Electroencephalography (EEG) signals are vital physiological indicators widely used for emotion recognition due to their high temporal resolution and direct measurement of brain activity. However, due to considerable inter-individual variability in EEG signal patterns, traditional methods have showed limited effectiveness in cross-subject emotion recognition tasks. Existing Transformer models for EEG emotion recognition often employ hybrid architectures to capture feature dependencies across multiple dimensions, however, these composite structures may result in insufficient information interaction and increased model complexity. To address these limitations, this paper proposes a multi-axis adapter transformer (MAAT) network, which leverages a unified Transformer framework to extract dependencies across frequency, channel, and temporal dimensions without relying on additional model components. Firstly, a multi-axis module is designed to replace the traditional multi-head attention module in the Transformer. This module captures dependencies across frequency, channel, and temporal dimensions, effectively modeling the complex and multi-faceted relationships within EEG signals. Secondly, adapter layers are integrated into the Transformer's feed-forward layers, which facilitate cross-subject transfer learning through fine-tuning. This design enables the model to adapt to new subjects with minimal parameter adjustments, improving generalization in cross-subject emotion recognition tasks. Experimental validations were conducted on the SEED, SEED-IV, and DEAP datasets. The MAAT model achieved high accuracy in both subject-dependent and subject-independent EEG emotion recognition tasks. These results confirm the model's effectiveness, robustness, and generalizability across diverse EEG datasets, outperforming existing methods in cross-subject recognition scenarios.
Wang et al. (Thu,) studied this question.