A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing (BCI). The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g., electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged Auto-Mutual Information Clustering (LAMIC) and Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.

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Highlights1. We conduct a feasibility study with 14 individuals with cerebral palsy (CP) to evaluate their control of two online Brain-computer interfaces. 2. Eight of the individuals with CP were able to control at least one of the BCIs at a statistically significant level of accuracy. 3. Analysis of the results reveals that BCIs may be controlled by some individuals with CP. Abstract ObjectiveBrain-computer interfaces (BCIs) have been proposed as a potential assistive device for individuals with cerebral palsy (CP) to assist with their communication needs. However, it is unclear how well-suited BCIs are to individuals with CP. Therefore, this study aims to investigate to what extent these users are able to gain control of BCIs. MethodsThis study is conducted with 14 individuals with CP attempting to control two standard online BCIs (1) based upon sensorimotor rhythm modulations, and (2) based upon steady state visual evoked potentials.Email address: reinhold.scherer@tugraz.at (Reinhold Scherer*) Preprint submitted to Clinical NeurophysiologyMarch 27, 2013 ResultsOf the 14 users, 8 are able to use one or other of the BCIs, online, with a statistically significant level of accuracy, without prior training. Classification results are driven by neurophysiological activity and not seen to correlate with occurrences of artifacts. However, many of these users' accuracies, while statistically significant, would require either more training or more advanced methods before practical BCI control would be possible. ConclusionsThe results indicate that BCIs may be controlled by individuals with CP but that many issues need to be overcome before practical application use may be achieved. SignificanceThis is the first study to assess the ability of a large group of different individuals with CP to gain control of an online BCI system. The results indicate that six users could control a sensorimotor rhythm BCI and three a steady state visual evoked potential BCI at statistically significant levels of accuracy (SMR accuracies; mean ± STD, 0.821 ± 0.116, SSVEP accuracies; 0.422 ± 0.069).

Objective. Many brain–computer interfaces (BCIs) use band power (BP) changes in the electroencephalogram to distinguish between different motor imagery (MI) patterns. Most current approaches do not take connectivity of separated brain areas into account. Our objective is to introduce single-trial connectivity features and apply these features to BCI data. Approach. We introduce a procedure for extracting single-trial connectivity estimates from vector autoregressive (VAR) models of independent components in a BCI setting. Main results. In a simulated BCI, we demonstrate that the directed transfer function (DTF) with full-frequency normalization and the direct DTF give classification results similar to BP, while other measures such as the partial directed coherence perform significantly worse. Significance. We show that single-trial MI classification is possible with connectivity measures extracted from VAR models, and that a BCI could potentially utilize such measures.