A Mental Workload Classification Method Based on GCN Modified by Squeeze-and-Excitation Residual

In some complex labor production and human-machine interactions, such as subway driving, to ensure both the efficient and rapid completion of work and the personal safety of staff and the integrity of operating equipment, the level of mental workload (MW) of operators is monitored at all times. In existing machine learning-based MW classification methods, the association information between neurons in different regions is almost not considered. To solve the above problem, a graph convolution network based on the squeeze-and-excitation (SE) block is proposed. For a raw electroencephalogram (EEG) signal, the principal component analysis (PCA) dimensionality reduction operation is carried out. After that, combined with the spatial distribution between brain electrodes, the dimensionality reduction data can be converted to graph structure data, carrying association information between neurons in different regions. In addition, we use graph convolution neural network (GCN) modified by SE residual to obtain final classification results. Here, to adaptively recalibrate channel-wise feature responses by explicitly modelling interdependencies between channels, the SE block is introduced. The residual connection can ease the training of networks. To discuss the performance of the proposed method, we carry out some experiments using the raw EEG signals of 10 healthy subjects, which are collected using the MATB-II platform based on multi-task aerial context manipulation. From the experiment results, the structural reasonableness and the performance superiority of the proposed method are verified. In short, the proposed GCN modified by the SE residual method is a workable plan of mental workload classification.