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Construction and cross-correlation analysis of the affective physiological response database

Science china(information Sciences) 2010年第9期53卷 1774-1784页

作者： wen wanhui1,qiu yuhui2,liu guangyuan1**,cheng nanpu3 & huang xiting4 1school of electronic and information engineering,southwest university,chongqing 400715,china 2College of Computer and information Science,southwest university,chongqing 400715,china 3school of Materials Science and engineering,southwest university,chongqing 400715,china 4school of Psychology,southwest university,chongqing 400715,china 1. school of electronic and information engineering Southwest University Chongqing 400715 China2. College of Computer and Information Science Southwest University Chongqing 400715 China3. School of Materials Science and Engineering Southwest University Chongqing 400715 China4. School of Psychology Southwest University Chongqing 400715 China

Six kinds of emotions,namely joy,surprise,disgust,grief,anger and fear,are elicited from 300 ordinary college students by means of high-rated movie *** signals of galvanic skin response(GSR),heart rate(HR),blood volume pulse(BVP),electrocardiogram(ECG),respiration(Rsp.),facial electromyography(EMG) and two channels of electroencephalography(EEG) from the frontal lobe were recorded while the subjects were watching the affective eliciting *** samples of each kind of signals are analyzed by using random matrix theory(RMT).From the distribution of the eigenvalues and eigenvectors of the correlation matrices of BVP,facial EMG and two EEGs,we find that the statistical properties of these four kinds of signals coincide with the RMT ***,the largest eigenvalue and the corresponding eigenvector component distribution of the correlation matrices of GSR,HR,ECG and *** deviated significantly from the RMT *** experiments also reveal that the correlations between two arbitrary samples of BVP,facial EMG or EEGs does not result from affective response but from random noises,whereas the amplitude variations of GSR,HR,ECG and *** the correlated affective physiological response *** to the data analysis results of RMT,110 features are firstly extracted from the GSR and HR signals,and then a subset of the initial features is selected by using the Backward Selection *** affective data samples each of which is a vector of the features are classified by using a Fisher classifier,and furthermore,the user-independent emotion recognition systems with good prediction performance are constructed during the model selection process.

关键词： affective computing emotion recognition affective physiological response database correlation analysis random matrix theory

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