基于IWOA-ELM算法的脑电情绪识别方法研究

Research on emotion recognition method based on IWOA-ELM algorithm for electroencephalogram

情绪是人类重要的生理属性,情绪识别技术可以更好地辅助人类进行自我认识。本文针对不同受试者之间的脑电信号(EEG)存在巨大差异的难点,在传统鲸鱼优化算法(WOA)中引入新机制,加速算法的优化和收敛。同时,将改进的鲸鱼优化算法(IWOA)用于搜索极限学习机模型(ELM)中的最佳训练方案,包括最佳特征集、训练参数以及脑电通道。纳入24种常见的EEG情绪特征进行测试,发现不同受试者最佳脑电情绪特征之间存在一定特异性,同时也具有共性。本文所提方法在脑电情绪识别中获得92.19%的平均识别准确率,显著减少了手动调试模型的工作量,且具有更高的识别精度和更短的训练时间,相较于对照方法具备更优越的性能,为情绪脑电信号解码研究提供了新的思路。

Emotion is a crucial physiological attribute in humans,and emotion recognition technology can significantly assist individuals in self-awareness.Addressing the challenge of significant differences in electroencephalogram(EEG)signals among different subjects,we introduce a novel mechanism in the traditional whale optimization algorithm(WOA)to expedite the optimization and convergence of the algorithm.Furthermore,the improved whale optimization algorithm(IWOA)was applied to search for the optimal training solution in the extreme learning machine(ELM)model,encompassing the best feature set,training parameters,and EEG channels.By testing 24 common EEG emotion features,we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects.The proposed method achieved an average recognition accuracy of 92.19%in EEG emotion recognition,significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method.It outperformed existing methods,providing a superior performance and introducing a novel perspective for decoding EEG signals,thereby contributing to the field of emotion research from EEG signal.