A systematic independent component analysis approach to extract mismatch negativity

  • Authors:

  • Fengyu Cong;Aleksandr Aleksandrov;Veronika Knyazeva;Tatyana Deinekina;Tapani Ristaniemi

  • Affiliations:

  • Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland;Department of Higher Neural Activity and Psychophysiology, Saint Petersburg State University, Saint Petersburg, Russia Federation;Department of Higher Neural Activity and Psychophysiology, Saint Petersburg State University, Saint Petersburg, Russia Federation;Department of Higher Neural Activity and Psychophysiology, Saint Petersburg State University, Saint Petersburg, Russia Federation;Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland

  • Venue:
  • ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
  • Year:
  • 2012

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Abstract

This study systematically addressed how independent component analysis (ICA) can extract mismatch negativity (MMN) from EEG data elicited by a conventional oddball paradigm. MMN is usually observed from difference wave (DW) which is produced by subtracting the responses of the repeated standard stimuli from those of the deviant stimuli. This study performed ICA on the DW and the responses of standard and deviant stimuli individually. Results showed that ERPs with the latency around 120 ms in the estimated responses of the standard and the deviant stimuli were different, and the MMN extracted by ICA from the DW and that observed from the new DW between the estimated responses of the deviant and standard stimuli by ICA were not significantly different particularly at the frontal electrodes. We draw the conclusion that when ICA is used to extract the MMN of adults, there might be no difference to perform DW before or after ICA.