  For EEG-based drowsiness recognition, it is desirable to use
subject-independent recognition since conducting calibration on each subject is
time-consuming. In this paper, we propose a novel Convolutional Neural Network
(CNN)-Long Short-Term Memory (LSTM) model for subject-independent drowsiness
recognition from single-channel EEG signals. Different from existing deep
learning models that are mostly treated as black-box classifiers, the proposed
model can explain its decisions for each input sample by revealing which parts
of the sample contain important features identified by the model for
classification. This is achieved by a visualization technique by taking
advantage of the hidden states output by the LSTM layer. Results show that the
model achieves an average accuracy of 72.97% on 11 subjects for leave-one-out
subject-independent drowsiness recognition on a public dataset, which is higher
than the conventional baseline methods of 55.42%-69.27%, and state-of-the-art
deep learning methods. Visualization results show that the model has discovered
meaningful patterns of EEG signals related to different mental states across
different subjects.
