Kang Xiaoxu, Schieber Marc H, Thakor Nitish V
Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1732-5. doi: 10.1109/EMBC.2012.6346283.
Previous works in Brain-Machine Interfaces (BMI) have mostly used a single Kalman filter decoder for deriving continuous kinematics in the complete execution of behavioral tasks. A linear dynamical system may not be able to generalize the sequence whose dynamics changes over time. Examples of such data include human motion such as walking, running, and dancing each of which exhibit complex constantly evolving dynamics. Switching linear dynamical systems (S-LDSs) are powerful models capable of describing a physical process governed by state equations that switch from time to time. The present work demonstrates the motion-state-dependent adaptive decoding of hand and arm kinematics in four different behavioral tasks. Single-unit neural activities were recorded from cortical ensembles in the ventral and dorsal premotor (PMv and PMd) areas of a trained rhesus monkey during four different reach-to-grasp tasks. We constructed S-LDSs for decoding of continuous hand and arm kinematics based on different epochs of the experiments, namely, baseline, pre-movement planning, movement, and final fixation. Average decoding accuracies as high as 89.9%, 88.6%, 89.8%, 89.4%, were achieved for motion-state-dependent decoding across four different behavioral tasks, respectively (p<0.05); these results are higher than previous works using a single Kalman filter (accuracy: 0.83). These results demonstrate that the adaptive decoding approach, or motion-state-dependent decoding, may have a larger descriptive capability than the decoding approach using a single decoder. This is a critical step towards the development of a BMI for adaptive neural control of a clinically viable prosthesis.
以往脑机接口（BMI）的研究大多使用单个卡尔曼滤波器解码器来推导行为任务完整执行过程中的连续运动学。线性动态系统可能无法概括动态随时间变化的序列。这类数据的例子包括人类运动，如行走、跑步和跳舞，每一种运动都表现出复杂且不断演变的动态。切换线性动态系统（S-LDSs）是强大的模型，能够描述由不时切换的状态方程所控制的物理过程。本研究展示了在四种不同行为任务中，手部和手臂运动学的运动状态依赖型自适应解码。在一只经过训练的恒河猴的腹侧和背侧运动前区（PMv和PMd）的皮质集合中记录了单神经元活动，记录过程涵盖四种不同的伸手抓握任务。我们基于实验的不同阶段构建了S-LDSs来解码连续的手部和手臂运动学，这些阶段分别是基线、运动前规划、运动和最终固定。在四种不同行为任务中的运动状态依赖型解码分别实现了高达89.9%、88.6%、89.8%、89.4%的平均解码准确率（p<0.05）；这些结果高于以往使用单个卡尔曼滤波器的研究（准确率：0.83）。这些结果表明，自适应解码方法，即运动状态依赖型解码，可能比使用单个解码器的解码方法具有更强的描述能力。这是朝着开发用于临床可行假肢自适应神经控制的BMI迈出的关键一步。
