Learning Spatial-Spectral-Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment.

Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable for extending a pre-trained model to new workload conditions. Because brain dynamics are complex across different tasks, it is difficult to propose efficient human-designed features based on prior knowledge. Therefore, this study proposes a concatenated structure of deep recurrent and 3D convolutional neural networks (R3DCNN) to learn EEG features across different tasks without prior knowledge. First, this study adds frequency and time dimensions to EEG topographic maps based on a Morlet wavelet transformation. Then, R3DCNN is proposed to simultaneously learn EEG features from the spatial, spectral and temporal dimensions. The proposed model is validated based on EEG signals collected from twenty subjects. This study employs a binary classification of low and high mental workload across spatial n-back and arithmetic tasks. The results show that the R3DCNN achieves an average accuracy of 88.9%, which is a significant increase compared with that of the state-of-the-art methods. In addition, the visualization of the convolutional layers demonstrates that the deep neural network can extract detailed features. These results indicate that R3DCNN is capable of identifying the mental workload levels for cross-task conditions.