Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results. Facing the situation that lacks EEG information decoding research, we first analyzed the impact of different EEG information(individual, session, emotion and trial) for emotion recognition by sample space visualization, sample aggregation phenomena quantification, and energy pattern analysis on five public datasets. Based on these phenomena and patterns, we provided the processing methods and interpretable work of various EEG differences. Through the analysis of emotional feature distribution patterns, we found the Individual Emotional Feature Distribution Difference(IEFDD). After analyzing the limitations of traditional modeling approach suffering from IEFDD, the Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably characterize emotional feature distribution patterns, four weight extraction methods were designed, and the optimal was the correction T-test(CT) weight extraction method. Finally, the performance of WCMF was validated on cross-dataset tasks in two kinds of experiments that simulated different practical scenarios, and the results showed that WCMF had more stable and better emotion recognition ability.
翻译:在基于EEG的情感计算领域,跨数据情感识别是一项极具挑战性的任务,受到许多因素的影响,使通用模型产生不令人满意的结果。面对缺乏EEG信息解码研究的情况,我们首先分析了不同EEG信息(个人、会议、情感和试验)的影响,以便通过空间可视化样本、聚合现象样本量化和五个公共数据集的能源模式分析来识别情感。根据这些现象和模式,我们提供了各种EEG差异的处理方法和可解释的工作。通过分析情感特征分布模式,我们发现了个人情感特征分布差异(IEDFD)。在分析了受IEDDF影响的传统模型方法的局限性之后,提出了基于轻视的频道模型矩阵框架(WCMF)。为了合理描述情感特征分布模式,设计了四种重量提取方法,最佳的就是校正测试权重提取方法。最后,在模拟不同实际情景的两种实验中,CWMMF的绩效在交叉数据设置任务上得到了验证,结果显示,CMFM具有更稳定、更清晰的情感能力认识。