xinliangzhou/EEG-based-Cross-Subject-Driver-Drowsiness-Recognition-with-an-Interpretable-CNN

Existing work in the field of BCI treats deep learning models as black-box classifiers. In this project, we develop a novel model named "InterpretableCNN" that allows sample wise analysis of important features for classification. The model not only achieves SOTA classification accuracy of EEG signals but also reveals meaningful features from EEG.

EEG-based-Cross-Subject-Driver-Drowsiness-Recognition-with-an-Interpretable-CNN

Pytorch implementation of the model "InterpretableCNN" proposed in the paper "EEG-Based Cross-Subject Driver Drowsiness Recognition With an Interpretable Convolutional Neural Network".

If you find the codes useful, pls cite the paper:

J. Cui, Z. Lan, O. Sourina and W. Müller-Wittig, "EEG-Based Cross-Subject Driver Drowsiness Recognition With an Interpretable Convolutional Neural Network," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2022.3147208.

Paper link: https://ieeexplore.ieee.org/document/9714736

The project contains 3 code files. They are implemented with Python 3.6.6.

"InterpretableCNN.py" contains the model. required library: torch

"LeaveOneOut_acc.py" contains the leave-one-subject-out method to get the classifcation accuracies. It requires the computer to have cuda supported GPU installed. required library:torch,scipy,numpy,sklearn

"VisTechnique.py" contains the novel visualization technique proposed in the paper. It requires the computer to have cuda supported GPU installed. required library:torch,scipy,numpy,matplotlib,mne

The processed dataset has been uploaded to: https://figshare.com/articles/dataset/EEG_driver_drowsiness_dataset/14273687

If you have any problems, please Contact Dr. Cui Jian at cuij0006@ntu.edu.sg

Known Issue: The file "VisTechnique.py" has been tested on library mne v0.18 and v0.19.2. It works perfectly for v0.18, while there will be some Deprecation Warnings for v0.19.2.