NDLI: Neural Decoding of Finger Movements Using Skellam-Based Maximum-Likelihood Decoding

Content Provider	IEEE Xplore Digital Library
Author	Hyun-Chool Shin Aggarwal, V. Acharya, S. Schieber, M.H. Thakor, N.V.
Copyright Year	1964
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)\| finger movements (m)). Based on the ML criterion, we choose finger movements to maximize Pr(x\|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.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	7
File Size	898411
Starting Page	754
Ending Page	760
File Format	PDF
ISSN	00189294
Volume Number	57
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood decoding Fingers Neural prosthesis Maximum likelihood estimation Neurons Biomedical engineering Information technology Probability density function Wrist Prosthetics Skellam Finger movements maximum likelihood neural decoding neural prosthetics
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Correction to Asynchronous Decoding of Dexterous Finger Movements Using M1 Neurons [Feb 08 3-14]

Model-based neural decoding of reaching movements: a maximum likelihood approach

Decoding of Individuated Finger Movements Using Surface Electromyography

Asynchronous Decoding of Dexterous Finger Movements Using M1 Neurons

Decoding Individuated Finger Movements Using Volume-Constrained Neuronal Ensembles in the M1 Hand Area

Neuron Selection Based on Deflection Coefficient Maximization for the Neural Decoding of Dexterous Finger Movements

Methods for estimating neural step sequences in neural prosthetic applications

M1 neural decoding of finger movements using a priori neural activities before movements

Bit error rate estimation for turbo decoding

Neural Decoding of Finger Movements Using Skellam-Based Maximum-Likelihood Decoding

Similar Documents

Correction to Asynchronous Decoding of Dexterous Finger Movements Using M1 Neurons [Feb 08 3-14]

Model-based neural decoding of reaching movements: a maximum likelihood approach

Decoding of Individuated Finger Movements Using Surface Electromyography

Asynchronous Decoding of Dexterous Finger Movements Using M1 Neurons

Decoding Individuated Finger Movements Using Volume-Constrained Neuronal Ensembles in the M1 Hand Area

Neuron Selection Based on Deflection Coefficient Maximization for the Neural Decoding of Dexterous Finger Movements

Methods for estimating neural step sequences in neural prosthetic applications

M1 neural decoding of finger movements using a priori neural activities before movements

Bit error rate estimation for turbo decoding

Neural Decoding of Finger Movements Using Skellam-Based Maximum-Likelihood Decoding