本文關(guān)鍵詞：情緒識別中EEG信號的特征表示研究　出處：《中央民族大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 情緒識別 EEG 小波變換 SVM 情緒腦區(qū) 電極相似度

【摘要】：情緒識別是人工智能、人機(jī)交互等領(lǐng)域的關(guān)鍵技術(shù)。信息時(shí)代的來臨,要求機(jī)器能夠更友好的理解和表達(dá)人類的情緒。在現(xiàn)實(shí)生活中,情緒識別已經(jīng)應(yīng)用到醫(yī)療、教育、商業(yè)等領(lǐng)域.但是由于情緒是一個(gè)非常復(fù)雜的認(rèn)知過程,情緒識別若想取得較好效果,有賴于不斷深入的研究。腦電信號是一種電生理信號,具有客觀性和精確性,能直接反映大腦的活動(dòng),因而被廣泛應(yīng)用到情緒識別中。本文基于腦電信號,進(jìn)行情緒識別研究,重點(diǎn)在于情緒相關(guān)的腦電特征提取、特征表示問題研究。在本文中,主要開展了三個(gè)方面的工作,分別是：1)情緒誘發(fā)：情緒誘發(fā)是情緒識別研究的關(guān)鍵前提,影響著腦電數(shù)據(jù)的準(zhǔn)確性和可用性。本文采用界內(nèi)普遍認(rèn)可的CAPS (Chinese Affective Picture System,中國情緒圖片庫)和IAPS (International Affective Picture System,國際情緒圖片庫)作為刺激材料,設(shè)計(jì)情緒誘導(dǎo)文件；以同類圖片連續(xù)刺激的方式誘發(fā)被試者的三種情緒,分別是積極、中性和消極情緒。連續(xù)刺激的方式使被試者的情緒體驗(yàn)更加強(qiáng)烈,因而能獲取更好的情緒識別結(jié)果。2)腦電時(shí)頻特征提取：本文利用小波變換,在時(shí)頻域提取了三類腦電特征、分別是子頻帶能量、能量比以及小波系數(shù)的根均方,他們很好的反應(yīng)了情緒相關(guān)的腦電活動(dòng)。SVM平均分類正確率能夠達(dá)到82.87%,表明這三種特征在情緒識別中是非常有效的；與IAPS相比,CAPS刺激采集的EEG數(shù)據(jù)具有更高的情緒識別率,表明情緒存在著文化背景的差異。另外,考慮到腦電信號非線性時(shí)變特性以及情緒的過程性,我們利用了有重疊的方式截取腦電信號樣本。SVM識別的結(jié)果表明了這種截取方式的優(yōu)勢。3)情緒腦區(qū)劃分：越來越多的研究關(guān)注特定腦區(qū)的情緒特征在識別中的重要性。但對腦區(qū)的劃分多是簡單的基于距離和對稱性原則來完成的,忽略了電極間的情緒相關(guān)性和差異性。本文提出了一種基于電極相似度聚類的情緒腦區(qū)劃分方法,以腦區(qū)中心表示區(qū)域內(nèi)所有電極的特征。該方法以電極時(shí)頻特征計(jì)算互相關(guān)度,以相關(guān)度最大且大于閾值的方式判斷電極是否連通,進(jìn)而將連通電極聚為一類,即劃分為一個(gè)腦區(qū)。用聚類中心表示類中所有電極的特征,消除了數(shù)據(jù)冗余,也達(dá)到了降維的效果。與一般的基于距離進(jìn)行腦區(qū)劃分的方法相比,該方法獲得了更高的情緒識別率。
[Abstract]:Emotion recognition is the key technology in artificial intelligence, human-computer interaction and other fields. The advent of the information age requires machines to be able to understand and express human emotions in a more friendly way. In real life, emotion recognition has been applied to medical, educational, commercial and other fields. However, because emotion is a very complex cognitive process, emotion recognition, if we want to achieve better results, depends on continuous in-depth research. Electroencephalogram (EEG) is a kind of electrophysiological signal, which is objective and accurate, and can directly reflect the activity of the brain, so it is widely used in emotion recognition. In this paper, based on EEG, the study of emotion recognition is focused on the study of EEG feature extraction and feature representation. In this paper, there are three main works: 1) emotion induction: emotion induction is the key prerequisite for emotion recognition research, which affects the accuracy and availability of EEG data. The industry generally accepted CAPS (Chinese Affective Picture System, China emotional picture library) and IAPS (International Affective Picture System, the International Affective Picture System) as stimuli, design emotion induced by documents; three kinds of emotion induced by similar images of continuous stimulation mode was that subjects are positive, neutral and negative emotions. The method of continuous stimulation makes the subjects' emotional experience more intense, and thus can obtain better results of emotion recognition. 2) EEG time frequency feature extraction: in this paper, three kinds of EEG characteristics, namely the subband energy, energy ratio and wavelet coefficients of root mean square are extracted from wavelet transform, and they are very good responses to emotional related brain activity. The average classification accuracy of SVM can reach 82.87%, indicating that these three characteristics are very effective in emotion recognition. Compared with IAPS, the EEG data collected by CAPS has higher emotional recognition rate, indicating that there are differences in cultural background between emotions. In addition, considering the nonlinear time-varying characteristics of EEG signals and the process of emotion, we use the overlapping method to intercept the samples of EEG signals. The results of SVM recognition demonstrate the advantage of this interception. 3) emotional brain region division: more and more attention is paid to the importance of emotional characteristics in specific brain areas in recognition. However, the division of the brain area is mostly done based on the principle of distance and symmetry, ignoring the emotional correlation and difference between the electrodes. In this paper, an emotional brain region division method based on electrode similarity clustering is proposed, which represents the characteristics of all electrodes in the region of the brain region. The method calculates the correlation degree based on the time frequency characteristics of electrodes, and determines whether the electrodes are connected by the maximum correlation and the maximum threshold, and then connects the connected electrodes into one class, that is, a brain area. The clustering center is used to denote the characteristics of all the electrodes in the class, and the data redundancy is eliminated, and the effect of reducing the dimension is also achieved. Compared with the general method of dividing the brain based on distance, this method obtains higher emotion recognition rate.
【學(xué)位授予單位】：中央民族大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN911.7

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相關(guān)期刊論文 前1條

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本文編號：1347555