Adaptive Offset Correction for Intracortical Brain–Computer Interfaces

Abstract—Intracortical brain–computer interfaces (iBCIs) de-code intended movement from neural activity for the control of ex-ternal devices such as a robotic arm. Standard approaches include a calibration phase to estimate decoding parameters. During iBCI operation, the statistical properties of the neural activity can de-part from those observed during calibration, sometimes hindering a user’s ability to control the iBCI. To address this problem, we adaptively correct the offset terms within a Kalman filter decoder via penalized maximum likelihood estimation. The approach can handle rapid shifts in neural signal behavior (on the order of sec-onds) and requires no knowledge of the intended movement. The algorithm, called multiple offset correction algorithm (MOCA), was tested using simulated neural activity and evaluated retrospec-tively using data collected from two people with tetraplegia oper-ating an iBCI. In 19 clinical research test cases, where a nonadap-tive Kalman filter yielded relatively high decoding errors, MOCA significantly reduced these errors ( ;, pairwise-test). MOCA did not significantly change the error in the remaining 23 cases where a nonadaptive Kalman filter already performed well. These results suggest that MOCA provides more robust decoding than the standard Kalman filter for iBCIs. Index Terms—Adaptive filtering, brain–computer interface