Wavelet-based emotion recognition system using EEG signal
Wavelet-based emotion recognition system using EEG signal
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DOI：
10.1007/s00521-015-2149-8
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发表时间：
2017-08-01
影响因子：
6
通讯作者：
Amiri, Mahmood
中科院分区：
文献类型：
--
作者：
Mohammadi, Zeynab;Frounchi, Javad;Amiri, Mahmood
在这项研究中，情绪状态的唤醒/效价维度已被分类使用的最小数量的通道和频段的EEG信号。利用离散小波变换，脑电信号被分解到相应的频带，然后提取几个特征。支持向量机和K-近邻分类器已被用来从提取的特征中检测情绪状态。对10通道脑电信号的分类结果表明，唤醒水平的分类准确率为86.75%，效价水平的分类准确率为84.05%。此外，与使用EEG信号的低频带相比，使用高频带，特别是伽马带，产生更高的准确性。所有这些都为开发一个实时的情感分类系统提供了支持。
In this research, emotional states in arousal/valence dimensions have been classified using minimum number of channels and frequency bands of EEG signal. Using the discrete wavelet transforms, EEG signals have been decomposed to corresponding frequency bands and then several features have been extracted. The support vector machine and K-nearest neighbor classifiers have been used to detect the emotional states from the extracted features. For the recorded 10-channel EEG signal, results illustrate the classification accuracy of 86.75 % for arousal level and 84.05 % for valence level. Moreover, using the high-frequency bands, specifically gamma band, yields higher accuracy compared to using low-frequency bands of EEG signal. All of these support to the development of a real-time emotion classification system.