情緒圖片視覺誘發(fā)EEG特征提取與分析
[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(xué)位授予單位】:天津大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2012
【分類號】:R318.0
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