Dual Kalman Filter Based on Maximum Correntropy Criterion for Adaptive Decoding in Brain-Machine Interface

Brain-Machine Interface (BMI) assists paralyzed patients in restoring motor functions by controlling neuroprosthetic devices through intentions captured from brain activities. Traditional neural signal decoding algorithms typically rely on Gaussian noise assumptions and static functional relationships between neural activities and movements, overlooking the common presence of non-Gaussian noise in the neural encoding process and the dynamic variations in neural tuning over time. To address these challenges, this study introduces a novel decoding algorithm that integrates maximum correntropy criterion (MCC) with dual Kalman filters. By incorporating MCC, the algorithm enhances the filter's robustness to non-Gaussian noise by capturing higher-order statistical properties of information. Additionally, a pair of mutually communicative dual filters are used to estimate states and periodically update parameters. Tested within a simulated Skinner box experiment, our algorithm exhibits superior performance in tracking the dynamic variations of neural tuning under non-Gaussian noise conditions compared to traditional Kalman filter with fixed tuning parameters and dual Kalman filter without MCC incorporation, and provides a new solution for BMI applications requiring long-term stable operation.