Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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Real-Time Decoding of Multi-Channel Peripheral Nerve Activity

다채널 말초 신경신호의 실시간 디코딩

  • 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)
  • Received : 2020.11.25
  • Accepted : 2020.12.28
  • Published : 2020.12.31
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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.

신경의수를 제어하기 위해서는 사용자의 의도를 인식하는 신경신호 디코딩이 중요하다. 본 논문에서는 다채널 말초 신경신호의 실시간 디코딩 방법을 제안한다. 말초 신경신호는 정중신경과 요골신경에서 측정되었으며 운동잡음은 국소 근사 다항식에 의해 제거되었다. 다음으로 활동전위는 k-평균 알고리즘으로 분류되었다. 특징벡터는 활동전위의 발화율로부터 추출되었으며 자기 조직화 특징지도를 통해 차원이 축소되었다. 마지막으로 다층 퍼셉트론으로 손동작을 분류하였다. 원숭이 실험에서 모든 신호처리가 실시간 제한조건 이내에 완료되었으며 높은 성공률로 손동작을 인식할 수 있었다.

Keywords

Acknowledgement

This work was supported in part by the convergence technology development program for bionic arm through the National Research Foundation of Korea (NRF) funded by the Ministry of Science & ICT (2017M3C1B2085311) and the National Research Council of Science & Technology (NST) grant by the Korea government (MSIT) (No. CAP-18-01-KIST).

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