EEG dynamics during music appreciation.
This study explores the electroencephalographic (EEG) correlates of emotions during music listening. Principal component analysis (PCA) is used to correlate EEG features with complex music appreciation. This study also applies machine-leaning algorithms to demonstrate the feasibility of classifying EEG dynamics in four subjectively-reported emotional states. The high classification accuracy (81.58+/-3.74%) demonstrates the feasibility of using EEG features to assess emotional states of human subjects. Further, the spatial and spectral patterns of the EEG most relevant to emotions seem reproducible across subjects.
Lin
Yuan-Pin
YP
Department of Electrical Engineering, National Taiwan University, Taiwan. yplin@sccn.ucsd.edu
Jung
Tzyy-Ping
TP
Chen
Jyh-Horng
JH
D001931:Brain Mapping; D004566:Electrodes; D004569:Electroencephalography / Q000295:instrumentation; D004644:Emotions / Q000502:physiology*; D006801:Humans; D009146:Music / Q000523:psychology*; D025341:Principal Component Analysis