6533b7d6fe1ef96bd12664ff

RESEARCH PRODUCT

Extract Mismatch Negativity and P3a through Two-Dimensional Nonnegative Decomposition on Time-Frequency Represented Event-Related Potentials

Tapani RistaniemiTiina Huttunen-scottIgor KalyakinAnh Huy PhanAndrzej CichockiHeikki LyytinenFengyu Cong

subject

medicine.diagnostic_testbusiness.industrySpeech recognitionMismatch negativityPattern recognitionElectroencephalographyNon-negative matrix factorizationTime–frequency analysisP3aEvent-related potentialFeature (machine learning)medicineArtificial intelligencebusinessOddball paradigmMathematics

description

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.

yearjournalcountryeditionlanguage
2010-01-01
https://doi.org/10.1007/978-3-642-13318-3_48