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On utilizing uncertainty information in template‐based EEG‐fMRI ballistocardiogram artifact removal
Author(s) -
Schulz MarcAndre,
Regenbogen Christina,
Moessnang Carolin,
Neuner Irene,
Finkelmeyer Andreas,
Habel Ute,
Kellermann Thilo
Publication year - 2015
Publication title -
psychophysiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.661
H-Index - 156
eISSN - 1469-8986
pISSN - 0048-5772
DOI - 10.1111/psyp.12406
Subject(s) - artifact (error) , electroencephalography , weighting , psychology , signal (programming language) , speech recognition , mismatch negativity , pattern recognition (psychology) , noise (video) , oddball paradigm , artificial intelligence , audiology , computer science , cognitive psychology , neuroscience , event related potential , image (mathematics) , acoustics , medicine , physics , programming language
Abstract The correction of ballistocardiogram artifacts in simultaneous EEG‐fMRI often yields unsatisfactory results. To improve the signal‐to‐noise ratio (SNR) of results, we inferred EEG signal uncertainty from postcorrection artifact residuals and computed the uncertainty‐weighted mean of ERPs. Using an uncertainty‐weighted mean significantly and consistently reduced both inter‐ and intrasubject SEM in the analysis of auditory evoked responses (AER, indicated by the N1‐P2 complex) and in the effects of an auditory oddball paradigm (N1‐P3 complex, standard‐deviant difference). SNR increased by 3% on average for the AER amplitude (intrasubject) and 17% on average for the auditory oddball ERP (intersubject). This demonstrates that weighting by uncertainty complements existing artifact correction algorithms to increase SNR in ERPs. More specifically, it is an efficient method to utilize seemingly corrupt (difficult‐to‐correct) EEG data that might otherwise be discarded.