NDLI: EMG Prediction From Motor Cortical Recordings via a Nonnegative Point-Process Filter

Content Provider	IEEE Xplore Digital Library
Author	Nazarpour, K. Ethier, C. Paninski, L. Rebesco, J.M. Miall, R.C. Miller, L.E.
Copyright Year	1964
Abstract	A constrained point-process filtering mechanism for prediction of electromyogram (EMG) signals from multichannel neural spike recordings is proposed here. Filters from the Kalman family are inherently suboptimal in dealing with non-Gaussian observations, or a state evolution that deviates from the Gaussianity assumption. To address these limitations, we modeled the non-Gaussian neural spike train observations by using a generalized linear model that encapsulates covariates of neural activity, including the neurons' own spiking history, concurrent ensemble activity, and extrinsic covariates (EMG signals). In order to predict the envelopes of EMGs, we reformulated the Kalman filter in an optimization framework and utilized a nonnegativity constraint. This structure characterizes the nonlinear correspondence between neural activity and EMG signals reasonably. The EMGs were recorded from 12 forearm and hand muscles of a behaving monkey during a grip-force task. In the case of limited training data, the constrained point-process filter improved the prediction accuracy when compared to a conventional Wiener cascade filter (a linear causal filter followed by a static nonlinearity) for different bin sizes and delays between input spikes and EMG output. For longer training datasets, results of the proposed filter and that of the Wiener cascade filter were comparable.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	10
File Size	739853
Starting Page	1829
Ending Page	1838
File Format	PDF
ISSN	00189294
Volume Number	59
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electromyography Kalman filters Neurons History Muscles Delay Computational modeling optimization Brain–machine interface (BMI) electromyogram (EMG) signal generalized linear model (GLM) Kalman filter
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

EMG prediction from Motor Cortical Recordings via a Non-Negative Point Process Filter

Using the Electromyogram to Anticipate Torques About the Elbow

Entropy-Based Optimization of Wavelet Spatial Filters

BUnscented Kalman filter for brain-machine interfaces

Accessing the Neural Drive to Muscle and Translation to Neurorehabilitation Technologies

Characterization of Human Motor Units From Surface EMG Decomposition

Prediction of EMG from multiple electrode recordings in primary motor cortex

A Comparative Study to Extract the Diaphragmatic Electromyogram Signal

Co-adaptive Kalman filtering in a naive rat cortical control task

EMG Prediction From Motor Cortical Recordings via a Nonnegative Point-Process Filter

Similar Documents

EMG prediction from Motor Cortical Recordings via a Non-Negative Point Process Filter

Using the Electromyogram to Anticipate Torques About the Elbow

Entropy-Based Optimization of Wavelet Spatial Filters

BUnscented Kalman filter for brain-machine interfaces

Accessing the Neural Drive to Muscle and Translation to Neurorehabilitation Technologies

Characterization of Human Motor Units From Surface EMG Decomposition

Prediction of EMG from multiple electrode recordings in primary motor cortex

A Comparative Study to Extract the Diaphragmatic Electromyogram Signal

Co-adaptive Kalman filtering in a naive rat cortical control task

EMG Prediction From Motor Cortical Recordings via a Nonnegative Point-Process Filter