Extract mismatch negativity and p3a through two-dimensional nonnegative decomposition on time-frequency represented event-related potentials

Authors:
Fengyu Cong;Igor Kalyakin;Anh-Huy Phan;Andrzej Cichocki;Tiina Huttunen-Scott;Heikki Lyytinen;Tapani Ristaniemi
Affiliations:
Department of Mathematical Information Technology, University of Jyväskylä, Finland;Department of Mathematical Information Technology, University of Jyväskylä, Finland;Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Japan;Laboratory for Advanced Brain Signal Processing, Brain Science Institute, RIKEN, Japan;Department of Psychology, University of Jyväskylä, Finland;Department of Psychology, University of Jyväskylä, Finland;Department of Mathematical Information Technology, University of Jyväskylä, Finland
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Year:
2010

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Abstract

This study compares the row-wise unfolding nonnegative tensor factorization (NTF) and the standard nonnegative matrix factorization (NMF) in extracting time-frequency represented event-related potentials—mismatch negativity (MMN) and P3a from EEG under the two-dimensional decomposition The criterion to judge performance of NMF and NTF is based on psychology knowledge of MMN and P3a MMN is elicited by an oddball paradigm and may be proportionally modulated by the attention So, participants are usually instructed to ignore the stimuli However the deviant stimulus inevitably attracts some attention of the participant towards the stimuli Thus, P3a often follows MMN As a result, if P3a was larger, it could mean that more attention would be attracted by the deviant stimulus, and then MMN could be enlarged The MMN and P3a extracted by the row-wise unfolding NTF revealed this coupling feature However, through the standard NMF or the raw data, such characteristic was not evidently observed.