Akademik Veri Yönetim Sistemi
IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health Engineering, Hong Kong and Shenzhen, Çin, 2 - 07 Ocak 2012, ss.224-227, (Tam Metin Bildiri)
This paper addresses the problem of emotion primitives estimation using information obtained from EEG signals. The EEG data were collected from 18 subjects, 9 male and 9 female, aged from 19 to 26 years old. We used audio clips from International Affective Digital Sounds (IADS) as stimuli for emotion elicitation. Hilbert-Huang Transform, a proper method for non-linear and non-stationary signal processing, was used for feature extraction. EEG signals were first decomposed into their Intrinsic Mode Functions (IMFs). Then 990 features were computed from the first five IMFs. To identify the most salient features and eliminate the redundant and irrelevant ones, we performed correlation based feature selection (CFS). This feature selection process reduced the number of features dramatically while increasing the performance remarkably. In this work, we used support vector regression for estimation of each emotion primitive value. Regression mean absolute error values and their standard deviations over all subjects for valence, activation, and dominance were obtained as 1.11 (0.13), 0.65 (0.09) and 0.38 (0.06) respectively. © 2012 IEEE.