Tam Wing-Kin, Wu Tong, Zhao Qi, Keefer Edward, Yang Zhi
Department of Biomedical Engineering, University of Minnesota Twin Cities, 7-105 Hasselmo Hall, 312 Church St. SE, Minnesota, 55455 USA.
Department of Computer Science and Engineering, University of Minnesota Twin Cities, 4-192 Keller Hall, 200 Union Street SE, Minnesota, 55455 USA.
BMC Biomed Eng. 2019 Sep 3;1:22. doi: 10.1186/s42490-019-0022-z. eCollection 2019.
Many people suffer from movement disability due to amputation or neurological diseases. Fortunately, with modern neurotechnology now it is possible to intercept motor control signals at various points along the neural transduction pathway and use that to drive external devices for communication or control. Here we will review the latest developments in human motor decoding. We reviewed the various strategies to decode motor intention from human and their respective advantages and challenges. Neural control signals can be intercepted at various points in the neural signal transduction pathway, including the brain (electroencephalography, electrocorticography, intracortical recordings), the nerves (peripheral nerve recordings) and the muscles (electromyography). We systematically discussed the sites of signal acquisition, available neural features, signal processing techniques and decoding algorithms in each of these potential interception points. Examples of applications and the current state-of-the-art performance were also reviewed. Although great strides have been made in human motor decoding, we are still far away from achieving naturalistic and dexterous control like our native limbs. Concerted efforts from material scientists, electrical engineers, and healthcare professionals are needed to further advance the field and make the technology widely available in clinical use.
许多人因截肢或神经系统疾病而患有运动障碍。幸运的是，借助现代神经技术，现在可以在神经传导通路的各个点截取运动控制信号，并利用这些信号来驱动外部设备进行通信或控制。在此，我们将回顾人类运动解码的最新进展。我们回顾了从人类身上解码运动意图的各种策略及其各自的优点和挑战。神经控制信号可以在神经信号传导通路的各个点被截取，包括大脑（脑电图、皮层脑电图、皮层内记录）、神经（外周神经记录）和肌肉（肌电图）。我们系统地讨论了在这些潜在截取点中的每一个点的信号采集部位、可用的神经特征、信号处理技术和解码算法。还回顾了应用实例和当前的先进性能。尽管在人类运动解码方面已经取得了巨大进展，但我们距离实现像我们的天然肢体那样自然和灵活的控制仍有很大差距。材料科学家、电气工程师和医疗保健专业人员需要共同努力，以进一步推动该领域的发展，并使该技术在临床应用中广泛可用。
