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Author Kunjira Kingphai & Yashar Moshfeghi |
| Abstract Deep learning-based approaches have recently received much attention and managed to accurately capture variance characteristics in the Electroencephalography (EEG) signals. In this paper, we aim to classify the subject’s mental workload (MWL) level from EEG signal by using deep learning models named Stacked Gated Recurrent Unit (GRU), Bidirectional GRU (BGRU), BGRU-GRU, Stacked Long-Short Term Memory (LSTM), Bidirectional LSTM (BLSTM), BLSTM-LSTM and Convolutional Neural Network (CNN). The classification was performed on a publicly available mental workload dataset, STEW. Our encouraging results show the potential of deep learning models for MWL level detection. |
