IEEE Trans Neural Syst Rehabil Eng. 2014 Mar;22(2):239-48. doi: 10.1109/TNSRE.2013.2287768.
Intracortical brain-computer interfaces (iBCIs) decode intended movement from neural activity for the control of external 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 depart 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 seconds) and requires no knowledge of the intended movement. The algorithm, called multiple offset correction algorithm (MOCA), was tested using simulated neural activity and evaluated retrospectively using data collected from two people with tetraplegia operating an iBCI. In 19 clinical research test cases, where a nonadaptive Kalman filter yielded relatively high decoding errors, MOCA significantly reduced these errors ( 10.6 ± 10.1% ; p < 0.05, pairwise t-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.
皮层内脑机接口（iBCI）可解码神经活动，以控制外部设备，如机械臂。标准方法包括校准阶段，以估计解码参数。在 iBCI 操作期间，神经活动的统计特性可能与校准期间观察到的特性不同，这有时会阻碍用户控制 iBCI 的能力。为了解决这个问题，我们通过惩罚最大似然估计自适应地校正卡尔曼滤波器解码器中的偏移项。该方法可以处理神经信号行为的快速变化（在几秒钟内），并且不需要了解预期的运动。该算法称为多偏移校正算法（MOCA），使用模拟神经活动进行了测试，并使用来自两名四肢瘫痪患者操作 iBCI 的数据进行了回顾性评估。在 19 个临床研究测试案例中，非自适应卡尔曼滤波器产生了相对较高的解码错误，而 MOCA 则显著降低了这些错误（10.6±10.1％；p<0.05，配对 t 检验）。在非自适应卡尔曼滤波器已经表现良好的其余 23 个案例中，MOCA 并没有显著改变错误。这些结果表明，与标准卡尔曼滤波器相比，MOCA 为 iBCI 提供了更稳健的解码。
