中圖分類號(hào)： TP301.6 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.222526 中文引用格式： 蔡靖，袁守國(guó)，李銳，等. 基于加權(quán)KNN算法的腦電信號(hào)情緒識(shí)別[J].電子技術(shù)應(yīng)用，2022，48(10)：25-30，35. 英文引用格式： Cai Jing，Yuan Shouguo，Li Rui，et al. Emotion recognition of EEG signals based on weighted KNN algorithm[J]. Application of Electronic Technique，2022，48(10)：25-30，35.
Emotion recognition of EEG signals based on weighted KNN algorithm
Cai Jing，Yuan Shouguo，Li Rui，Xu Menghui
School of Instrument Science and Electrical Engineering，Jilin University，Changchun 130061，China
Abstract： Emotion is closely related to human behavior, family and society. Emotion can not only reflect all kinds of human feelings, thoughts and behaviors, but also the psychological and physiological responses produced by various external stimuli. Therefore, the correct identification of emotion is very important in many fields. The change of emotion will lead to the change of electroencephalogram(EEG) signal. On the contrary, these changes also reflect the change of emotional state. Based on the DEAP database, this paper extracts the time-domain and frequency-domain features of EEG signals, and reduces the dimension of the features by principal component analysis(PCA). The weighted KNN algorithm is used for 5-fold cross validation training. Finally, the recognition accuracy of excited, relaxed, depressed and angry emotions reaches 80%.
Key words : EEG signal；principal component analysis(PCA)；time-domain feature；frequency-domain feature；weighted KNN
