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Creators/Authors contains: "Chang, C-Y"

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  1. ; ; ; ; (, International IEEE/EMBS Conference on Neural Engineering)
    Electroencephalography (EEG)-based emotion classification has drawn increasing attention yet EEG signals associated with emotional responses are still elusive. This study applies a multi-model adaptive mixture independent component analysis (AMICA) as an unsupervised approach to identify and characterize emotional states. Empirical results showed that the AMICA was able to learn distinct models that accounted for four self-imagery emotions. While large-scale analyses and careful examinations are needed, the pilot study offers evidence for AMICA as a promising, data-driven approach to model EEG dynamics of self-imagery emotions. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; ; ; ; ; et al (, Physical Review D)
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