Real-time ocular artifacts suppression from EEG signals using an unsupervised adaptive blind source separation.
Independent component analysis (ICA) has been shown to be a powerful tool for artifactual suppression from electroencephalogram (EEG) recordings. However, the real-time application of this method for artifact rejection has not been considered so far. This article presents a method based on an unsupervised, self-normalizing, adaptive learning algorithm for on-line blind source separation. Simulation results are provided to show the validity and effectiveness of the technique with different distributions. The results from real-data demonstrate that the proposed scheme removes perfectly eye blink and eye movement artifacts from the EEG signals and is suitable for use during on-line EEG monitoring such as EEG-based brain computer interface.
Shayegh
Farzaneh
F
Department of Biomedical Engineering, Faculty of Electrical Engineering, Iran University of Science & Technology, Tehran, Iran.
Erfanian
Abbas
A
D000465:Algorithms; D016477:Artifacts; D001767:Blinking; D001931:Brain Mapping; D003198:Computer Simulation; D003201:Computers; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods*; D004585:Electrooculography / Q000295:instrumentation* / Q000379:methods*; D004867:Equipment Design; D005133:Eye Movements; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic; D013997:Time Factors; D014785:Vision, Ocular