Yue Zan, Xiao Peng, Wang Jing, Tong Raymond Kai-Yu
Institute of Robotics and Intelligent Systems, Xi'an Jiaotong University, Xi'an, China.
Neurorehabilitation Robotics Research Institute, Xi'an Jiaotong University, Xi'an, China.
Front Neurosci. 2023 Dec 11;17:1241772. doi: 10.3389/fnins.2023.1241772. eCollection 2023.
Hand rehabilitation in chronic stroke remains challenging, and finding markers that could reflect motor function would help to understand and evaluate the therapy and recovery. The present study explored whether brain oscillations in different electroencephalogram (EEG) bands could indicate the motor status and recovery induced by action observation-driven brain-computer interface (AO-BCI) robotic therapy in chronic stroke. The neurophysiological data of 16 chronic stroke patients who received 20-session BCI hand training is the basis of the study presented here. Resting-state EEG was recorded during the observation of non-biological movements, while task-stage EEG was recorded during the observation of biological movements in training. The motor performance was evaluated using the Action Research Arm Test (ARAT) and upper extremity Fugl-Meyer Assessment (FMA), and significant improvements ( < 0.05) on both scales were found in patients after the intervention. Averaged EEG band power in the affected hemisphere presented negative correlations with scales pre-training; however, no significant correlations ( > 0.01) were found both in the pre-training and post-training stages. After comparing the variation of oscillations over training, we found patients with good and poor recovery presented different trends in delta, low-beta, and high-beta variations, and only patients with good recovery presented significant changes in EEG band power after training (delta band, < 0.01). Importantly, motor improvements in ARAT correlate significantly with task EEG power changes (low-beta, c.c = 0.71, = 0.005; high-beta, c.c = 0.71, = 0.004) and task/rest EEG power ratio changes (delta, c.c = -0.738, = 0.003; low-beta, c.c = 0.67, = 0.009; high-beta, c.c = 0.839, = 0.000). These results suggest that, in chronic stroke, EEG band power may not be a good indicator of motor status. However, ipsilesional oscillation changes in the delta and beta bands provide potential biomarkers related to the therapeutic-induced improvement of motor function in effective BCI intervention, which may be useful in understanding the brain plasticity changes and contribute to evaluating therapy and recovery in chronic-stage motor rehabilitation.
慢性中风后的手部康复仍然具有挑战性,寻找能够反映运动功能的标志物将有助于理解和评估治疗及恢复情况。本研究探讨了不同脑电图(EEG)频段的脑振荡是否能够指示慢性中风患者在动作观察驱动的脑机接口(AO-BCI)机器人治疗中所诱导的运动状态和恢复情况。16名接受了20次BCI手部训练的慢性中风患者的神经生理数据是本研究所呈现内容的基础。在观察非生物运动期间记录静息态EEG,而在训练中观察生物运动期间记录任务阶段EEG。使用动作研究臂测试(ARAT)和上肢Fugl-Meyer评估(FMA)对运动表现进行评估,干预后患者在这两个量表上均有显著改善(<0.05)。患侧半球的平均EEG频段功率与训练前量表呈负相关;然而,在训练前和训练后阶段均未发现显著相关性(>0.01)。在比较训练过程中振荡的变化后,我们发现恢复良好和恢复较差的患者在δ波、低β波和高β波变化方面呈现出不同的趋势,并且只有恢复良好的患者在训练后EEG频段功率出现了显著变化(δ波频段,<0.01)。重要的是,ARAT中的运动改善与任务EEG功率变化(低β波,c.c = 0.71, = 0.005;高β波,c.c = 0.71, = 0.004)以及任务/静息EEG功率比变化(δ波,c.c = -0.738, = 0.003;低β波,c.c = 0.67, = 0.009;高β波,c.c = 0.839, = 0.000)显著相关。这些结果表明,在慢性中风中,EEG频段功率可能不是运动状态的良好指标。然而,δ波和β波频段的患侧振荡变化提供了与有效BCI干预中治疗诱导的运动功能改善相关的潜在生物标志物,这可能有助于理解脑可塑性变化,并有助于评估慢性期运动康复中的治疗和恢复情况。