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

【摘要】：情感識(shí)別是當(dāng)前研究的一個(gè)熱點(diǎn)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(jí)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(yùn)作機(jī)理有著十分重要的意義。情感識(shí)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(hào)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(shí)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(shí)別的研究,主要工作如下： (1)提出一種基于腦電信號(hào)樣本熵的情感識(shí)別方法,EEG信號(hào)經(jīng)過偽跡去除和濾波處理之后,通過K-S檢驗(yàn)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(jì)基于圖片刺激材料的心理學(xué)實(shí)驗(yàn)范式,并采用實(shí)驗(yàn)室購買的BP (Brain Products)腦電信號(hào)記錄儀系統(tǒng)在該實(shí)驗(yàn)范式基礎(chǔ)上進(jìn)行EEG信號(hào)的采集；然后使用BP系統(tǒng)提供的軟件將采集到的EEG信號(hào)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(hào)；最后在提出理論的基礎(chǔ)上針對提取出的p波段EEG信號(hào)進(jìn)行正負(fù)兩類情感狀態(tài)的識(shí)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(shí)驗(yàn)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(hào)；最后在提出理論的基礎(chǔ)上針對提取出的p波段EEG信號(hào)對被試不同激活度和愉悅度的情感狀態(tài)進(jìn)行識(shí)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(shí)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(shí)別。 總之,本文的結(jié)果充分表明,使用樣本熵作為腦電信號(hào)特征用于情感識(shí)別具有一定的識(shí)別效果,同時(shí)證實(shí)了腦電信號(hào)的p波節(jié)律特征用于情感識(shí)別的可能性,并找出了與情感識(shí)別活動(dòng)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(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7

【共引文獻(xiàn)】

相關(guān)期刊論文 前4條

1 涂紅偉;駱培聰;;暗示理論視角下員工組織公民行為形成機(jī)制的研究[J];福建師范大學(xué)學(xué)報(bào)(哲學(xué)社會(huì)科學(xué)版);2014年02期

2 石京;肖遙;陳志良;;音樂喜好對駕駛行為的影響[J];交通信息與安全;2014年05期

3 李立;曹銳;相潔;;腦電數(shù)據(jù)近似熵與樣本熵特征對比研究[J];計(jì)算機(jī)工程與設(shè)計(jì);2014年03期

4 丁炯;張宏;童勤業(yè);陳琢;;Studies of phase return map and symbolic dynamics in a periodically driven Hodgkin Huxley neuron[J];Chinese Physics B;2014年02期

相關(guān)博士學(xué)位論文 前1條

1 風(fēng)美茵;語言誘導(dǎo)與古琴音樂對原發(fā)性失眠癥患者療效的比較研究[D];中國中醫(yī)科學(xué)院;2013年

相關(guān)碩士學(xué)位論文 前1條

1 閆東濤;腦電對大型短程團(tuán)體心理干預(yù)效果的評價(jià)研究[D];山西醫(yī)科大學(xué);2013年

，

本文編號(hào)：1965315