Improving Emotion Analysis for Speech-Induced EEGs Through EEMD-HHT-Based Feature Extraction and Electrode Selection
Improving Emotion Analysis for Speech-Induced EEGs Through EEMD-HHT-Based Feature Extraction and Electrode Selection
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DOI：
10.4018/ijmdem.2021040101
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发表时间：
2021-04-01
影响因子：
0.9
通讯作者：
Bo, Hongjian
中科院分区：
文献类型：
--
作者：
Chen, Jing;Li, Haifeng;Bo, Hongjian
使用脑电图信号进行情绪检测有利于消除社会掩蔽，从而更好地理解潜在的情绪。本文介绍了对情绪言语的认知反应和来自脑电图信号的情绪识别。提出了一种从情绪言语诱发的脑电信号中识别心理状态的框架：首先，设计了言语诱发的情绪认知实验，并收集了脑电数据集。其次，使用EEMD-HHT提取与功率相关的特征，它比STFT和WT更准确地反映信号的瞬时频率。使用 MIC 和统计分析对频段和刺激的情绪注释之间的关系进行了广泛的分析。与脑电图信号最强的相关性存在于外侧和内侧眶额皮层 (OFC)。最后，评估了不同特征集和分类器组合的性能，实验表明，本文提出的框架可以有效地从脑电信号中识别情感，效价准确率为 75.7%，唤醒准确率为 71.4%。
Emotion detection using EEG signals has advantages in eliminating social masking to obtain a better understanding of underlying emotions. This paper presents the cognitive response to emotional speech and emotion recognition from EEG signals. A framework is proposed to recognize mental states from EEG signals induced by emotional speech: First, speech-evoked emotion cognitive experiment is designed, and EEG dataset is collected. Second, power-related features are extracted using EEMD-HHT, which is more accurate to reflect the instantaneous frequency of the signal than STFT and WT. An extensive analysis of relationships between frequency bands and emotional annotation of stimulus are presented using MIC and statistical analysis. The strongest correlations with EEG signals are found in lateral and medial orbitofrontal cortex (OFC). Finally, the performance of different feature set and classifier combinations are evaluated, and the experiments show that the framework proposed in this paper can effectively recognize emotion from EEG signals with accuracy of 75.7% for valence and 71.4% for arousal.