Interactive component extraction from fEEG, fNIRS and peripheral biosignals for affective brain-machine interfacing paradigms

  • Authors:

  • Tomasz M. Rutkowski;Toshihisa Tanaka;Andrzej Cichocki;Donna Erickson;Jianting Cao;Danilo P. Mandic

  • Affiliations:

  • Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi 351-0198, Japan;Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei-shi, Tokyo 184-8588, Japan and Laboratory for Advanced Brain Signal Pr ...;Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi 351-0198, Japan;Showa Music University, 1-11-1 Kamiasao, Asao-ku, Kawasaki-shi, Kanagawa 215-8558, Japan and Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi ...;Saitama Institute of Technology, Saitama 369-0293, Japan and Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi 351-0198, Japan;Communication and Signal Processing Research Group, Department of Electrical and Electronic Engineering, Imperial College, Exhibition Road, London SW7 2BT, United Kingdom

  • Venue:
  • Computers in Human Behavior
  • Year:
  • 2011

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Abstract

This paper investigates whether some well understood principles of human behavioral analysis can be used to design novel paradigms for affective brain-computer/machine interfaces. This is achieved by using the visual, audio, and audiovisual stimuli representing human emotions. The analysis of brain responses to such stimuli involves several challenges related to the conditioning of brain electrical responses, extraction of the responses to stimuli and mutual information between the several physiological recording modalities used. This is achieved in the time-frequency domain, using multichannel empirical mode decomposition (EMD), which proves very accurate in the joint analysis of neurophysiological and peripheral body signals. Our results indicate the usefulness of such an approach and confirm the possibility of using affective brain-computer/machine interfaces.