Subject-Independent-Drowsiness-Recognition-from-Single-Channel-EEG-with-an-Interpretable-CNN-LSTM
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 the LSTM layer. Results show that the model not only has a high accuracy but also learns biologically explainable features, e.g., Alpha spindles, as evidence for the drowsy state.
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