title: Automatic artefact removal in a self-paced hybrid brain- computer interface system.
abstract: BACKGROUND
  A 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.
  
  
  METHODS
  To 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.
  
  
  RESULTS
  With 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%.
  
  
  CONCLUSIONS
  The 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.
authors: Department of Electrical and Computer Engineering, University of British Columbia, 2356 Main Mall, Vancouver, V6T1Z4 Canada. yongy@ece.ubc.ca
authors: Xinyi
authors: X
authors: Yong
authors: Mehrdad
authors: M
authors: Fatourechi
authors: Rabab K
authors: RK
authors: Ward
authors: Gary E
authors: GE
authors: Birch
mesh_terms: D000465:Algorithms; D016477:Artifacts; D062207:Brain-Computer Interfaces; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods; D004576:Electromyography; D004585:Electrooculography; D004867:Equipment Design; D005133:Eye Movements / Q000502:physiology; D005260:Female; D006801:Humans; D008297:Male; D012044:Regression Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface; D058067:Wavelet Analysis; D055815:Young Adult