Multi-domain feature of event-related potential extracted by nonnegative tensor factorization: 5 vs. 14 electrodes EEG data

Authors:
Fengyu Cong;Anh Huy Phan;Piia Astikainen;Qibin Zhao;Jari K. Hietanen;Tapani Ristaniemi;Andrzej Cichocki
Affiliations:
Department of Mathematical Information Technology, University of Jyväskylä, Finland;Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Japan;Department of Psychology, University of Jyväskylä, Finland;Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Japan;Human Information Processing Laboratory, School of Social Science and Humanities, University of Tampere, Finland;Department of Mathematical Information Technology, University of Jyväskylä, Finland;Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Japan
Venue:
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Year:
2012

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Abstract

As nonnegative tensor factorization (NTF) is particularly useful for the problem of underdetermined linear transform model, we performed NTF on the EEG data recorded from 14 electrodes to extract the multi-domain feature of N170 which is a visual event-related potential (ERP), as well as 5 typical electrodes in occipital-temporal sites for N170 and in frontal-central sites for vertex positive potential (VPP) which is the counterpart of N170, respectively. We found that the multi-domain feature of N170 from 5 electrodes was very similar to that from 14 electrodes and more discriminative for different groups of participants than that of VPP from 5 electrodes. Hence, we conclude that when the data of typical electrodes for an ERP are decomposed by NTF, the estimated multi-domain feature of this ERP keeps identical to its counterpart extracted from the data of all electrodes used in one ERP experiment.