## NeuroEmo: An fMRI Dataset for Emotion Recognition

  
 Description: The purpose of the study is to enable emotion recognition analysis using culturally relevant stimuli, such as Indian Bollywood movie clips, in fMRI data collected from Indian participants. This dataset explores the neural basis of emotions in a contextually meaningful way, focusing on functional and dynamic connectivity.


## Summary

This dataset contains fMRI data collected from 40 healthy participants who watched emotional Indian movie videos clips.
Tasks included resting-state and emotion-elicitation tasks. Data was organized following the BIDS standard. 
Dataset size: 7.21 GB (BIDS-compliant format), 36 GB (raw DICOM files).

The data was collected at fMRI center of Central Institute of Psychiatry (CIP), Kanke, Ranchi.The research proposal and experimental protocol received ethical clearance from the Institute Ethics Committee, CIP (No. IEC/CIP/2022-23/1709).

## Data Collection details: 

## Acquisition details:
Scanner: Philips Ingenia 3T MRI scanner with following parameter-
-T1w data was collected for matrix size 192x192x256, voxel size 1x1x1 mm, slice thickness 1, space between slices 1, echo time 0.002943, repetition time 0.0065, flip angle 9^°. 
-Task-rest_bold was collected for matrix size 96x96x38, voxel size -2.29x2.29x4 mm, slice thickness 4, space between slices 4, echo time 0.035001, repetition time 2.02697, flip angle 90^°, slice 38.
-Task-fe_bold was collected for matrix size 128x128x36, voxel size =-1.8x1.8x4 mm, slice thickness 4, space between slices 4, echo time 0.035, repetition time 3, flip angle  90^°, slice 36.

## Stimulus videos

Videos used as stimuli were selected from [Mishra, S., Srinivasan, N., Asif, M., and Tiwary, U.S., 2023. Affective film dataset from India (AFDI): Creation and validation with an Indian sample. Journal of Cultural Cognitive Science, 7(3), pp. 255–267] based on their potential to evoke emotions. The videos were downloaded in high quality and cropped to 30-second clips to elicit specific emotions corresponding to five emotion classes: Calm, Afraid, Delighted, Depressed, and Excited.

## Tasks
Participants viewed movie clips designed to evoke different emotions task details:
onset	duration	emotion

0	30	calm
30	30	white noise
60	30	afraid
90	30	white noise
120	30	delighted
150	30	white noise
180	30	depressed
210	30	white noise
240	30	excited
270	30	white noise
300	30	delighted
330	30	white noise
360	30	depressed
390	30	white noise
420	30	calm
450	30	white noise
480	30	excited
510	30	white noise
540	30	afraid
570	30	white noise

## File Structure
	-/sub-01/
	-/anat/
	-sub-01_T1w.nii: Anatomical data of sub-01
	-sub-01_T1w.json
  	-/func/
        -sub-01_task-fe_bold.nii: Functional data for the emotion task.
	-sub-01_task-fe_bold.json
        -sub-01_task-rest_bold.nii: Resting-state functional data.
	-sub-01_task-rest_bold.json

## Preprocessing and codes used in this work can be found on https://github.com/abgeena/NeuroEmo
Data have been preprocessed using:
- SPM12
  - Motion correction
  - Slice-timing correction
  - Co-registration
  - Segmentation
  - Normalization to MNI space
  - Smoothing

## How to Use
1. Download the dataset from OpenNeuro.
2. Use Nilearn or SPM toolbox to analyze the activation and functional connectivity.

## Contact
For questions, email: [[email protected]]