使用K近邻分类法从多通道脑电图信号中进行情绪识别。
Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.
作者信息
Li Mi, Xu Hongpei, Liu Xingwang, Lu Shengfu
机构信息
Department of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
The Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100024, China.
出版信息
Technol Health Care. 2018;26(S1):509-519. doi: 10.3233/THC-174836.
BACKGROUND
Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals.
OBJECTIVE
This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels.
METHODS
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.
RESULTS
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.
CONCLUSIONS
This paper provided better frequency bands and channels reference for emotion recognition based on EEG.
背景
基于多通道脑电图（EEG）信号的情感识别已开展了许多研究。
目的
本文探讨不同频段和不同通道数量的脑电信号对情感识别准确率的影响。
方法
我们使用不同组合的脑电通道在效价和唤醒度维度上对情感状态进行分类。首先，对DEAP默认预处理数据进行归一化。接下来，使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。
结果
基于伽马频段的10、14、18和32个脑电通道在效价维度上的分类准确率分别为89.54%、92.28%、93.72%和95.70%，在唤醒度维度上分别为89.81%、92.24%、93.69%和95.69%。随着通道数量的增加，情感状态的分类准确率也随之提高，伽马频段的分类准确率大于贝塔频段，其次是阿尔法和西塔频段。
结论
本文为基于脑电的情感识别提供了较好的频段和通道参考。
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