Optimal calibration of the learning rate in closed-loop adaptive brain-machine interfaces.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Machine Learning

Background:

• Closed-loop decoder adaptation (CLDA) enhances brain-machine interface (BMI) performance by refitting decoder parameters during operation.
• Dynamic state-space algorithms are emerging for point process decoder fitting.
• The learning rate is a critical, yet empirically chosen, parameter in CLDA algorithms.

Purpose of the Study:

• To develop a principled framework for calibrating the learning rate in adaptive state-space algorithms for CLDA.
• To establish a method for selecting an optimal learning rate that balances convergence speed and steady-state error.
• To provide a systematic approach for setting the learning rate based on desired performance levels.

Main Methods:

• Developed a framework to analytically determine the trade-off between convergence rate and steady-state error covariance.
• Derived an upper-bound on steady-state error covariance as a function of the learning rate.
• Implemented an inverse mapping to select the optimal learning rate based on a maximum allowable steady-state error.

Main Results:

• The proposed calibration algorithm successfully selects an optimal learning rate.
• The selected learning rate meets specified steady-state error requirements.
• The algorithm achieves the fastest possible convergence rate for the given steady-state error level.

Conclusions:

• A principled framework for learning rate calibration in adaptive state-space CLDA is established.
• This method offers a systematic approach to optimize BMI decoder adaptation.
• The framework enables efficient and accurate brain-machine interface performance by optimizing key parameters.