A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG

Pytorch implementation of the paper "A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG".

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

"Jian Cui, Zirui Lan, Yisi Liu, Ruilin Li, Fan Li, Olga Sourina, Wolfgang Müller-Wittig, A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG, Methods, 2021, ISSN 1046-2023, https://doi.org/10.1016/j.ymeth.2021.04.017."

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

"CompactCNN.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

"VisualizationTech.py" contains the visualization technique based on the CAM method (Class Activation Map). 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