本文選題：腦電　切入點：壓力評估　出處：《燕山大學》2014年碩士論文

【摘要】：長期的壓力狀態(tài)，容易導致各種疾病，情感應(yīng)激狀態(tài)評估是合理和有效的壓力干預(yù)基礎(chǔ)。本文以壓力情感評估為研究目標，充分利用腦電數(shù)據(jù)中蘊含豐富情感信息的特點，結(jié)合復雜度、熵理論以及多重分形方法，實現(xiàn)了壓力情感狀態(tài)的分析評估，，設(shè)計并完成了基于腦電的壓力情感評價系統(tǒng)。 針對壓力情感腦電數(shù)據(jù)特征提取的問題，本文提出了一種基于復雜度與熵相結(jié)合的方法。KC復雜度可以描述腦電數(shù)據(jù)的隨機程度，小波熵和近似熵分別能夠在時頻兩域中對腦電數(shù)據(jù)的復雜程度和能量分布狀況進行量化；采用遺傳算法改進的支持向量機融合以上3種特征參數(shù)，壓力情感狀態(tài)評估得以實現(xiàn)。本文中采集了14名被試總共92組壓力腦電數(shù)據(jù)�；谏鲜鋈N特征融合算法對被試的壓力情感狀態(tài)進行分析評估，最高準確率為94.12%，平均準確率為82.06%。研究同時表明，不同腦區(qū)對壓力敏感程度不同，左半球相對右半球來說，壓力感受敏感。對情感腦電數(shù)據(jù)分析的結(jié)果顯示，本文提出的近似熵、小波熵特征融合方法優(yōu)于傳統(tǒng)的統(tǒng)計學特征，比Sander Koelstra的頻率能量特征方法喚醒度的分類準確率高出7.49%。 腦電的多重分形譜為腦電的非線性分析提供了一個有效方法。根據(jù)信號的奇異性大小分析比較發(fā)現(xiàn)，壓力腦電信號的奇異譜寬度大于無壓力腦電信號的奇異譜寬度，這說明人們在不同壓力狀態(tài)下，腦電的多重分形特性不同，壓力越小，腦電的復雜度趨于減弱。同樣，人類不同情感狀態(tài)下，腦電的多重分形特性也不同，基于多重分形譜特征對情感腦電進行分類，分類結(jié)果較現(xiàn)有研究結(jié)果最多高出28.88%，說明我們對數(shù)據(jù)的有效篩選和特征提取，在情感識別中是可行的。 在Visual Studio2008編譯器環(huán)境下利用C#和MATALB混合編程開發(fā)壓力情感狀態(tài)評估系統(tǒng)。對相關(guān)腦電數(shù)據(jù)進行預(yù)處理、特征提取、分類識別，最后得出被測者的情緒壓力狀態(tài)。
[Abstract]:The assessment of emotional stress state is a reasonable and effective basis for stress intervention. The purpose of this study is to make full use of the characteristics of EEG data containing rich emotional information. Combined with complexity, entropy theory and multifractal method, the analysis and evaluation of stress emotion state are realized, and a stress emotion evaluation system based on EEG is designed and completed. Aiming at the problem of feature extraction of pressure-emotional EEG data, a method based on complexity and entropy is proposed in this paper. KC complexity can describe the random degree of EEG data. Wavelet entropy and approximate entropy can quantify the complexity and energy distribution of EEG data in time-frequency domain respectively. In this paper, we collected a total of 92 groups of stress-EEG data from 14 subjects. Based on the above three feature fusion algorithms, we analyzed and evaluated the pressure-emotional state of the subjects. The highest accuracy rate is 94.12 and the average accuracy is 82.06.The study also shows that different brain regions are sensitive to stress, and the left hemisphere is more sensitive to stress than the right hemisphere. The wavelet entropy feature fusion method is superior to the traditional statistical feature, and the classification accuracy is 7.49% higher than that of Sander Koelstra's frequency energy feature method. Multifractal spectrum of EEG provides an effective method for nonlinear analysis of EEG. According to the analysis of singularity of signals, it is found that the singular spectrum width of pressure-EEG signals is larger than that of pressure-free EEG signals. This shows that the multifractal characteristics of EEG are different in different stress states. The lower the pressure is, the more complexity of EEG tends to weaken. Similarly, the multifractal characteristics of EEG are different in different emotional states of human beings. The classification of affective EEG based on multifractal spectrum features shows that the result of classification is 28.88% higher than that of existing research, which shows that our effective data selection and feature extraction are feasible in emotion recognition. In the environment of Visual Studio2008 compiler, C # and MATALB are used to develop the stress emotional state evaluation system. The related EEG data are preprocessed, feature extracted, classified and identified. Finally, the emotional stress state of the subjects is obtained.
【學位授予單位】：燕山大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN911.7

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