In this project I will use an open dataset (from the LIMO task - Guillaume A. Rousselet. LIMO EEG dataset. 2016. doi:10.7488/ds/1556.).
In this dataset, participants were subjected to a task in which they would observe two different faces (face A/face B) across trials. In each trial, the images could be 'less coherent' - meaning more blurred. Then, participants had to report which face they had just seen.
However, the question I'm trying to investigate is: can the EEG signal determine which face the participant was observing?
To this end, I built this script to read the EEG data via MNE and test different decoders to find which more accurately can distinguish from the EEG signal if the participant was subjected to seeing Face A or Face B.
Clone the project
git clone https://github.com/joaohnp/EEG_decoding
Go to the project directory
cd my-project
Install dependencies
pip install -r requeriments.txt
Run EDA.py
python EDA.py
After EDA has been ran, we now have accuracy scores for different models. With this we can investigate which classifier works best with our data.
python best_model.py
Now we can check in detail which decoder worked best in which electrode.
Data preparation and analysis: MNE, numpy
Machine Learning and Deep Learning: TensorFlow, XGBoost, LDA, SVM
For support, email [email protected].