本文選題：情感識別 + 腦電信號��； 參考：《太原理工大學(xué)》2014年碩士論文

【摘要】：情感識別是當(dāng)前研究的一個熱點課題,屬于人工智能研究領(lǐng)域。對人的情感和認(rèn)知的研究是人工智能的高級階段,研究人腦是如何處理各種情感狀態(tài),對于探究人腦的運作機(jī)理有著十分重要的意義。情感識別在人們?nèi)粘Ｉ钪衅鸬降淖饔靡苍絹碓街匾?由此產(chǎn)生了很多針對人類情感進(jìn)行研究的方法,其中腦電信號特征提取是研究人類情感的主要手段之一。 基于EEG的情感識別應(yīng)用非常廣,論文在已有研究的基礎(chǔ)上,著重進(jìn)行了情感腦電的特征提取及識別的研究,主要工作如下： (1)提出一種基于腦電信號樣本熵的情感識別方法,EEG信號經(jīng)過偽跡去除和濾波處理之后,通過K-S檢驗篩選樣本熵存在顯著差異的電極,形成情感分類的特征向量,然后利用SVM-Weight算法進(jìn)行分類。 (2)設(shè)計基于圖片刺激材料的心理學(xué)實驗范式,并采用實驗室購買的BP (Brain Products)腦電信號記錄儀系統(tǒng)在該實驗范式基礎(chǔ)上進(jìn)行EEG信號的采集；然后使用BP系統(tǒng)提供的軟件將采集到的EEG信號進(jìn)行預(yù)處理,并提取出預(yù)處理后數(shù)據(jù)的p波段EEG信號；最后在提出理論的基礎(chǔ)上針對提取出的p波段EEG信號進(jìn)行正負(fù)兩類情感狀態(tài)的識別。 (3)篩選Deap網(wǎng)站提供的預(yù)處理后的情感數(shù)據(jù),找出其中行為實驗與被試標(biāo)注一致的視頻；然后提取出預(yù)處理后數(shù)據(jù)的p波段EEG信號；最后在提出理論的基礎(chǔ)上針對提取出的p波段EEG信號對被試不同激活度和愉悅度的情感狀態(tài)進(jìn)行識別。 (4)比較與分析樣本熵與其他三種特征提取方法(近似熵、LZC復(fù)雜度和Hurst指數(shù))的情感識別正確率以及特征提取的效率,結(jié)果說明相較于其他三種特征提取方法,樣本熵更適合于提取腦電特征并進(jìn)行情感識別。 總之,本文的結(jié)果充分表明,使用樣本熵作為腦電信號特征用于情感識別具有一定的識別效果,同時證實了腦電信號的p波節(jié)律特征用于情感識別的可能性,并找出了與情感識別活動相關(guān)的腦區(qū)。期望這種方法能在BCI、智能醫(yī)療護(hù)理系統(tǒng)等應(yīng)用領(lǐng)域中得到很好的應(yīng)用。
[Abstract]:Emotion recognition is a hot topic in current research and belongs to the field of artificial intelligence. The study of human emotion and cognition is the advanced stage of artificial intelligence. It is of great significance to study how the human brain deals with various emotional states. Emotion recognition plays a more and more important role in people's daily life. As a result, there are many methods to study human emotion, among which EEG feature extraction is one of the main methods to study human emotion. Emotion recognition based on EEG is widely used. On the basis of existing research, this paper focuses on the research of feature extraction and recognition of emotional EEG. The main work is as follows: 1) an emotion recognition method based on EEG sample entropy is proposed. After the EEG signal is removed by artifact and filtered, K-S test is used to screen the electrode with significant difference of sample entropy to form the feature vector of emotion classification. Then SVM-Weight algorithm is used to classify. (2) designing the psychological experimental paradigm based on image stimulation material, and adopting the BP brain products EEG recorder system purchased by the laboratory to collect EEG signals on the basis of the experimental paradigm. Then the software provided by BP system is used to pre-process the collected EEG signal and extract the p-band EEG signal of the pre-processed data. Finally, on the basis of the proposed theory, the positive and negative emotional states of the extracted p-band EEG signals are recognized. (3) screening the pre-processed emotional data provided by the Deap website, finding out the video of the behavior experiment consistent with the tagging of the subjects, and then extracting the p-band EEG signal of the pre-processed data. Finally, based on the proposed theory, the emotion states of different activation and pleasure degree were identified for the extracted p-band EEG signals. (4) comparing and analyzing the sample entropy and the other three feature extraction methods (approximate entropy, LZC complexity and Hurst index), the accuracy of emotion recognition and the efficiency of feature extraction are compared. The results show that compared with the other three feature extraction methods, Sample entropy is more suitable for EEG feature extraction and emotion recognition. In a word, the results of this paper fully show that the use of sample entropy as EEG signal features has a certain effect in emotion recognition, and the possibility of using p-wave rhythm feature of EEG signal in emotion recognition is also confirmed. The brain regions associated with emotional recognition activities were also identified. It is expected that this method can be applied in BCI, intelligent medical care system and other fields.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7

【共引文獻(xiàn)】

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本文編號：1965315