_GLM_Channels_Time_WLS.txt'),... - 'analysis_type','Full scalp analysis','parameters',{[1 2 3],[4 5 6],[7 8 9]},... - 'factor names',{'face','repetition'},'type','Channels','nboot',1000,'tfce',0,'skip design check','yes'); -``` -## Contrast - -At this stage, as we did before we can perform some contrast to check which conditions are driving the effects. Let’s have a look at the difference Famous vs Unfamiliar – the contrast is [1 1 1 0 0 0 -1 -1 -1] (figure 33 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/33a.jpg), [Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/33b.jpg) and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/33c.jpg) and compare that with a [paired t-test (next section)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/8.-Paired-t-test-(Famous-vs-Unfamiliar)). - -![Figure 33a. Con ANOVA ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/33a.jpg) -![Figure 33b. Con ANOVASpectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/33b.jpg) -![Figure 33c. Con ANOVA ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/33c.jpg) -_Figure 33. Contrast famous vs unfamiliar_ - -```matlab -% add contrast famous>unfamiliar -limo_contrast(fullfile(pwd,'Yr.mat'),fullfile(pwd,'LIMO.mat'), 3 ,[1 1 1 0 0 0 -1 -1 -1]); % compute a new contrast -limo_contrast(fullfile(pwd,'Yr.mat'),fullfile(pwd,'LIMO.mat'), 4); % do the bootstrap - although here there is no effect anyway -``` diff --git a/plugins/limo/8.-Paired-t-test-(Famous-vs-Unfamiliar).md b/plugins/limo/8.-Paired-t-test-(Famous-vs-Unfamiliar).md deleted file mode 100644 index d3f4661..0000000 --- a/plugins/limo/8.-Paired-t-test-(Famous-vs-Unfamiliar).md +++ /dev/null @@ -1,43 +0,0 @@ ---- -layout: default -title: 8.-Paired-t-test-(Famous-vs-Unfamiliar) -long_title: 8.-Paired-t-test-(Famous-vs-Unfamiliar) -parent: LIMO -grand_parent: Plugins ---- -Let say you only want to know if famous faces differ from unfamiliar faces – again an ANOVA could be set up test the main effect and using contrasts. Alternatively, if that is the only effect of interest, you can compute a contrast at the subject level and do a paired- t-test on contrasts. - -Crucially, let's say the research question is familiar vs unfamiliar and scrambled are just a control - doing the ANOVA is a little meaningless because you now include scrambled as a condition when in fact it's a control - using contrasts we can compute those differences [1 1 1 -1 -1 -1 0 0 0 0 ; 0 0 0 -1 -1 -1 1 1 1 0] and then compute a paired t-test familiar vs unfamiliar controlled for scrambled. Note however, that the contrast within the ANOVA also controls for the presence of scrambled in the model. - -# 1st level - -compute 2 contrasts as described [previously](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/5.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level)), or in command line: - -``` matlab -cd(STUDY.filepath) -[~,~,contrast.LIMO_files] = limo_get_files([],[],[],... - fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],... - 'LIMO_files_FaceRepAll_GLM_Channels_Time_WLS.txt')); -contrast.mat = [1 1 1 -1 -1 -1 0 0 0 ; 0 0 0 -1 -1 -1 1 1 1]; -limo_batch('contrast only',[],contrast); -``` - -# 2nd level - -From the 2nd level menu ([figure 9](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/9.jpg)), click on ‘paired t-test’, and select the contrasts computed previously for each condition (i.e. con1 and con3). Results (figure 34 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/34a.jpg), [Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/34b.jpg) and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/34c.jpg)) are very similar to figure 33 ([ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/33a.jpg), [Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/33b.jpg) and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/33c.jpg) i.e. the post-hoc test from the 2-way ANOVA and differences relates to the fact that different amount of variance is accounted for, and the paired t-test uses trimmed means across subjects. - -![Figure 34a. t-test ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/34a.jpg) -![Figure 34b. t-test pectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/34b.jpg) -![Figure 34c. t-test ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/34c.jpg) -_Figure 34. Contrast famous vs unfamiliar_ - -This can be executed in command line as: -```matlab -mkdir('Paired_ttest'); cd('Paired_ttest'); -chanlocs = [STUDY.filepath filesep 'limo_gp_level_chanlocs.mat']; -files = {fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'con_5_files_FaceRepAll_GLM_Channels_Time_WLS.txt'), ... - fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'con_6_files_FaceRepAll_GLM_Channels_Time_WLS.txt')}; -limo_random_select('paired t-test',chanlocs,'LIMOfiles',files,... - 'analysis_type','Full scalp analysis', 'type','Channels','nboot',1000,'tfce',0); -``` - diff --git "a/plugins/limo/9.-Between-subjects\342\200\231-ANOVAs-with-repeated-factors.md" "b/plugins/limo/9.-Between-subjects\342\200\231-ANOVAs-with-repeated-factors.md" deleted file mode 100644 index d136907..0000000 --- "a/plugins/limo/9.-Between-subjects\342\200\231-ANOVAs-with-repeated-factors.md" +++ /dev/null @@ -1,39 +0,0 @@ ---- -layout: default -title: 9.-Between-subjects’-ANOVAs-with-repeated-factors -long_title: 9.-Between-subjects’-ANOVAs-with-repeated-factors -parent: LIMO -grand_parent: Plugins ---- -After [editing the STUDY for group](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/Between-Subjects-Categorical-Designs), we have the same Beta files as before, but also txt files split per group which makes file selection easier. - -From the 2nd level GUI ([figure 9](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/9.jpg)), after creating/selecting a working directory and have selected your group level channel location file (likely in the /derivative root folder) select ANOVA, Repeated Measures ANOVA, Full scalp analysis, enter 2 groups, the finally select to use betas ; picking up iteratively list of beta files per group ([figure 36](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/36.jpg)). As before you also have to enter factors (here 3) and indicate the relevant beta parameters (here [1:3]). - -![Figure 36. Gp * Repeated measure ANOVA](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/36.jpg) -_Figure 36. Gp * Repeated measure ANOVA_ - -These steps can be executed in command line as: -```matlab -mkdir('Gp-Conditions_ANOVA'); cd('Gp-Conditions_ANOVA'); -chanlocs = [STUDY.filepath filesep 'limo_gp_level_chanlocs.mat']; -Files = cell(2,1); % groups in rows, repeated measures in columns -Files{1} = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'Beta_files_Gp1_ANOVA_Faces_GLM_Channels_Time_WLS.txt'); -Files{2} = fullfile(STUDY.filepath,['LIMO_' STUDY.filename(1:end-6)],'Beta_files_Gp2_ANOVA_Faces_GLM_Channels_Time_WLS.txt'); -LIMOPath = limo_random_select('Repeated Measures ANOVA',chanlocs,'LIMOfiles',Files,... - 'analysis_type','Full scalp analysis','parameters',{[1 2 3];[1 2 3]},... - 'factor names',{'face'},'type','Channels','nboot',1000,'tfce',0,'skip design check','yes'); -``` - -Output files are now the main effect of faces, the main effect of group and the interaction gp*faces. Results for the main effect are similar to the one-way ANOVA with faces as the only factor, but here we can test for the interaction group * faces as well (figure 37 for [ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/37a.jpg), [Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/37b.jpg) and [ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/37c.jpg)). - -![Figure 37a. Gp results ERP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/37a.jpg) -_Figure 37a. Gp results ERP_ - -![Figure 37b. Gp results Spectrum](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/37b.jpg) -_Figure 37b. Gp results Spectrum_ - -![Figure 37c. Gp*Face interaction results ERSP](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/37c.jpg) -_Figure 37c. Gp*Face interaction results ERSP_ - - - diff --git a/plugins/limo/Between-Subjects-Categorical-Designs.md b/plugins/limo/Between-Subjects-Categorical-Designs.md deleted file mode 100644 index ef3be68..0000000 --- a/plugins/limo/Between-Subjects-Categorical-Designs.md +++ /dev/null @@ -1,36 +0,0 @@ ---- -layout: default -title: Between-Subjects-Categorical-Designs -long_title: Between-Subjects-Categorical-Designs -parent: LIMO -grand_parent: Plugins ---- -We replicate here the 1-way ANOVA with familiar, unfamiliar and scrambled faces but split the data in two age groups. Of course, we can take the txt files, edit them and save copies for each group – then in LIMO MEEG we simply use these files. Here, instead, we recompute the subjects model adding in the STUDY design our groups, which will consequently save txt files per group (but not change estimates per subjects). Since some subjects have unspecified age – we create three groups based on the median (figure 35). - -Group 1 is under 26: sub- 3, 8, 15, 16, 17, 18 -Group 2 is above or equal 26: sub- 2, 5, 9, 10, 11, 12, 14 -Group 3: sub- 4, 6, 7, 13, 19 unspecified - -![Figure 35. Edit study](https://github.com/LIMO-EEG-Toolbox/limo_meeg/blob/master/resources/images/35.jpg) -_Figure 35. Editing STUDY adding groups_ - -You can update the study using pop_study typing in command line: -```matlab -cd(STUDY.filepath) -[STUDY ALLEEG] = std_editset( STUDY, ALLEEG, 'commands',{{'index',2,'group','1'}, ... - {'index',7,'group','1'},{'index',14,'group','1'},{'index',15,'group','1'}, ... - {'index',16,'group','1'},{'index',17,'group','1'},{'index',1,'group','2'}, ... - {'index',4,'group','2'},{'index',8,'group','2'},{'index',9,'group','2'}, ... - {'index',10,'group','2'},{'index',11,'group','2'},{'index',13,'group','2'}, ... - {'index',3,'group','3'},{'index',5,'group','3'},{'index',6,'group','3'}, ... - {'index',12,'group','3'}, {'index',18,'group','3'}}, 'updatedat','off','rmclust','on'); -[STUDY, EEG] = pop_savestudy( STUDY, EEG, 'savemode','resave'); -``` - -Estimate the models, selecting the 1st design with face type only. As before, text files are created, with additionally a split per group of LIMO/Beta/con files. - -From here, we can perform two 2nd level analyses: -- [Between subjects’ ANOVAs with repeated factors](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/9.-Between-subjects%E2%80%99-ANOVAs-with-repeated-factors) -- [Two sample t-tests](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/10.-Two-sample-t-tests) - - diff --git a/plugins/limo/Designs-with-Continuous-variables.md b/plugins/limo/Designs-with-Continuous-variables.md deleted file mode 100644 index 3202e84..0000000 --- a/plugins/limo/Designs-with-Continuous-variables.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -layout: default -title: Designs-with-Continuous-variables -long_title: Designs-with-Continuous-variables -parent: LIMO -grand_parent: Plugins ---- -- [Regression among subjects](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/11.-Regression-among-subjects) -- [Regression at the trial level](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/12.-Regression-at-the-trial-level) \ No newline at end of file diff --git a/plugins/limo/_Sidebar.md b/plugins/limo/_Sidebar.md deleted file mode 100644 index 7bac219..0000000 --- a/plugins/limo/_Sidebar.md +++ /dev/null @@ -1,31 +0,0 @@ ---- -layout: default -title: _Sidebar -long_title: _Sidebar -parent: LIMO -grand_parent: Plugins ---- -# [Home](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki) - -# [Access Scripts reproducing the tutorial](https://github.com/LIMO-EEG-Toolbox/limo_meeg/tree/master/resources/code) - -# [Preprocessing](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/1.-Preprocessing) -- getting the data -- preprocessing - -# [Within Subject Categorical Designs](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/2.-Within-Subject-Categorical-Designs) -- [One way repeated measures ANOVA (Famous, Unfamiliar, Scrambled faces as conditions)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/2.-One-way-repeated-measures-ANOVA-(Famous,-Unfamiliar,-Scrambled-faces-as-conditions))) -- [One way repeated measures ANOVA revised (Famous, Unfamiliar, Scrambled faces as 1st level contrasts)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/3.--One-way-repeated-measures-ANOVA-revised-(Famous,-Unfamiliar,-Scrambled-faces-as-1st-level-contrasts)) -- [Summary statistics to measure and report effects and effect sizes](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/4.-Summary-statistics:-Effects-and-Effect-sizes) -- [One sample t-test (contrasting Full Faces vs Scrambled Faces at the subject level)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/5.-One-sample-t-test-(contrasting-Full-Faces-vs-Scrambled-Faces-at-the-subject-level)) -- [Summary statistics of differences](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/6.-Summary-statistics-of-differences) -- [Two-ways ANOVA (Faces x Repetition)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/7.-Two-ways-ANOVA-(Faces-x-Repetition)) -- [Paired t-test (Famous vs Unfamiliar)](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/8.-Paired-t-test-(Famous-vs-Unfamiliar)) - -# [Between Subjects Categorical Designs](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/Between-Subjects-Categorical-Designs) -- [Between subjects’ ANOVAs with repeated factors](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/9.-Between-subjects%E2%80%99-ANOVAs-with-repeated-factors) -- [Two sample t-tests](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/10.-Two-sample-t-tests) - -# [Designs with Continuous variables](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/Designs-with-Continuous-variables/) -- [Regression among subjects](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/11.-Regression-among-subjects) -- [Regression at the trial level](https://github.com/LIMO-EEG-Toolbox/limo_meeg/wiki/12.-Regression-at-the-trial-level) \ No newline at end of file diff --git a/plugins/limo/index.md b/plugins/limo/index.md deleted file mode 100644 index 735227d..0000000 --- a/plugins/limo/index.md +++ /dev/null @@ -1,38 +0,0 @@ ---- -layout: default -title: LIMO -long_title: LIMO -parent: Plugins -has_children: true -nav_order: 27 ---- -To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/LIMO-EEG-Toolbox/limo_meeg). - -# LInear MOdeling of MEEG data - -The LInear MOdelling of MEEG data (LIMO MEEG) toolbox is a Matlab toolbox dedicated to the statistical analysis of MEEG data. It has some interfacing with EEGLAB (in particular the STUDY in the EEGLAB develop version) to act as a plug in. However, once data are imported all is performed within LIMO MEEG and the toolbox can thus work for any data sets. - -This repo is the stable version of LIMO MEEG (v2) to be used with EEGLAB (https://sccn.ucsd.edu/eeglab/) but can be used with in other applications like FieldTrip (http://www.fieldtriptoolbox.org/) or BrainStorm (https://neuroimage.usc.edu/brainstorm/) for your research applications. The previous version (1.5) is now archived here: http://datashare.is.ed.ac.uk/handle/10283/2190 - -## Installation - -Have EEGLAB installed (because we call some functions) and LIMO in the plug-in directory. - -## Documentation -in the doc directory (a bit outdated) -and of course the [wiki](https://github.com/LIMO-EEG-Toolbox/limo_eeg/wiki) - -## LIMO tutorial dataset - -With the software we released a dataset that can now be cited and downloaded here: http://datashare.is.ed.ac.uk/handle/10283/2189 - -## Questions - -Best to use the discussion forums like the eeglab mailing list or neurostar (tagging people) for general analysis questions. -You can also email directly or raise a github issue, in particular for bugs. - -## Contribute - -Anyone is welcome to contribute ! check here [how you can get involved](https://github.com/LIMO-EEG-Toolbox/limo_eeg/blob/master/contributing.md), the [code of conduct](https://github.com/LIMO-EEG-Toolbox/limo_eeg/blob/master/code_of_conduct.md). - -Contributors are listed [here](https://github.com/LIMO-EEG-Toolbox/limo_eeg/blob/master/contributors.md) diff --git a/plugins/nwbio/index.md b/plugins/nwbio/index.md index 3c06404..f610cfc 100644 --- a/plugins/nwbio/index.md +++ b/plugins/nwbio/index.md @@ -3,7 +3,6 @@ layout: default title: nwbio long_title: nwbio parent: Plugins -has_children: true nav_order: 13 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/nwbio). diff --git a/plugins/relica/index.md b/plugins/relica/index.md index 84312a7..1c37615 100644 --- a/plugins/relica/index.md +++ b/plugins/relica/index.md @@ -3,7 +3,6 @@ layout: default title: relica long_title: relica parent: Plugins -has_children: true nav_order: 20 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/relica). diff --git a/plugins/roiconnect/index.md b/plugins/roiconnect/index.md index 4c133fe..c0a42fb 100644 --- a/plugins/roiconnect/index.md +++ b/plugins/roiconnect/index.md @@ -3,7 +3,6 @@ layout: default title: roiconnect long_title: roiconnect parent: Plugins -has_children: true nav_order: 3 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/roiconnect). diff --git a/plugins/std_dipoleDensity/index.md b/plugins/std_dipoleDensity/index.md index 97c0e83..1081dc9 100644 --- a/plugins/std_dipoleDensity/index.md +++ b/plugins/std_dipoleDensity/index.md @@ -3,7 +3,6 @@ layout: default title: std_dipoleDensity long_title: std_dipoleDensity parent: Plugins -has_children: true nav_order: 21 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/std_dipoleDensity). diff --git a/plugins/trimOutlier/index.md b/plugins/trimOutlier/index.md index b738d50..bc865d5 100644 --- a/plugins/trimOutlier/index.md +++ b/plugins/trimOutlier/index.md @@ -3,7 +3,6 @@ layout: default title: trimOutlier long_title: trimOutlier parent: Plugins -has_children: true nav_order: 10 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/trimOutlier). diff --git a/plugins/viewprops/index.md b/plugins/viewprops/index.md index 6b78065..6de4fe4 100644 --- a/plugins/viewprops/index.md +++ b/plugins/viewprops/index.md @@ -3,7 +3,6 @@ layout: default title: viewprops long_title: viewprops parent: Plugins -has_children: true nav_order: 22 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/viewprops). diff --git a/plugins/zapline-plus/index.md b/plugins/zapline-plus/index.md index 5506ee9..6681cb4 100644 --- a/plugins/zapline-plus/index.md +++ b/plugins/zapline-plus/index.md @@ -3,7 +3,6 @@ layout: default title: zapline-plus long_title: zapline-plus parent: Plugins -has_children: true nav_order: 8 --- To view the plugin source code, please visit the plugin's [GitHub repository](https://github.com/sccn/zapline-plus). @@ -11,7 +10,7 @@ To view the plugin source code, please visit the plugin's [GitHub repository](ht # Zapline-plus Improvements of the ZapLine function to remove line noise from EEG/MEG data. Adds automatic detection of the number of components to remove, and chunks the data into segments to account for nonstationarities. -Dependencies of Noisetools are provided with permission by Alain de Cheveigné. Please visit the original repository for more info and additional noise removal tools: http://audition.ens.fr/adc/NoiseTools/ +Dependencies of Noisetools are provided with permission by Alain de Cheveigné. Please visit the original repository for more info and additional noise removal tools: [http://audition.ens.fr/adc/NoiseTools/](http://audition.ens.fr/adc/NoiseTools/) # Quick start ```matlab @@ -25,7 +24,10 @@ EEG = clean_data_with_zapline_plus_eeglab_wrapper(EEG,struct('noisefreqs',[50])) # Please cite -Original Zapline paper: Cheveigné, Alain de. 2020. “ZapLine: A Simple and Effective Method to Remove Power Line Artifacts.” NeuroImage 207 (February): 116356. https://www.sciencedirect.com/science/article/pii/S1053811919309474 +Original Zapline paper: Cheveigné, Alain de. 2020. “ZapLine: A Simple and Effective Method to Remove Power Line Artifacts.” NeuroImage 207 (February): 116356. [https://www.sciencedirect.com/science/article/pii/S1053811919309474](https://www.sciencedirect.com/science/article/pii/S1053811919309474). -Zapline-plus paper: Klug, M., and N. A. Kloosterman. 2021. “Zapline-plus: A Zapline Extension for Automatic and Adaptive Removal of Frequency-Specific Noise Artifacts in M/EEG.” bioRxiv. https://www.biorxiv.org/content/10.1101/2021.10.18.464805.abstract. +Zapline-plus paper: Klug, M., and N. A. Kloosterman. 2021. “Zapline-plus: A Zapline Extension for Automatic and Adaptive Removal of Frequency-Specific Noise Artifacts in M/EEG.” bioRxiv. [https://www.biorxiv.org/content/10.1101/2021.10.18.464805.abstract](https://www.biorxiv.org/content/10.1101/2021.10.18.464805.abstract). +# Versions + +- v1.0, initial version