C2DGCN:使用电脑学信号识别的人类情绪识别的交叉连接分布式学习启用图形卷积网络
Puja Cholke1, Shailaja Uke2, Jyoti Jayesh Chavhan3
1Department of Information Technology, Vishwakarma Institute of Technology, Pune, Maharashtra 411037 India.
Cognitive neurodynamics
|December 29, 2025
概括
这项研究介绍了一种新的交叉连接分布式学习支持的图形卷积网络 (C2DGCN),用于从电脑图 (EEG) 信号中准确识别情绪. C2DGCN有效地降低了复杂性,并增强了特征提取,实现了高精度.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 情绪识别对于人与计算机的互动至关重要.
- 电脑电图 (EEG) 为情绪识别提供了准确的脑活动特征.
- 基于EEG的情绪识别现有的深度学习方法与复杂的特征和过度配合扎.
研究的目的:
- 提出一个有效的交叉连接的分布式学习支持的图形卷积网络 (C2DGCN),用于使用EEG信号增强情绪识别.
- 解决当前深度学习模型中计算复杂性和过度拟合的局限性.
- 为了提高从大脑活动中识别情绪的准确性和效率.
主要方法:
- 开发了一种新的C2DGCN架构,集成交叉连接的分布式学习,以进行广泛的功能共享和集成.
- 使用的统计时间频率信号描述器用于复杂的特征提取和减轻过度装配.
- 验证了SEED-IV和DEAP数据集上的模型,以进行可靠的绩效评估.
主要成果:
- C2DGCN实现了高精度 (97.73%在SEED-IV上,97.66%在DEAP上),灵敏度 (98.32%在SEED-IV上,97.25%在DEAP上),特异性 (98.22%在SEED-IV上,98.07%在DEAP上) 和精度 (98.32%在SEED-IV上,97.98%在DEAP上).
- 与现有方法相比,计算复杂性的显著降低.
- 有效地捕捉了复杂的EEG特征,并减轻了过度拟合问题.
结论:
- 拟议的C2DGCN模型为基于EEG的情绪识别提供了一个计算效率高和高度准确的解决方案.
- 交叉连接的分布式学习和统计时间频率描述器的整合在克服以前方法的局限性方面被证明是有效的.
- 这项研究通过改进脑-计算机接口能力,推动了情感计算领域的发展.
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