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1. A-Compact-and-Interpretable-Convolutional-Neural-Network-for-Single-Channel-EEG A-Compact-and-Interpretable-Convolutional-Neural-Network-for-Single-Channel-EEG Public

   In this project, we propose a CNN model to classify single-channel EEG for driver drowsiness detection. We use the Class Activation Map (CAM) method for visualization. Results show that the model n…

   Python 22 11

2. Towards-Best-Practice-of-Interpreting-Deep-Learning-Models-for-EEG-based-BCI Towards-Best-Practice-of-Interpreting-Deep-Learning-Models-for-EEG-based-BCI Public

   In this project, we implemented 7 interpretation techniques on two benchmark deep learning models "EEGNet" and "InterpretableCNN" for EEG-based BCI. The methods include: gradient×input, DeepLIFT, i…

   Python 14 3

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

   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 import…

   Python 12 4

4. Subject-Independent-Drowsiness-Recognition-from-Single-Channel-EEG-with-an-Interpretable-CNN-LSTM Subject-Independent-Drowsiness-Recognition-from-Single-Channel-EEG-with-an-Interpretable-CNN-LSTM Public

   In this project, we propose a CNN-LSTM model to classify single-channel EEG for driver drowsiness detection. We designed a visualization technique by taking advantage of the hidden states output by…

   Python 11 2