DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis
<|reference_start|>DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis: 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%.<|reference_end|>
@article{pan2024dua:,
    title={DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion
  Analysis},
    author={Yue Pan, Qile Liu, Qing Liu, Li Zhang, Gan Huang, Xin Chen, Fali Li,
  Peng Xu, Zhen Liang},
    journal={arXiv preprint arXiv:2407.20519},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20519},
    primaryClass={cs.HC cs.AI}
}