Toward a model-based predictive controller design in brain-computer interfaces

• Published in: Annals of biomedical engineering 2011-05, Vol.39 (5), p.1482-1492
• Main Authors: Kamrunnahar, M., Dias, Nuno Sérgio Mendes, Schiff, S. J.
• Format: Article
• Language: English
• Subjects: Biochemistry; Biological; Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Brain; Brain - physiology; Ciências Médicas; Classical Mechanics; Computers; Engenharia e Tecnologia; Engenharia Médica; Humans; Medicina Básica; Models, Biological; Motor task discrimination; Movement imagery task; Science & Technology; User-Computer Interface
• ISSN: 0090-6964; 1573-9686
• DOI: 10.1007/s10439-011-0248-y

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications. Grants K25NS061001 (MK) and K02MH01493 (SJS) from the National Institute of Neurological Disorders And Stroke (NINDS) and the National Institute of Mental Health (NIMH), the Portuguese Foundation for Science and Technology (FCT) Grant SFRH/BD/21529/2005 (NSD), the Pennsylvania Department of Community and Economic Development Keystone Innovation Zone Program Fund (SJS), and the Pennsylvania Department of Health using Tobacco Settlement Fund (SJS).