No abstract

ReliefF Matching Feature Selection (RMFS) is proposed in the paper, which can solve the problem of individual specificity and global threshold mismatch of emotion recognition. Firstly, EEG was decomposed into six emotion-related bands by wavelet packet, then EMD was employed for extracting the 10 categories of features of wavelet coefficient and IMF component of the reconstructed signal; Secondly, the optimization formula of the feature group weight was proposed based on feature sets selected by ReliefF, and it can get the weights of different test features, which were the global optimal matching feature group and the corresponding matching channel, so it can eliminate the redundant information and solve the problem of individual specificity. Finally, SVM was employed to identify the test feature group data to obtain emotional recognition results. The experimental results show that the average correct rates of RMFS for two-category of the valence and the arousal are 93.28% and 93.32%, and the four-categories are higher than 83%. The efficiency of the single subject using RMFS is improved by 42.65%, which is better than the traditional ReliefF algorithm.

The discrete binary particle swarm optimization algorithm proposed in this work can address the difficulty of imprecise emotion-related typical features and localization of emotion recognition. Eighteen classes of features in the time domain, frequency domain and time-frequency domain and differential entropy features are used as features for holistic emotion recognition accuracy using linear and nonlinear methods. Then, the next particle position, velocity and fitness are updated according to the recognition rate accuracy, so as to obtain the optimal feature combination by the emotion recognition accuracy, and lastly the subjects were identified using SVM to get the emotion recognition outcome. The initial experiments reveal that the percentage of correctness was 88.95% and 76.12% for BPSO and PSO, respectively. and the highest correct rate of happy among the four categories of emotions is 91.78%, which is improved by up to 12.83% using the BPSO algorithm.

Mental fatigue (MF) jeopardizes performance and safety through a variety of cognitive impairments and according to the complexity loss theory, should represent “complexity loss” in electroencephalogram (EEG). However, the studies are few and inconsistent concerning the relationship between MF and loss of complexity, probably because of the susceptibility of brain waves to noise. In this study, MF was induced in thirteen male college students by a simulated flight task. Before and at the end of the task, spontaneous EEG and auditory steady-state response (ASSR) were recorded and instantaneous frequency variation (IFV) in alpha rhythm was extracted and analyzed by multiscale entropy (MSE) analysis. The results show that there were significant differences in IFV in alpha rhythm either from spontaneous EEG or from ASSR for all subjects. Therefore, the proposed method can be effective in revealing the complexity loss caused by MF in spontaneous EEG and ASSR, which may serve as a promising analyzing method to mark mild mental impairments.