From the Noninvasive Brain-Machine Interface Systems Laboratory, Department of Electrical and Computer Engineering, University of Houston, Houston, Texas (JLC-V, MB, FZ, KN, AV); Neural Rehabilitation Group, Cajal Institute, Spanish Research Council, Madrid, Spain (MB, JLP); TIRR Memorial Hermann and Department of PM&R, University of Texas Health Sciences Center, Houston, Texas (GEF); Palo Alto Research Center, Inc. (PARC, a Xerox Company), Palo Alto, California (AV); and School of Engineering, Tecnologico de Monterrey, Monterrey, Mexico (JLC-V, GEF, RS, JLP).
Am J Phys Med Rehabil. 2018 Aug;97(8):541-550. doi: 10.1097/PHM.0000000000000914.
Advancements in robot-assisted gait rehabilitation and brain-machine interfaces may enhance stroke physiotherapy by engaging patients while providing information about robot-induced cortical adaptations. We investigate the feasibility of decoding walking from brain activity in stroke survivors during therapy using a powered exoskeleton integrated with an electroencephalography-based brain-machine interface.
The H2 powered exoskeleton was designed for overground gait training with actuated hip, knee, and ankle joints. It was integrated with active-electrode electroencephalography and evaluated in hemiparetic stroke survivors for 12 sessions per 4 wks. A continuous-time Kalman decoder operating on delta-band electroencephalography was designed to estimate gait kinematics.
Five chronic stroke patients completed the study with improvements in walking distance and speed training for 4 wks, correlating with increased offline decoding accuracy. Accuracies of predicted joint angles improved with session and gait speed, suggesting an improved neural representation for gait, and the feasibility to design an electroencephalography-based brain-machine interface to monitor brain activity or control a rehabilitative exoskeleton.
The Kalman decoder showed increased accuracies as the longitudinal training intervention progressed in the stroke participants. These results demonstrate the feasibility of studying changes in patterns of neuroelectric cortical activity during poststroke rehabilitation and represent the first step in developing a brain-machine interface for controlling powered exoskeletons.
机器人辅助步态康复和脑机接口的进步可以通过让患者参与并提供有关机器人引起的皮质适应的信息，从而增强中风物理治疗。我们研究了在使用与基于脑电图的脑机接口集成的动力外骨骼进行治疗期间，从中风幸存者的大脑活动中解码行走的可行性。
H2 动力外骨骼专为地面行走训练而设计，具有带动力的髋关节、膝关节和踝关节。它与主动电极脑电图集成，并在偏瘫中风幸存者中进行了 4 周内 12 次的评估。设计了一个连续时间卡尔曼解码器，用于估计步态运动学，该解码器在 delta 波段脑电图上运行。
五名慢性中风患者完成了为期 4 周的行走距离和速度训练，与离线解码准确性的提高相关。预测关节角度的准确性随着会话和行走速度的提高而提高，这表明步态的神经表示得到了改善，并且有可能设计基于脑电图的脑机接口来监测大脑活动或控制康复外骨骼。
卡尔曼解码器在中风参与者的纵向训练干预过程中显示出更高的准确性。这些结果证明了在中风康复期间研究神经电皮质活动模式变化的可行性，并代表了开发用于控制动力外骨骼的脑机接口的第一步。
