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.

Problem

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Audience

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Market

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Pricing

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