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Dynamic hierarchical learning of temporal-spatial-spectral EEG features with transformers for cognitive workload estimation

The electroencephalogram (EEG) has been demonstrated to be a useful instrument for analyzing human brain activity and assessing a person's cognitive workload. Howerver, cross-subject cognitive workload assessment remains a challenging task to date because of the wide variation in EEG statistics across subjects. In this paper, a dynamic hierarchical attention based on transformer (DHAT) is proposed to learn the temporal-spatial-spectral features of EEG data. The DHAT starts with a hierarchical attention from extracted features in the frequency and time domains, learning inter-channel features and time domain features by muti-head self-attention calculation. The inferred representation of the dimension vector is calculated by automatically assigning a different weight size to the dimension vector. At the same time, the position encoding, feed-forward neural networks, dropout components are injected to achieve better performance. Afterwards, the gate recurrent unit (GRU) with residual add was used to dynamically fuse the temporal features obtained from the hierarchical learning. Validation was performed on 48 subjects from the STEW database, with a mean accuracy of 76.81% for cognitive workload classification across subjects. The DHAT's advanced capability and competitiveness is demonstrated by the comparison with all existing approaches.