【摘要】：音樂誘發(fā)下的情感狀態(tài)評(píng)估結(jié)果可為輔助音樂治療提供理論支持與幫助。情感狀態(tài)評(píng)估的關(guān)鍵是情感腦電的特征提取,故本文針對(duì)情感腦電特征提取算法的性能優(yōu)化問題開展研究。采用Koelstra等提出的分析人類情緒狀態(tài)的多模態(tài)標(biāo)準(zhǔn)數(shù)據(jù)庫DEAP,提取8種正負(fù)情緒代表各個(gè)腦區(qū)的14個(gè)通道腦電數(shù)據(jù),基于小波分解重構(gòu)δ、θ、α、β四種節(jié)律波;在分析比較小波特征(小波系數(shù)能量和小波熵)、近似熵和Hurst指數(shù)三種腦電特征情感識(shí)別效果的基礎(chǔ)上,提出一種基于主成分分析(PCA)融合小波特征、近似熵和Hurst指數(shù)的腦電特征提取算法。本算法保留累積貢獻(xiàn)率大于85%的主成分,并選擇特征根差異較大的特征參數(shù),基于支持向量機(jī)實(shí)現(xiàn)情感狀態(tài)評(píng)估。結(jié)果表明,使用單一小波特征(小波系數(shù)能量和小波熵)、近似熵和Hurst指數(shù)特征量,情感識(shí)別的正確率均值分別是73.15%、50.00%和45.54%,而改進(jìn)算法識(shí)別準(zhǔn)確率均值在85%左右。基于改進(jìn)算法情感識(shí)別的分類準(zhǔn)確率比傳統(tǒng)方法至少能提升12%,可為情感腦電特征提取以及輔助音樂治療提供幫助。
[Abstract]:The results of music-induced emotional state evaluation can provide theoretical support and help for music therapy. The key to emotional state evaluation is the feature extraction of emotional EEG, so this paper focuses on the performance optimization of EEEG feature extraction algorithm. The multimodal standard database DEAP, proposed by Koelstra et al is used to extract 14 channel EEG data from 8 positive and negative emotions representing various brain regions, and to reconstruct 未, 胃, 偽 and 尾 rhythms based on wavelet decomposition. On the basis of analyzing and comparing the emotional recognition effects of wavelet features (wavelet coefficient energy and wavelet entropy), approximate entropy and Hurst exponent, a (PCA) fusion wavelet feature based on principal component analysis (PCA) is proposed. EEG feature extraction algorithm based on approximate entropy and Hurst exponent. In this algorithm, the principal components whose cumulative contribution rate is more than 85% are retained, and the feature parameters with large differences in feature roots are selected to realize emotional state evaluation based on support vector machine. The results show that using single wavelet feature (wavelet coefficient energy and wavelet entropy), approximate entropy and Hurst exponent feature, the average accuracy of emotion recognition is 73.1550.00% and 45.54g, respectively. The average recognition accuracy of the improved algorithm is about 85%. The classification accuracy of emotion recognition based on the improved algorithm is at least 12% higher than that of the traditional method, which can help to extract emotional EEG features and assist music therapy.
【作者單位】： 燕山大學(xué)電氣工程學(xué)院生物醫(yī)學(xué)工程研究所;河北省測(cè)試計(jì)量技術(shù)及儀器重點(diǎn)實(shí)驗(yàn)室;北京工業(yè)大學(xué)生命科學(xué)與生物工程學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(51677162) 河北省自然科學(xué)基金項(xiàng)目(F2014203244) 中國博士后科學(xué)基金項(xiàng)目(2014M550582)
【分類號(hào)】：R318;TN911.7
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本文編號(hào)：2344975