Optimization and evaluation of the control framework for brain-machine interfaces

This work presents and evaluates a method for reducing the number of hyper-parameters in the continuous control system used by a 2-class motor imagery (MI) brain-machine interface (BMI). The work focuses on two parameters (ω and ψ) used within a dynamical control system that considers the nature and temporal evolution of the BMI decoder output and that it has been already validated in the past.To identify the optimal values for the parameters, we analysed a dataset of 12 subjects performing 2-class MI tasks. For each subject, we defined a new metric to investigate the existence of a relationship between the hyper-parameters. The study reveals a quadratic relationship with coefficient of determination (R 2 ) of 81.67%.Finally, the established relationship was evaluated through an closed-loop experiment involving three healthy subjects. Results demonstrated the potential use of the discovered quadratic relationship to reduce the number of parameters for the dynamical control system and, thus, to simplify the BMI operations.