Neuron selection by relative importance for neural decoding of dexterous finger prosthesis control application |
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Authors: | Hyoung-Nam Kim Yong-Hee Kim Hyun-Chool Shin Vikram Aggarwal Marc H Schieber Nitish V Thakor |
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Institution: | 1. Department of Electronics Engineering, Pusan National University, Busan 609-735, Republic of Korea;2. Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA;3. School of Electronics Engineering, Soongsil University, Seoul 156-734, Republic of Korea;4. Department of Neurology, Neurobiology and Anatomy, Brain and Cognitive Sciences and Physical Medicine and Rehabilitation, University of Rochester Medical Center, Rochester, NY 14642, USA |
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Abstract: | Future generations of upper limb prosthesis will have dexterous hand with individual fingers and will be controlled directly by neural signals. Neurons from the primary motor (M1) cortex code for finger movements and provide the source for neural control of dexterous prosthesis. Each neuron's activation can be quantified by the change in firing rate before and after finger movement, and the quantified value is then represented by the neural activity over each trial for the intended movement. Since this neural activity varies with the intended movement, we define the relative importance of each neuron independent of specific intended movements. The relative importance of each neuron is determined by the inter-movement variance of the neural activities for respective intended movements. Neurons are ranked by the relative importance and then a subpopulation of rank-ordered neurons is selected for the neural decoding. The use of the proposed neuron selection method in individual finger movements improved decoding accuracy by 21.5% in the case of decoding with only 5 neurons and by 9.2% in the case of decoding with only 10 neurons. With only 15 highly ranked neurons, a decoding accuracy of 99.5% was achieved. The performance improvement is still maintained when combined movements of two fingers were included though the decoding accuracy fell to 95.7%. Since the proposed neuron selection method can achieve the targeting accuracy of decoding algorithms with less number of input neurons, it can be significant for developing brain–machine interfaces for direct neural control of hand prostheses. |
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