title: Generalizations of the subject-independent feature set for music-induced emotion recognition.
abstract: Electroencephalogram (EEG)-based emotion recognition has been an intensely growing field. Yet, how to achieve acceptable accuracy on a practical system with as fewer electrodes as possible is less concerned. This study evaluates a set of subject-independent features, based on differential power asymmetry of symmetric electrode pairs [1], with emphasis on its applicability to subject variability in music-induced emotion classification problem. Results of this study have evidently validated the feasibility of using subject-independent EEG features to classify four emotional states with acceptable accuracy in second-scale temporal resolution. These features could be generalized across subjects to detect emotion induced by music excerpts not limited to the music database that was used to derive the emotion-specific features.
authors.affiliation: Department of Electrical Engineering, National Taiwan University, Taipei 10617, Taiwan.
authors.forename: Yuan-Pin
authors.initials: YP
authors.lastname: Lin
authors.forename: Jyh-Horng
authors.initials: JH
authors.lastname: Chen
authors.forename: Jeng-Ren
authors.initials: JR
authors.lastname: Duann
authors.forename: Chin-Teng
authors.initials: CT
authors.lastname: Lin
authors.forename: Tzyy-Ping
authors.initials: TP
authors.lastname: Jung
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000503:physiopathology*; D004569:Electroencephalography / Q000379:methods*; D004644:Emotions / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D009146:Music / Q000523:psychology*; D010363:Pattern Recognition, Automated / Q000379:methods*