Decoding motor performance from brain signals offers promising avenues for adaptive deep brain stimulation (aDBS) for Parkinson's disease (PD). In a two-center cohort of 19 PD patients executing a drawing task, we decoded motor performance from electroencephalography (n=15) and, critically for clinical translation, electrocorticography (n=4). Within each session, patients performed the task under DBS on and DBS off. A total of 35 sessions were recorded. Instead of relying on single frequency bands, we derived patient-specific biomarkers using a filterbank-based machine-learning approach. DBS modulated kinematics significantly in 23 sessions. Significant neural decoding of kinematics was possible in 28 of the 35 sessions (average Pearson's $\text{r}= 0.37$). Our results further demonstrate modulation of speed-accuracy trade-offs, with increased drawing speed but reduced accuracy under DBS. Joint evaluation of behavioral and neural decoding outcomes revealed six prototypical scenarios, for which we provide guidance for future aDBS strategies.

翻译：从脑信号中解码运动表现为帕金森病（PD）的自适应深部脑刺激（aDBS）提供了有前景的途径。在一个双中心队列中，我们对19名执行绘图任务的PD患者进行脑电图（n=15）以及用于临床转化的关键——皮质电图（n=4）的运动表现解码。在每次实验过程中，患者分别在DBS开启和DBS关闭状态下执行任务，共记录了35次实验。我们不依赖单一频带，而是采用基于滤波器组的机器学习方法导出患者特异性生物标志物。DBS在23次实验中显著调节了运动学参数。在35次实验中有28次实现了运动学的显著神经解码（平均皮尔逊相关系数$\text{r}=0.37$）。我们的结果进一步证明了速度-准确性权衡的调节，即在DBS下绘图速度增加但准确性下降。行为学与神经解码结果的联合评估揭示了六种原型场景，我们为未来aDBS策略提供了相应指导。