Refined Force Estimation in Monkey's Pinching Tasks Through Integrated EMG and ECoG Data: A Kalman Filter Method

In the development of brain-computer interfaces (BCIs), precise decoding of motor outputs is crucial. This study presents an enhanced Kalman filter approach that integrates electromyography (EMG) with electrocorticography (ECoG) to improve force estimation in pinching tasks. By incorporating EMG data as a state variable in the filter, we aim to account for musculoskeletal dynamics, enhancing the accuracy of force predictions. This integration significantly improves the decoding performance, particularly during dynamic force phases. The results confirm the importance of embedding musculoskeletal dynamics into ECoG-based BCIs, which may help improve prosthetic control and motor rehabilitation for people with motor impairments.