Journal
JOURNAL OF NEURAL ENGINEERING
Volume 11, Issue 3, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1741-2560/11/3/035013
Keywords
EEG; artifact removal; independent component analysis (ICA); blind source separation (BSS); brain-computer interface (BCI)
Categories
Funding
- European ICT Programme [FP7-224631 TOBI]
- German Federal Ministry for Education and Research (BMBF) [01GQ0850]
- Federal State of Berlin
- BrainLinks-BrainTools Cluster of Excellence (DFG) [EXC 1086]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available