Abstract
BackgroundA novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI) system. This hybrid system combines a self-paced BCI with an eye-tracker to operate a virtual keyboard. To select a letter, the user must gaze at the target for at least a specific period of time (dwell time) and then activate the BCI by performing a mental task. Unfortunately, electroencephalogram (EEG) signals are often contaminated with artefacts. Artefacts change the quality of EEG signals and subsequently degrade the BCI’s performance.MethodsTo remove artefacts in EEG signals, the proposed algorithm uses the stationary wavelet transform combined with a new adaptive thresholding mechanism. To evaluate the performance of the proposed algorithm and other artefact handling/removal methods, semi-simulated EEG signals (i.e., real EEG signals mixed with simulated artefacts) and real EEG signals obtained from seven participants are used. For real EEG signals, the hybrid BCI system’s performance is evaluated in an online-like manner, i.e., using the continuous data from the last session as in a real-time environment.ResultsWith semi-simulated EEG signals, we show that the proposed algorithm achieves lower signal distortion in both time and frequency domains. With real EEG signals, we demonstrate that for dwell time of 0.0s, the number of false-positives/minute is 2 and the true positive rate (TPR) achieved by the proposed algorithm is 44.7%, which is more than 15.0% higher compared to other state-of-the-art artefact handling methods. As dwell time increases to 1.0s, the TPR increases to 73.1%.ConclusionsThe proposed artefact removal algorithm greatly improves the BCI’s performance. It also has the following advantages: a) it does not require additional electrooculogram/electromyogram channels, long data segments or a large number of EEG channels, b) it allows real-time processing, and c) it reduces signal distortion.
Highlights
A novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI) system
With semi-simulated EEG signals, we show that the proposed algorithm achieves lower signal distortion in both time and frequency domains
Using real EEG signals, we fully investigate and compare the performance of the hybrid BCI system in the following situations: 1) when artefacts are ignored; 2) when EEG segments with artefacts are rejected; and 3) when automatic artefact removal algorithms such as the proposed algorithm and Blind Source Separation (BSS) algorithms are employed
Summary
A novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI) system. The system usually sends an external cue to the user and the user must issue a control command within a window of opportunity provided by the system This limits the use of a Designing a self-paced BCI system with high performance is associated with two major challenges. To remove the noise from a signal using SWT, three steps need to be performed [40]: when applying SWT for artefact removal, two important issues need to be taken into consideration: 1) the thresholding function used to attenuate the wavelet coefficients; and 2) the estimation procedure for obtaining the optimal threshold.
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