The validity and reasonableness of emotional data are the key issues in the cognitive affective computing research. Effects of the emotion recognition are decided by the quality of selected data directly. Therefore, it is an important part of affective computing research to build affective computing database with good performance, so that it is the hot spot of research in this field. In this paper, the performance of two classical cognitive affective computing databases, the Massachusetts Institute of Technology (MIT) cognitive affective computing database and Germany Augsburg University emotion recognition database were compared, their data structure and data types were compared respectively, and emotional recognition effect based on the data were studied comparatively. The results indicated that the analysis based on the physical parameters could get the effective emotional recognition, and would be a feasible method of pressure emotional evaluation. Because of the lack of stress emotional evaluation data based on the physiological parameters domestically, there is not a public stress emotional database. We hereby built a dataset for the stress evaluation towards the high stress group in colleges, candidates of postgraduates of Ph.D and master as the subjects. We then acquired their physiological parameters, and performed the pressure analysis based on this database. The results indicated that this dataset had a certain reference value for the stress evaluation, and we hope this research can provide a reference and support for emotion evaluation and analysis.
The purpose of using brain-computer interface (BCI) is to build a bridge between brain and computer for the disable persons, in order to help them to communicate with the outside world. Electroencephalography (EEG) has low signal to noise ratio (SNR), and there exist some problems in the traditional methods for the feature extraction of EEG, such as low classification accuracy, lack of spatial information and huge amounts of features. To solve these problems, we proposed a new method based on time domain, frequency domain and space domain. In this study, independent component analysis (ICA) and wavelet transform were used to extract the temporal, spectral and spatial features from the original EEG signals, and then the extracted features were classified with the method combined support vector machine (SVM) with genetic algorithm (GA). The proposed method displayed a better classification performance, and made the mean accuracy of the Graz datasets in the BCI Competitions of 2003 reach 96%. The classification results showed that the proposed method with the three domains could effectively overcome the drawbacks of the traditional methods based solely on time-frequency domain when the EEG signals were used to describe the characteristics of the brain electrical signals.