【摘要】：1872年,達爾文在《人類和動物的表情》一書中指出情緒是高級進化階段的適應工具,從此人們開始了情緒實驗與理論的研究。經過100多年,到20世紀后期情緒研究蓬勃發(fā)展起來,并與認知、神經科學、腦科學等研究相結合;其研究手段也多種多樣,如腦電(EEG)、功能磁共振成像(fMRI)、功能近紅外成像(fNIRI)等。EEG因其高時間分辨率和簡便易行優(yōu)勢,被廣泛用于情緒研究中。 本文設計了基于國際標準情緒圖片庫(IAPS)的情緒圖片視覺誘發(fā)實驗,被試者觀看各等級的情緒圖片并采集EEG信號。通過對EEG信號進行特征提取與分析,找到與情緒變化相關的EEG特征,并嘗試在EEG特征與情緒等級之間建立對應關系,以期實現基于腦電特征的情緒等級分類識別。文中首先對被試者觀看圖片時的EEG進行功率譜分析,構建其功率譜腦地形圖。由該地形圖可知,情緒圖片視覺誘發(fā)時前額區(qū)域腦電最為活躍。信號的頻譜分析表明EEG能量主要集中在15Hz以下。為找到EEG信號最具情緒可分性的頻段,本文對一些導聯(lián)的EEG進行了可分頻段分析;同時,對EEG信號進行了功率譜熵、相關維數分析,并對AF3、AF4、F3、F4導聯(lián)的EEG特征進行了最小二乘直線擬合,在情緒等級與EEG特征之間建立了對應關系。在模式識別環(huán)節(jié),首先分別使用支持向量機的5-折交叉驗證方法和隱馬爾科夫模型對所提取的腦電信號特征進行了分類識別;然后進行了特征層融合后的模式識別,得到融合特征的分類識別率。 結果顯示,特征信息融合后,本文對情緒圖片等級一、五、八的最高平均識別率達到86.5%。目前已經能夠通過情緒圖片誘發(fā)EEG更客觀的將最消極、中性、最積極這三種情緒狀態(tài)區(qū)分開,下一步將進一步研究將每個等級區(qū)分開的特征提取與分類識別算法。
[Abstract]:In 1872, Darwin pointed out in Human and Animal expressions that emotion is an adaptive tool in the advanced stage of evolution, from which people began to study emotional experiments and theories. After more than 100 years, by the late 20th century, emotional research has flourished, and combined with cognitive, neuroscience, brain science, and so on. EEG, such as EEG, functional magnetic resonance imaging (fMRI), functional near infrared imaging (fNIRI) and so on, are widely used in emotional research because of their high temporal resolution and simplicity. Based on the International Standard emotional Picture Library (IAPS), a visual evoked experiment of emotion picture was designed in this paper. The subjects watched the emotion pictures of different levels and collected EEG signals. Through the feature extraction and analysis of EEG signals, the EEG features related to emotional changes are found, and the corresponding relationship between EEG features and emotion grades is attempted to be established, in order to realize the classification and recognition of emotion grades based on EEG features. In this paper, the EEG of the subjects watching the picture is analyzed by power spectrum analysis, and the brain map of the power spectrum is constructed. According to the topographic map, the frontal area is the most active when the emotional picture is visually induced. Spectrum analysis shows that EEG energy is mainly below 15 Hz. In order to find out the most emotional band of EEG signal, this paper analyzes the frequency band of EEG with some leads, and analyzes the power spectrum entropy and correlation dimension of EEG signal. The EEG features of AF3 / AF4 / F3F4 lead were fitted by least-square straight line fitting, and the corresponding relationship between emotional grade and EEG features was established. In pattern recognition, support vector machine (SVM) 5- fold cross validation method and hidden Markov model are used to classify the extracted EEG features, and then the feature layer fusion pattern recognition is carried out. The classification recognition rate of fusion features is obtained. The results show that after feature information fusion, the highest average recognition rate of emotional image grades 1, 5 and 8 is 86.5%. At present, EEG can be induced by emotional images to more objectively distinguish the three most negative, neutral and active emotional states. The next step will be to further study the feature extraction and classification recognition algorithm.
【學位授予單位】：天津大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：R318.0

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