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http://dx.doi.org/10.7471/ikeee.2020.24.4.1039

Real-Time Decoding of Multi-Channel Peripheral Nerve Activity  

Jee, In-Hyeog (School of Electronics Engineering, Kyungpook National University)
Lee, Yun-Jung (School of Electronics Engineering, Kyungpook National University)
Chu, Jun-Uk (Dept. of Medical Device, Daegu Research Center for Medical Devices and Rehabilitation Engineering, Korea Institute of Machinery and Materials)
Publication Information
Journal of IKEEE / v.24, no.4, 2020 , pp. 1039-1049 More about this Journal
Abstract
Neural decoding is important to recognize the user's intention for controlling a neuro-prosthetic hand. This paper proposes a real-time decoding method for multi-channel peripheral neural activity. Peripheral nerve signals were measured from the median and radial nerves, and motion artifacts were removed based on locally fitted polynomials. Action potentials were then classified using a k-means algorithm. The firing rate of action potentials was extracted as a feature vector and its dimensionality was reduced by a self-organizing feature map. Finally, a multi-layer perceptron was used to classify hand motions. In monkey experiments, all processes were completed within a real-time constrain, and the hand motions were recognized with a high success rate.
Keywords
Neural activity; Decoding; Action potentials; Neuro-prosthetic hand; Monkey experiments;
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