Bioengineering Laboratory, University of Utah, Salt Lake City, UT 84602, USA.
IEEE Trans Neural Syst Rehabil Eng. 2010 Aug;18(4):424-32. doi: 10.1109/TNSRE.2010.2047590. Epub 2010 Apr 8.
A rhesus monkey was trained to perform individuated and combined finger flexions of the thumb, index, and middle finger. Nine implantable myoelectric sensors (IMES) were then surgically implanted into the finger muscles of the monkey's forearm, without any adverse effects over two years postimplantation. Using an inductive link, EMG was wirelessly recorded from the IMES as the monkey performed a finger flexion task. The EMG from the different IMES implants showed very little cross correlation. An offline parallel linear discriminant analysis (LDA) based algorithm was used to decode finger activity based on features extracted from continuously presented frames of recorded EMG. The offline parallel LDA was run on intraday sessions as well as on sessions where the algorithm was trained on one day and tested on following days. The performance of the algorithm was evaluated continuously by comparing classification output by the algorithm to the current state of the finger switches. The algorithm detected and classified seven different finger movements, including individual and combined finger flexions, and a no-movement state (chance performance = 12.5%) . When the algorithm was trained and tested on data collected the same day, the average performance was 43.8+/-3.6% n=10. When the training-testing separation period was five months, the average performance of the algorithm was 46.5+/-3.4% n=8. These results demonstrated that using EMG recorded and wirelessly transmitted by IMES offers a promising approach for providing intuitive, dexterous control of artificial limbs where human patients have sufficient, functional residual muscle following amputation.
一只恒河猴经过训练，可以独立或组合地弯曲拇指、食指和中指。随后，九枚可植入式肌电传感器（IMES）被手术植入猴子前臂的肌肉中，在植入后的两年内没有任何不良反应。通过感应式连接，当猴子执行手指弯曲任务时，IMES 产生的肌电信号被无线记录下来。来自不同 IMES 植入物的肌电信噪比非常低。使用一种基于离线并行线性判别分析（LDA）的算法，根据从记录的肌电信号中不断呈现的帧中提取的特征，对手指活动进行解码。离线并行 LDA 不仅在日间会话中运行，还在算法在一天内进行训练并在接下来的几天进行测试的会话中运行。通过将算法的分类输出与手指开关的当前状态进行比较，连续评估算法的性能。该算法检测并分类了七种不同的手指运动，包括单独和组合的手指弯曲，以及无运动状态（偶然表现为 12.5%）。当算法在同一天采集的数据上进行训练和测试时，平均性能为 43.8+/-3.6%（n=10）。当训练-测试分离期为五个月时，算法的平均性能为 46.5+/-3.4%（n=8）。这些结果表明，使用 IMES 记录和无线传输的肌电信号为提供直观、灵活的假肢控制提供了一种有前途的方法，对于那些截肢后仍有足够、功能正常的残留肌肉的人类患者来说尤其如此。
