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Found 3 papers, 3 papers with code
Subject-Independent Drowsiness Recognition from Single-Channel EEG with an Interpretable CNN-LSTM model
For EEG-based drowsiness recognition, it is desirable to use subject-independent recognition since conducting calibration on each subject is time-consuming.
A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG
Driver drowsiness is one of main factors leading to road fatalities and hazards in the transportation industry.
EEG-based Cross-Subject Driver Drowsiness Recognition with an Interpretable Convolutional Neural Network
Results show that the model achieves an average accuracy of 78. 35% on 11 subjects for leave-one-out cross-subject drowsiness recognition, which is higher than the conventional baseline methods of 53. 40%-72. 68% and state-of-the-art deep learning methods of 71. 75%-75. 19%.
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