BENEFITS OF MULTI-DOMAIN FEATURE OF MISMATCH NEGATIVITY EXTRACTED BY NON-NEGATIVE TENSOR FACTORIZATION FROM EEG COLLECTED BY LOW-DENSITY ARRAY-Reference-Cited by-同舟云学术

BENEFITS OF MULTI-DOMAIN FEATURE OF MISMATCH NEGATIVITY EXTRACTED BY NON-NEGATIVE TENSOR FACTORIZATION FROM EEG COLLECTED BY LOW-DENSITY ARRAY

Published:2012-11-27 Issue:06 Volume:22 Page:1250025
ISSN:0129-0657
Container-title:International Journal of Neural Systems
language:en
Short-container-title:Int. J. Neur. Syst.

Author:

CONG FENGYU¹,PHAN ANH HUY²,ZHAO QIBIN²,HUTTUNEN-SCOTT TIINA³,KAARTINEN JUKKA³,RISTANIEMI TAPANI¹,LYYTINEN HEIKKI³,CICHOCKI ANDRZEJ²

Affiliation:

1. Department of Mathematical Information Technology, University of Jyväskylä, Finland

2. Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Japan

3. Department of Psychology, University of Jyväskylä, Finland

Abstract

Through exploiting temporal, spectral, time-frequency representations, and spatial properties of mismatch negativity (MMN) simultaneously, this study extracts a multi-domain feature of MMN mainly using non-negative tensor factorization. In our experiment, the peak amplitude of MMN between children with reading disability and children with attention deficit was not significantly different, whereas the new feature of MMN significantly discriminated the two groups of children. This is because the feature was derived from multi-domain information with significant reduction of the heterogeneous effect of datasets.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Networks and Communications,General Medicine

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0129065712500256

Reference65 articles.

1. Early selective-attention effect on evoked potential reinterpreted

2. AUTOMATIC IDENTIFICATION OF EPILEPTIC AND BACKGROUND EEG SIGNALS USING FREQUENCY DOMAIN PARAMETERS

3. SYNCHRONIZATION MEASURES OF THE SCALP ELECTROENCEPHALOGRAM CAN DISCRIMINATE HEALTHY FROM ALZHEIMER'S SUBJECTS

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