A self-paced brain–computer interface for controlling a robot simulator: an online event labelling paradigm and an extended Kalman filter based algorithm for online training

Due to the non-stationarity of EEG signals, online training and adaptation are essential to EEG based brain–computer interface (BCI) systems. Self-paced BCIs offer more natural human–machine interaction than synchronous BCIs, but it is a great challenge to train and adapt a self-paced BCI online because the user’s control intention and timing are usually unknown. This paper proposes a novel motor imagery based self-paced BCI paradigm for controlling a simulated robot in a specifically designed environment which is able to provide user’s control intention and timing during online experiments, so that online training and adaptation of the motor imagery based self-paced BCI can be effectively investigated. We demonstrate the usefulness of the proposed paradigm with an extended Kalman filter based method to adapt the BCI classifier parameters, with experimental results of online self-paced BCI training with four subjects.

[1]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[2]  John Q. Gan,et al.  Asynchronous BCI Control of a Robot Simulator with Supervised Online Training , 2007, IDEAL.

[3]  K. Aihara,et al.  An Iterative Algorithm for Spatio-Temporal Filter Optimization , 2006 .

[4]  Karla Felix Navarro,et al.  A Comprehensive Survey of Brain Interface Technology Designs , 2007, Annals of Biomedical Engineering.

[5]  J. Mourino,et al.  Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  G. Birch,et al.  Initial on-line evaluations of the LF-ASD brain-computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[7]  Horst Bischof,et al.  The Self-Paced Graz Brain-Computer Interface: Methods and Applications , 2007, Comput. Intell. Neurosci..

[8]  Bernhard Graimann,et al.  A comparison approach toward finding the best feature and classifier in cue-based BCI , 2007, Medical & Biological Engineering & Computing.

[9]  A. Buttfield,et al.  Towards a robust BCI: error potentials and online learning , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  G. Pfurtscheller,et al.  The BCI competition III: validating alternative approaches to actual BCI problems , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Stephen J. Roberts,et al.  Adaptive BCI based on variational Bayesian Kalman filtering: an empirical evaluation , 2004, IEEE Transactions on Biomedical Engineering.

[12]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[13]  C. Rimnac,et al.  The Effect of Moisture Absorption on the Fatigue Crack Propagation Resistance of Acrylic Bone Cement / Der Einfluβ der Flüssigkeitsabsorption auf die Bruchzähigkeit von Knochenzement , 2004, Biomedizinische Technik. Biomedical engineering.

[14]  M Fatourechi,et al.  A self-paced brain–computer interface system with a low false positive rate , 2008, Journal of neural engineering.

[15]  G. Pfurtscheller,et al.  Continuous EEG classification during motor imagery-simulation of an asynchronous BCI , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[16]  Moritz Grosse-Wentrup,et al.  Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces , 2006, NIPS.

[17]  J. Blumberg,et al.  Adaptive Classification for Brain Computer Interfaces , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  John Q. Gan,et al.  Comparison of three methods for adapting LDA classifiers with BCI applications , 2008 .

[19]  W. D. Penny,et al.  Real-time brain-computer interfacing: A preliminary study using Bayesian learning , 2006, Medical and Biological Engineering and Computing.

[20]  S. J. Roberts,et al.  AN ADAPTIVE , SPARSE-FEEDBACK EEG CLASSIFIER FOR SELF-PACED BCI , 2006 .

[21]  Jdel.R. Millan,et al.  On the need for on-line learning in brain-computer interfaces , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[22]  Gary E. Birch,et al.  A brain-controlled switch for asynchronous control applications , 2000, IEEE Trans. Biomed. Eng..

[23]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[24]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[25]  Bernhard Schölkopf,et al.  Learning Optimal EEG Features Across Time, Frequency and Space , 2006, NIPS 2006.

[26]  K. Müller,et al.  THE BERLIN BRAIN-COMPUTER INTERFACE FOR RAPID RESPONSE , 2004 .

[27]  Reinhold Scherer,et al.  Study of On-Line Adaptive Discriminant Analysis for EEG-Based Brain Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.

[28]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.