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Emotion classification from electroencephalographic (EEG) data has been a growing research topic in these years as its importance in potential applications such as affective brain-computer interactions and clinical assessment of mental illness. Many previous related works mainly focused on time-frequency features from the scalp channels, i. e. channel-based features, to obtain better classification accuracy. However, the far-field potentials creased by the brain activity in any area project widely across the scalp surface by passive volume conduction. Therefore, scalp EEG signals are the sum of activities from brain and non-brain sources, confounding the scalp-level analysis. This study applies independent component analysis (ICA) to broadband spectral time series of scalp EEG to extract the brain sources that modulate the spectral contents of brain sources involved in emotional responses during music appreciation. Compared to the principal component analysis (PCA), the emotion-related independent spectral components provide informative features which yield a remarkably high emotion-classification performance and seem more neurophysiologically plausible in terms of dipolarity.
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