Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.
Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.
复制标题
使用 K 最近邻分类从多通道 EEG 信号中进行情绪识别
DOI：
10.3233/thc-174836
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Lu S
中科院分区：
文献类型：
--
作者：
Li M;Xu H;Liu X;Lu S
基于多通道脑电信号的情感识别已被广泛研究。 本文探讨了不同频段、不同通道数的脑电信号对情绪识别准确率的影响。我们分类的情绪状态的效价和唤醒维度使用不同的组合的EEG通道。首先，对DEAP默认预处理数据进行归一化。然后，利用离散小波变换将脑电信号划分为四个频段，并计算熵和能量作为K-近邻分类器的特征。 基于Gamma频段的10、14、18和32通道脑电的分类准确率在效价维度上分别为89.54%、92.28%、93.72%和95.70%，在唤醒维度上分别为89.81%、92.24%、93.69%和95.69%。随着通道数量的增加，情绪状态的分类准确度也增加，伽马频带的分类准确度大于β频带的分类准确度，随后是α和θ频带。为基于脑电信号的情感识别提供了更好的频段和通道参考。
Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals. This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels. We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier. The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands. This paper provided better frequency bands and channels reference for emotion recognition based on EEG.