Skip to content
This repository has been archived by the owner on Sep 12, 2022. It is now read-only.

Commit

Permalink
update readme
Browse files Browse the repository at this point in the history
  • Loading branch information
NoahHenrikKleinschmidt committed May 19, 2022
1 parent 93b69a5 commit 6a5ed11
Show file tree
Hide file tree
Showing 2 changed files with 50 additions and 0 deletions.
Binary file modified EEGToolkit/EEGStats/__pycache__/EEGStats.cpython-38.pyc
Binary file not shown.
50 changes: 50 additions & 0 deletions README.md
Original file line number Diff line number Diff line change
@@ -1,6 +1,56 @@
# AP-EEG

## Project description
This package offers a quick way to analyse EEG data for reaction time-delay experiments. The package comes not just with an API, but additionally with both a direct command-line interface as well as a web-app based graphical user interface.

Two types of datafiles are required to run: one with the raw EEG signal data, and one with meta data about the timepoints of different types of events. Supported filetypes are `csv`, `txt`, `tsv`, and `npy`.

The output is a summary figure that compares the EEG signal of different event types between each other as well as for each event against the baseline through a position-wise t-test, and highlights significant differences in signal responses.

### Example Usage
#### CLI
```bash
EEGToolkit --eeg ./data/eeg.txt
--event ./data/events.tsv
# the frequency at which data was recorded
-f 500
# start and end of the time
# window around each event timepoint
-s -0.5 -e 1.2
```
#### GUI
```bash
EEGToolkit --viewer
# or
EEGToolkit -i
```
#### API
```python
import EEGToolkit as eeg

# setup the EEGData object
e = eeg.EEGData(
signal_path = "./data/eeg.txt",
event_path = "./data/events.tsv",
sampling_frequency = 500
)

# extract event time-windows
e.extract( start_sec = -0.5, stop_sec = 1.2 )

# perform baseline comparison for each signal
e.baseline()

# change the labelling scales of the data
timescale = 1000 # milliseconds
signalscale = 1000 # millivolts

# generate a summary figure
e.summary( x_scale = timescale, y_scale = signalscale )
```



> ## EEG data analysis
> Students who choose this task ill be provided with the raw EEG recording of one channel, sampled at $500 [Hz]$ which was recorded from a participant presented with auditory stimuli. The students will also receive an events file describing when during the recording one out of two possible sounds were presented to the subject.
> ### Task 1
Expand Down

0 comments on commit 6a5ed11

Please sign in to comment.