日本大阪大学研究人员日前在美国《神经学年鉴》（Annals of Neurology）期刊网络版上发表论文说，他们利用安装在运动麻痹患者脑部表面的电极，成功读取患者的脑电波，推测出其意图，从而使作为假肢的机器人动了起来。这一成果有望促进开发出帮助相关疾病患者运动和表达意图的装置。
研究人员为12名年龄在13岁至66岁的运动麻痹患者脑部表面安装电极，这些患者的运动麻痹症状各不相同。研究人员利用电脑分析患者希望动手和动胳膊时脑电波的特征，然后让电脑“记住”这些特征，推测患者运动意图，结果准确率可达60%至90%。
例如，对于半身不遂患者，电脑能够高效解读患者的脑电波，准确推测出患者希望做出的动作，将分析结果输入作为假肢的机器人，机器人能实时实现弯曲肘部、抓东西等符合患者意图的动作。
研究人员下一步将扩大研究对象范围，争取将这项技术早日实用化，以帮助患者提高生活质量。（生物谷 Bioon.com）
doi:10.1002/ana.22613
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Electrocorticographic control of a prosthetic arm in paralyzed patients
Yanagisawa, Takufumi; Hirata, Masayuki; Saitoh, Youichi; Kishima, Haruhiko; Matsushita, Kojiro; Goto, Tetsu; Fukuma, Ryohei; Yokoi, Hiroshi; Kamitani, Yukiyasu; Yoshimine, Toshiki
Objective: Paralyzed patients may benefit from restoration of movement afforded by prosthetics controlled by electrocorticography (ECoG). Although ECoG shows promising results in human volunteers, it is unclear whether ECoG signals recorded from chronically paralyzed patients provide sufficient motor information, and if they do, whether they can be applied to control a prosthetic. Methods: We recorded ECoG signals from sensorimotor cortices of 12 patients while they executed or attempted to execute 3 to 5 simple hand and elbow movements. Sensorimotor function was severely impaired in 3 patients due to peripheral nervous system lesion or amputation, moderately impaired due to central nervous system lesions sparing the cortex in 4 patients, and normal in 5 patients. Time frequency and decoding analyses were performed with the patients' ECoG signals. Results: In all patients, the high gamma power (80–150Hz) of the ECoG signals during movements was clearly responsive to movement types and provided the best information for classifying different movement types. The classification performance was significantly better than chance in all patients, although differences between ECoG power modulations during different movement types were significantly less in patients with severely impaired motor function. In the impaired patients, cortical representations tended to overlap each other. Finally, using the classification method in real time, a moderately impaired patient and 3 nonparalyzed patients successfully controlled a prosthetic arm
