DAVG-ViT:一个具有GA-BiLSTM的域适应视觉变换器,用于EEG情绪识别
Wenjuan Gu1, Jing Li1, Junxiang Peng1
1Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, China.
Computer methods in biomechanics and biomedical engineering
|December 24, 2025
概括
这项研究介绍了DAVG-ViT,这是EEG情绪识别的新型模型. 它通过增强全球建模和捕捉跨道时间动态来实现高精度,改善域泛化.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 基于脑电图 (EEG) 的情绪识别是不断发展的领域,因为它具有非侵入性和高时间分辨率.
- 现有的模型在全球依赖性建模,跨道交互和域泛化方面面临挑战.
研究的目的:
- 提出一个新的模型,DAVG-ViT,用于改进基于EEG的情绪识别.
- 解决全球建模,跨道关系和域名适应方面的局限性.
主要方法:
- 拟议的DAVG-ViT模型整合了用于增强全球建模的适配器.
- 集团注意力和BiLSTM被用来捕捉跨道关系和双向时间动态.
- 使用梯度逆转层的对抗性域调整可以最大限度地减少分布转移.
主要成果:
- 在基准数据集上,DAVG-ViT实现了卓越的性能:在SEED上准确率为99.37%,在SEED-IV.上准确率为95.91%.
- 在DEAP数据集上,获得了98.95%的唤起和98.47%的价值的准确性.
结论:
- DAVG-ViT模型在EEG情绪识别方面取得了重大进展.
- 提出的方法有效地解决了全球建模,跨道交互和域泛化的挑战.
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