title: EEG-based emotion estimation using Bayesian weighted-log-posterior function and perceptron convergence algorithm.
abstract: This paper addresses the emotion recognition problem from electroencephalogram signals, in which emotions are represented on the valence and arousal dimensions. Fast Fourier transform analysis is used to extract features and the feature selection based on Pearson correlation coefficient is applied. This paper proposes a probabilistic classifier based on Bayes' theorem and a supervised learning using a perceptron convergence algorithm. To verify the proposed methodology, we use an open database. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the average accuracy of the valence and arousal estimation is 70.9% and 70.1%, respectively. For the three-level class case, the average accuracy is 55.4% and 55.2%, respectively.
authors.affiliation: Faculty of Mechanical and Automotive Engineering, Catholic University of Daegu, Hayang, Gyeongsan-Si, Gyeongbuk 712-702, Republic of Korea.
authors.forename: Hyun Joong
authors.initials: HJ
authors.lastname: Yoon
authors.forename: Seong Youb
authors.initials: SY
authors.lastname: Chung
mesh_terms: D000328:Adult; D000465:Algorithms; D001143:Arousal / Q000502:physiology*; D001499:Bayes Theorem; D016208:Databases, Factual; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D012815:Signal Processing, Computer-Assisted
keywords: Bayes classifier; Electroencephalogram (EEG); Emotion recognition; Perceptron convergence algorithm