辅助分类器对抗网络以最大的子域差异为基于EEG的情绪识别
Zhaowen Xiao1, Qingshan She2,3, Feng Fang4
1HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou, 310018, China.
Medical & biological engineering & computing
|June 2, 2025
概括
辅助分类器对抗网络 (ACAN) 从脑电图 (EEG) 数据中改善了不受监督的情绪识别. 这种方法有效地减少了域移位,并增强了大脑-计算机接口的模型概括性.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 生物医学工程 生物医学工程
背景情况:
- 域适应 (DA) 对于使用脑电图 (EEG) 数据的无监督情绪识别至关重要,特别是在不同的会议和受试者之间.
- 现有的DA模型在从个体和会话变异性转移到跨域的过程中扎,这限制了概括性.
- 任务特定子域之间的差异往往被忽视,进一步影响模型性能.
研究的目的:
- 建议辅助分类器对抗网络 (ACAN) 来增强来自EEG的无监督情绪识别.
- 通过对齐全球和子域来解决跨领域的转变.
- 为了最大限度地提高子域差异,以提高模型的有效性.
主要方法:
- 在功能空间中实现了一个域对齐模块,以最大限度地减少域间和子域间的差异.
- 引入了一个辅助对抗分类器,通过对抗学习生成可区分的子域特征.
- 在特征提取器和辅助分类器之间使用对抗式学习.
主要成果:
- 在SEED,SEED-IV和DEAP数据库上的跨会话和跨主题实验中,ACAN展示了有效性和优势.
- 拟议的方法在处理领域转移方面优于最先进的DA技术.
- 验证了模型在复杂场景中提高情绪识别准确性的能力.
结论:
- 在基于EEG的情绪识别中,ACAN有效地解决了跨领域的转变.
- 该方法通过对准域和子域,显著提高了模型概括性.
- 这项工作推动了用于情绪识别的强大的脑计算机接口的开发.
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