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.

Installation

This package can be installed via pip from the TestPyPi Index using

pip install -i https://test.pypi.org/simple/ EEGToolkit

Example Usage

CLI

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

EEGToolkit --viewer
# or 
EEGToolkit -i 

API

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 )

Example Output

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

Extract the time-period from $0.1 [sec]$ before to $1 [sec]$ after each event for the EEG signal. Store these time-periods in two arrays, one for each of the two event-/sound-types.

Task 2

Plot the average response for each type of event, with shading for the standard error. On the plot, also indicate $t=0$, the amplitude of the signal in microvolts (Y-axis), and the time relative to sound onset in $[ms]$ (X-axis).

Task 3

For each sound type, for each time-point after sound onset, test wheter it is significantly different across trials from the baseline. Also test at what time-points after sound onset the EEG signals for the two sound-types are different from each other. Produce one plot containing the average EEG signal of each sound type, along with lines indicating the significant time-periods.

Milestones

• Part 1: Project management and first deliverable (Deadline: April 28)
• Part 2: GitHub and second deliverable (Deadline: May 5)
• Part 3: Classes and refactoring (Deadline: May 12)
• Part 4: Unit tests and issues (Deadline: May 19)
• Part 5: Third deliverable, presentation and virtual environment (Deadline: June 2)

Project roadmap

Main Objective

The goal is to develop a small package with CLI to evaluate EEG data for reaction delays given different auditory stimuli. While the outset data is given as a binary stimulus experiment, extending to an arbitrary number of different stimuli would be a desirable extention.

Implementation

General TODOs

• proper Docstrings and comments in general ...
• re-factoring for private methods ...
• requirements.txt
• setup.py
  • (optional but kinda cool) a cool Entrypoint to allow direct CLI calling...
• testpypi distribution
• HTML documentation

CLI TODOs:

• Core is pretty much finished already...
• Change the argparse settings to allow defaults for parameters like x_scale to allow easier usage

Task TODOs:

Task 1:

• Event extraction is pretty much finished already

Possible additions:

• split reading the files and extracting data, thereby allowing different filetypes to be processed. In case this is done, we might add support for txt / tsv / csv files for more general applicability of the software.

Task 2:

• plotting is pretty much finished already...
• Add units to the figure axes labels
• (optional) adjust color scheme ...

Task 3:

• What about the intra-signal baseline-comparison ?
• signal-comparison is finished already...