Controlling an electromyography-based power-assist device for the wrist using electroencephalography cortical currents
Controlling an electromyography-based power-assist device for the wrist using electroencephalography cortical currents
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DOI：
10.1080/01691864.2016.1215935
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发表时间：
2017-01-01
影响因子：
2
通讯作者：
Koike, Yasuharu
中科院分区：
文献类型：
--
作者：
Kawase, Toshihiro;Yoshimura, Natsue;Koike, Yasuharu
由大脑活动控制的动力辅助装置将为运动障碍患者提供更好的生活质量。这种设备需要从大脑活动中提取必要的信息，如果可能的话，以非侵入性的方式。在这里，我们根据脑电图 (EEG) 皮质电流源 (CCS) 信号以及标准脑电图传感器信号重建了腕部屈曲和伸展的肌肉活动。然后，我们使用重建的肌肉活动来控制基于肌电图 (EMG) 的动力辅助装置。我们通过将脑电图估计的平衡点、刚度和扭矩与肌电图计算的平衡点、刚度和扭矩进行比较来评估性能。我们发现 EEG-CCS 信号比 EEG 传感器信号提供更高的估计精度。此外，我们使用重建的肌肉活动信号对基于肌电图的设备进行离线控制。我们的结果表明，可以根据 EEG-CCS 信号重建肌肉活动，并且具有足够的精度来控制基于 EMG 的动力辅助设备。我们证明脑机接口可用于控制基于肌电图的动力辅助设备，从而相互拓宽其在康复中的适用性。
A power-assist device controlled with brain activity would offer improved quality of life for people with motor impairments. Such a device requires extracting the necessary information from brain activity, and if possible, in a non-invasive manner. Here, we reconstructed muscle activity for wrist flexion and extension from electroencephalography (EEG) cortical current source (CCS) signals as well as standard EEG sensor signals. We then used the reconstructed muscle activity to control an electromyography (EMG)-based power assist device. We evaluated performance by comparing equilibrium point, stiffness, and torque estimated from EEG with those calculated from EMG. We found that EEG-CCS signals provided higher estimation accuracy than EEG sensor signals. Moreover, we performed offline control of the EMG-based device using reconstructed muscle activity signals. Our results suggest that muscle activity can be reconstructed from EEG-CCS signals with sufficient accuracy to control an EMG-based power-assist device. We demonstrate that brain-machine interfaces can be used to control EMG-based power-assist devices, mutually broadening their applicability in rehabilitation.