Campus Bio-Medico University, Rome, Italy.
Neurorehabil Neural Repair. 2012 Mar-Apr;26(3):275-81. doi: 10.1177/1545968311408919. Epub 2011 Jul 5.
Interfacing an amputee's upper-extremity stump nerves to control a robotic hand requires training of the individual and algorithms to process interactions between cortical and peripheral signals.
To evaluate for the first time whether EEG-driven analysis of peripheral neural signals as an amputee practices could improve the classification of motor commands.
Four thin-film longitudinal intrafascicular electrodes (tf-LIFEs-4) were implanted in the median and ulnar nerves of the stump in the distal upper arm for 4 weeks. Artificial intelligence classifiers were implemented to analyze LIFE signals recorded while the participant tried to perform 3 different hand and finger movements as pictures representing these tasks were randomly presented on a screen. In the final week, the participant was trained to perform the same movements with a robotic hand prosthesis through modulation of tf-LIFE-4 signals. To improve the classification performance, an event-related desynchronization/synchronization (ERD/ERS) procedure was applied to EEG data to identify the exact timing of each motor command.
Real-time control of neural (motor) output was achieved by the participant. By focusing electroneurographic (ENG) signal analysis in an EEG-driven time window, movement classification performance improved. After training, the participant regained normal modulation of background rhythms for movement preparation (α/β band desynchronization) in the sensorimotor area contralateral to the missing limb. Moreover, coherence analysis found a restored α band synchronization of Rolandic area with frontal and parietal ipsilateral regions, similar to that observed in the opposite hemisphere for movement of the intact hand. Of note, phantom limb pain (PLP) resolved for several months.
Combining information from both cortical (EEG) and stump nerve (ENG) signals improved the classification performance compared with tf-LIFE signals processing alone; training led to cortical reorganization and mitigation of PLP.
将上肢截肢者残肢的神经与机器人手相连接，需要对个体进行训练，并开发算法来处理皮质和外周信号之间的相互作用。
首次评估在截肢者进行练习时，对周围神经信号进行 EEG 驱动分析是否可以提高运动指令的分类。
将四个薄膜纵向束内电极（tf-LIFEs-4）植入上臂远端残肢的正中神经和尺神经中 4 周。实施人工智能分类器分析 LIFE 信号，记录参与者尝试执行 3 种不同的手和手指运动时的情况，这些运动通过在屏幕上随机显示的图片来代表。在最后一周，通过调制 tf-LIFE-4 信号，参与者被训练使用机器人手假肢执行相同的运动。为了提高分类性能，通过应用事件相关去同步/同步（ERD/ERS）程序来识别每个运动命令的精确时间，对 EEG 数据进行处理。
参与者实现了神经（运动）输出的实时控制。通过将电神经图（ENG）信号分析聚焦在 EEG 驱动的时间窗口内，运动分类性能得到了提高。经过训练，参与者恢复了缺失肢体对侧感觉运动区对运动准备的背景节律正常调制（α/β 频带去同步）。此外，相干性分析发现 Rolandic 区与同侧额区和顶区之间恢复了α 带同步，类似于对完整手运动观察到的对侧半球情况。值得注意的是，幻肢痛（PLP）缓解了几个月。
与单独处理 tf-LIFE 信号相比，结合来自皮质（EEG）和残肢神经（ENG）信号的信息提高了分类性能；训练导致皮质重组和 PLP 减轻。
