00Chenyl

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

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 not only has a high accuracy but also learns biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state.

unet-brats

00Chenyl/unet-brats

00Chenyl/A-Compact-and-Interpretable-Convolutional-Neural-Network-for-Single-Channel-EEG

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 not only has a high accuracy but also learns biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state.