An improved P300 extraction using ICA-R for P300-BCI speller

In this study, a new P300 extraction method is investigated by using a form of constrained independent component analysis (cICA) algorithm called one-unit ICA-with-reference (ICA-R) which extracts the P300 signal based on its temporal information. The main advantage of this method compared to the existing ICA-based method is that the desired P300 signal is extracted directly without requiring partial or full signal decomposition and any post-processing on the outcome of the ICA before the P300 signal can be obtained. Since only one IC is extracted, the method is computationally more efficient for real-time P300 BCI applications. In our study, when tested on the BCI competition 2003 dataset IIb, the current state-of-the-art performance is maintained by using the one-unit ICA-R. Besides that, the ability of the method to visualize P300 signals at the single-trial level also suggests it has potential applications in other types of ERP studies.

[1]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[2]  Klaus-Robert Müller,et al.  The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials , 2004, IEEE Transactions on Biomedical Engineering.

[3]  Suogang Wang,et al.  Enhancing Evoked Responses for BCI Through Advanced ICA Techniques , 2006 .

[4]  T. Sejnowski,et al.  Analysis and visualization of single‐trial event‐related potentials , 2001, Human brain mapping.

[5]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[6]  Yael Arbel,et al.  Single trial independent component analysis for P300 BCI system , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Wei Lu,et al.  ICA with Reference , 2006, Neurocomputing.

[8]  G.F. Inbar,et al.  An improved P300-based brain-computer interface , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Christopher J. James,et al.  Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis , 2003, IEEE Transactions on Biomedical Engineering.

[10]  Zhi-Lin Zhang,et al.  Morphologically constrained ICA for extracting weak temporally correlated signals , 2008, Neurocomputing.

[11]  Fusheng Yang,et al.  BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications , 2004, IEEE Transactions on Biomedical Engineering.

[12]  Tae-Seong Kim,et al.  Robust extraction of P300 using constrained ICA for BCI applications , 2012, Medical & Biological Engineering & Computing.