Presacco Alessandro, Forrester Larry, Contreras-Vidal Jose L
Department of Kinesiology, University of Maryland, College Park, MD 20742, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4588-91. doi: 10.1109/IEMBS.2011.6091136.
Before 2009, the feasibility of applying brain-machine interfaces (BMIs) to control prosthetic devices had been limited to upper limb prosthetics such as the DARPA modular prosthetic limb. Until recently, it was believed that the control of bipedal locomotion involved central pattern generators with little supraspinal control. Analysis of cortical dynamics with electroencephalography (EEG) was also prevented by the lack of analysis tools to deal with excessive signal artifacts associated with walking. Recently, Nicolelis and colleagues paved the way for the decoding of locomotion showing that chronic recordings from ensembles of cortical neurons in primary motor (M1) and primary somatosensory (S1) cortices can be used to decode bipedal kinematics in rhesus monkeys. However, neural decoding of bipedal locomotion in humans has not yet been demonstrated. This study uses non-invasive EEG signals to decode human walking in six nondisabled adults. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs, to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular kinematics of the left and right hip, knee and ankle joints and EEG were recorded concurrently. Our results support the possibility of decoding human bipedal locomotion with EEG. The average of the correlation values (r) between predicted and recorded kinematics for the six subjects was 0.7 (± 0.12) for the right leg and 0.66 (± 0.11) for the left leg. The average signal-to-noise ratio (SNR) values for the predicted parameters were 3.36 (± 1.89) dB for the right leg and 2.79 (± 1.33) dB for the left leg. These results show the feasibility of developing non-invasive neural interfaces for volitional control of devices aimed at restoring human gait function.
2009年以前，将脑机接口（BMI）应用于控制假肢装置的可行性仅限于上肢假肢，如美国国防高级研究计划局（DARPA）的模块化假肢。直到最近，人们一直认为双足运动的控制涉及中枢模式发生器，几乎没有脊髓上的控制。脑电图（EEG）对皮层动力学的分析也因缺乏处理与行走相关的过多信号伪迹的分析工具而受到阻碍。最近，尼科莱利斯及其同事为运动解码铺平了道路，表明从初级运动（M1）和初级体感（S1）皮层的皮层神经元集合进行的长期记录可用于解码恒河猴的双足运动学。然而，人类双足运动的神经解码尚未得到证实。本研究使用非侵入性EEG信号对六名非残疾成年人的人类行走进行解码。参与者被要求以自己选择的舒适速度在跑步机上行走，同时接收下肢的视觉反馈，以反复避免踩到跑步机皮带上绘制的条带。同时记录左右髋、膝和踝关节的角运动学和EEG。我们的结果支持了用EEG解码人类双足运动的可能性。六名受试者预测运动学与记录运动学之间的相关值（r）平均值，右腿为0.7（±0.12），左腿为0.66（±0.11）。预测参数的平均信噪比（SNR）值，右腿为3.36（±1.89）dB，左腿为2.79（±1.33）dB。这些结果表明，开发用于旨在恢复人类步态功能的设备的自主控制的非侵入性神经接口是可行的。
