Neural Decoding of Finger Movements Using Skellam-Based Maximum-Likelihood Decoding
DC Field | Value | Language |
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dc.contributor.author | Shin, Hyun-Chool | - |
dc.contributor.author | Aggarwal, Vikram | - |
dc.contributor.author | Acharya, Soumyadipta | - |
dc.contributor.author | Schieber, Marc H. | - |
dc.contributor.author | Thakor, Nitish V. | - |
dc.date.available | 2018-05-10T14:13:39Z | - |
dc.date.created | 2018-04-17 | - |
dc.date.issued | 2010-03 | - |
dc.identifier.issn | 0018-9294 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/14796 | - |
dc.description.abstract | We present an optimal method for decoding the activity of primary motor cortex (M1) neurons in a nonhuman primate during single finger movements. The method is based on the maximum-likelihood (ML) inference, which assuming the probability of finger movements is uniform, is equivalent to the maximum a posteriori ( MAP) inference. Each neuron's activation is first quantified by the change in firing rate before and after finger movement. We then estimate the probability density function of this activation given finger movement, i.e., Pr(neuronal activation (x)vertical bar finger movements ( m)). Based on the ML criterion, we choose finger movements to maximize Pr(x vertical bar m). Experimentally, data were collected from 115 task-related neurons in M1 as the monkey performed flexion and extension of each finger and the wrist ( 12 movements). With as few as 20-25 randomly selected neurons, the proposed method decoded single-finger movements with 99% accuracy. Since the training and decoding procedures in the proposed method are simple and computationally efficient, the method can be extended for real-time neuroprosthetic control of a dexterous hand. | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | - |
dc.subject | CORTEX HAND AREA | - |
dc.subject | CORTICAL-NEURONS | - |
dc.subject | REPRESENTATION | - |
dc.subject | INFORMATION | - |
dc.subject | ENSEMBLES | - |
dc.title | Neural Decoding of Finger Movements Using Skellam-Based Maximum-Likelihood Decoding | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TBME.2009.2020791 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, v.57, no.3, pp.754 - 760 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000274990800029 | - |
dc.identifier.scopusid | 2-s2.0-77649141839 | - |
dc.citation.endPage | 760 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 754 | - |
dc.citation.title | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | - |
dc.citation.volume | 57 | - |
dc.contributor.affiliatedAuthor | Shin, Hyun-Chool | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | Finger movements | - |
dc.subject.keywordAuthor | maximum likelihood | - |
dc.subject.keywordAuthor | neural decoding | - |
dc.subject.keywordAuthor | neural prosthetics | - |
dc.subject.keywordAuthor | Skellam | - |
dc.subject.keywordPlus | CORTEX HAND AREA | - |
dc.subject.keywordPlus | CORTICAL-NEURONS | - |
dc.subject.keywordPlus | REPRESENTATION | - |
dc.subject.keywordPlus | INFORMATION | - |
dc.subject.keywordPlus | ENSEMBLES | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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