Robust artifactual independent component classification for BCI practitioners.
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).


METHODS
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.


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.


CONCLUSIONS
Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.
Machine Learning Laboratory, Technical University of Berlin, Marchstr. 23, D-10587 Berlin, Germany.
Irene
I
Winkler
Stephanie
S
Brandl
Franziska
F
Horn
Eric
E
Waldburger
Carsten
C
Allefeld
Michael
M
Tangermann
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D062207:Brain-Computer Interfaces; D003143:Communication Devices for People with Disabilities; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface