Towards Asynchronous Brain-computer Interfaces: A P300-based Approach with Statistical Models

Asynchronous control is a critical issue in developing brain-computer interfaces for real-life applications, where the machine should be able to detect the occurrence of a mental command. In this paper we propose a computational approach for robust asynchronous control using the P300 signal, in a variant of oddball paradigm. First, we use Gaussian models in the support vector margin space to describe various types of EEG signals that are present in an asynchronous P300-based BCI. This allows us to derive a probability measure of control state given EEG observations. Second, we devise a recursive algorithm to detect and locate control states in ongoing EEG. Experimental results indicate that our system allows information transfer at approx. 20 bit/min at low false alarm rate (1/min).

[1]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[2]  F. Rösler,et al.  Event-related potentials during auditory and somatosensory discrimination in sighted and blind human subjects. , 1996, Brain research. Cognitive brain research.

[3]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

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

[5]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[6]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[7]  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.

[8]  Touradj Ebrahimi,et al.  Brain-computer interface in multimedia communication , 2003, IEEE Signal Process. Mag..

[9]  E. Curran,et al.  Learning to control brain activity: A review of the production and control of EEG components for driving brain–computer interface (BCI) systems , 2003, Brain and Cognition.

[10]  William Z Rymer,et al.  Brain-computer interface technology: a review of the Second International Meeting. , 2003, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[12]  Ben H. Jansen,et al.  An exploratory study of factors affecting single trial P300 detection , 2004, IEEE Transactions on Biomedical Engineering.

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

[14]  Cuntai Guan,et al.  P300 Brain-Computer Interface Design for Communication and Control Applications , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[15]  Y. Li,et al.  An Effective BCI Speller Based on Semi-supervised Learning , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  M. Thulasidas,et al.  Robust classification of EEG signal for brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.