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

M Kamrunnahar1, N S Dias, S J Schiff

  • 1Center for Neural Engineering, Department of Engineering Science and Mechanics, The Pennsylvania State University, University Park, PA 16802, USA. muk11@psu.edu

Annals of Biomedical Engineering
|January 27, 2011
PubMed
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This study introduces a model-based predictive controller (MPC) for brain-computer interfaces (BCI). The new controller design shows comparable performance to existing methods for motor imagery task discrimination.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Control Systems

Background:

  • Current brain-computer interface (BCI) applications often rely on ad hoc, non-model-based filters.
  • Model-based predictive control (MPC) offers a potential pathway to enhance BCI performance.
  • Designing an optimal MPC is a crucial first step for advanced BCI systems.

Purpose of the Study:

  • To present a foundational step in designing a robust and optimal model-based predictive controller (MPC) for BCI applications.
  • To explore the utility of model-based features extracted from electroencephalography (EEG) for controller design and task discrimination.

Main Methods:

  • Extracted model-based features from human scalp electroencephalography (EEG) data during motor imagery tasks.
  • Utilized a simple autoregressive (AR) model to generate parameters for controller design.

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  • Evaluated the performance of these parameters for both controller design and motor imagery task discrimination.

    • Main Results:

    • The parameters generated for MPC design demonstrated effectiveness in motor imagery task discrimination.
    • Task discrimination performance using these parameters was comparable to commonly used features like band powers and direct AR parameters.
    • Achieved task discrimination errors ranging from 8-23%.

    Conclusions:

    • Model-based features derived from EEG can be effectively used for both BCI controller design and task discrimination.
    • The proposed approach using an AR model for MPC parameter extraction shows promise for improving BCI performance.
    • Optimal MPC has significant implications for developing high-performance BCI applications.