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