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Main Title: Robust artifactual independent component classification for BCI practitioners
Author(s): Winkler, Irene
Brandl, Stephanie
Horn, Franziska
Waldburger, Eric
Allefeld, Carsten
Tangermann, Michael
Type: Article
Language Code: en
Abstract: Objective. EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brain–computer interfaces (BCIs). Approach. Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms. Main results. We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel strategy improves artifact classification. Addressing (2), ICA artifact cleaning has little influence on average BCI performance when analyzed by state-of-the-art BCI methods. When slow motor-related features are exploited, performance varies strongly between individuals, as artifacts may obstruct relevant neural activity or are inadvertently used for BCI control. Significance. Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.
Issue Date: 19-May-2014
Date Available: 6-Apr-2020
DDC Class: 006 Spezielle Computerverfahren
610 Medizin und Gesundheit
Subject(s): EEG
artifact removal
independent component analysis
blind source separation
brain–computer interface
Sponsor/Funder: EC/FP7/224631/EU/Tools for Brain-Computer Interaction/TOBI
BMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, Zentrum
DFG, 194657344, EXC 1086: BrainLinks-BrainTools
Journal Title: Journal of Neural Engineering
Publisher: Institute of Physics Publishing (IOP)
Publisher Place: Bristol
Volume: 11
Issue: 3
Article Number: 035013
Publisher DOI: 10.1088/1741-2560/11/3/035013
EISSN: 1741-2552
ISSN: 1741-2560
Appears in Collections:FG Maschinelles Lernen » Publications

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