DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis

• URL: http://arxiv.org/abs/2407.20519v1
• Date: Tue, 30 Jul 2024 03:31:03 GMT
• Title: DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis
• Authors: Yue Pan, Qile Liu, Qing Liu, Li Zhang, Gan Huang, Xin Chen, Fali Li, Peng Xu, Zhen Liang,
• Abstract summary: We propose a Dual Attentive (DuA) transformer framework for long-term continuous EEG emotion analysis. Unlike segment-based approaches, the DuA transformer processes an entire EEG trial as a whole, identifying emotions at the trial level. This framework is designed to adapt to varying signal lengths, providing a substantial advantage over traditional methods.
• Score: 15.858955204180907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Affective brain-computer interfaces (aBCIs) are increasingly recognized for their potential in monitoring and interpreting emotional states through electroencephalography (EEG) signals. Current EEG-based emotion recognition methods perform well with short segments of EEG data. However, these methods encounter significant challenges in real-life scenarios where emotional states evolve over extended periods. To address this issue, we propose a Dual Attentive (DuA) transformer framework for long-term continuous EEG emotion analysis. Unlike segment-based approaches, the DuA transformer processes an entire EEG trial as a whole, identifying emotions at the trial level, referred to as trial-based emotion analysis. This framework is designed to adapt to varying signal lengths, providing a substantial advantage over traditional methods. The DuA transformer incorporates three key modules: the spatial-spectral network module, the temporal network module, and the transfer learning module. The spatial-spectral network module simultaneously captures spatial and spectral information from EEG signals, while the temporal network module detects temporal dependencies within long-term EEG data. The transfer learning module enhances the model's adaptability across different subjects and conditions. We extensively evaluate the DuA transformer using a self-constructed long-term EEG emotion database, along with two benchmark EEG emotion databases. On the basis of the trial-based leave-one-subject-out cross-subject cross-validation protocol, our experimental results demonstrate that the proposed DuA transformer significantly outperforms existing methods in long-term continuous EEG emotion analysis, with an average enhancement of 5.28%.

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