【摘要】：本文旨在結合小波分析與經驗模態(tài)分解(EMD),充分提取音樂干預下的腦電(EEG)信號特征參數,提高情緒狀態(tài)評估的分類準確率與可靠性,以期為輔助音樂治療提供支持與幫助。采用音樂誘發(fā)情緒的多通道標準情感數據庫(DEAP)中的數據,利用小波變換提取出額區(qū)(F3,F4)、顳區(qū)(T7,T8)和中央(C3,C4)通道的α波、β波以及θ波節(jié)律;對提取的腦電節(jié)律進行EMD以獲得固有模態(tài)函數(IMF)分量,再進一步提取腦電節(jié)律波的IMF分量平均能量和幅度差特征值,即每種節(jié)律波中包含3個平均能量特征和2個幅度差特征值,以達到充分提取EEG特征信息的目的;最后基于支持向量機分類器實現情感狀態(tài)評估。結果表明,利用該算法可以使無情緒、積極情緒、消極情緒之間分類最優(yōu)正確率達到100%,使得積極與消極情緒之間的識別率提升10%左右,可以實現無情緒與積極、無情緒與消極情緒等情感狀態(tài)的有效評估。處于不同情感狀態(tài)下,音樂治療效果差異較大,提高情感狀態(tài)評估的分類正確率,將幫助提高音樂治療的效果,更好地為音樂治療提供支持。
[Abstract]:In this paper, wavelet analysis and empirical mode decomposition (EMD),) are used to fully extract the characteristic parameters of EEG (EEG) signal under music intervention, and to improve the classification accuracy and reliability of emotional state evaluation, in order to provide support and help for music therapy. The rhythm of 偽 wave, 尾 wave and 胃 wave of frontal region (F3F4), temporal region (T7T8) and central (C3OC4) channel were extracted by wavelet transform from the multichannel standard emotion database (DEAP). The extracted EEG rhythm is extracted by EMD to obtain the (IMF) component of the intrinsic mode function, and the eigenvalues of the average energy and amplitude difference of the IMF component of the EEG rhythm wave are further extracted. That is, each rhythm wave contains three average energy features and two amplitude difference eigenvalues to fully extract EEG feature information. Finally, emotion state evaluation is realized based on support vector machine classifier. The results show that this algorithm can make the optimal classification accuracy of non-emotion, positive emotion and negative emotion up to 100, and the recognition rate between positive and negative emotions can be increased by about 10%. Effective assessment of emotional states such as no emotion and negative emotion. In different emotional states, the effect of music therapy is quite different. Improving the classification accuracy of emotional state evaluation will help to improve the effect of music therapy and provide better support for music therapy.
【作者單位】： 燕山大學電氣工程學院生物醫(yī)學工程研究所;河北省測試計量技術及儀器重點實驗室;北京工業(yè)大學生命科學與生物工程學院;前景光電技術有限公司;
【基金】：河北省自然科學基金資助項目(F2014203244) 中國博士后科學基金資助項目(2014M550582)
【分類號】：R493;TN911.7
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本文編號：2195441