EEG-Based Classification of Music Appraisal Responses Using Time-Frequency Analysis and Familiarity Ratings
A time-windowing feature extraction approach based on time-frequency (TF) analysis is adopted here to investigate the time-course of the discrimination between musical appraisal electroencephalogram (EEG) responses, under the parameter of familiarity. An EEG data set, formed by the responses of nine subjects during music listening, along with self-reported ratings of liking and familiarity, is used. Features are extracted from the beta (13-30 Hz) and gamma (30-49 Hz) EEG bands in time windows of various lengths, by employing three TF distributions (spectrogram, Hilbert-Huang spectrum, and Zhao-Atlas-Marks transform). Subsequently, two classifiers (k-NN and SVM) are used to classify feature vectors in two categories, i.e., "like" and "dislike," under three cases of familiarity, i.e., regardless of familiarity (LD), familiar music (LDF), and unfamiliar music (LDUF). Key findings show that best classification accuracy (CA) is higher and it is achieved earlier in the LDF case {91.02 +/- 1.45% (7.5-10.5 s)} as compared to the LDUF case {87.10 +/- 1.84% (10-15 s)}. Additionally, best CAs in LDF and LDUF cases are higher as compared to the general LD case {85.28 +/- 0.77%}. The latter results, along with neurophysiological correlates, are further discussed in the context of the existing literature on the time-course of music-induced affective responses and the role of familiarity.
eeg music familiarity appraisal classification pattern recognition signal processing