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Model-based neural decoding of reaching movements: a maximum likelihood approach.
IEEE Trans Biomed Eng. 2004 Jun; 51(6):925-32.IT

Abstract

A new paradigm for decoding reaching movements from the signals of an ensemble of individual neurons is presented. This new method not only provides a novel theoretical basis for the task, but also results in a significant decrease in the error of reconstructed hand trajectories. By using a model of movement as a foundation for the decoding system, we show that the number of neurons required for reconstruction of the trajectories of point-to-point reaching movements in two dimensions can be halved. Additionally, using the presented framework, other forms of neural information, specifically neural "plan" activity, can be integrated into the trajectory decoding process. The decoding paradigm presented is tested in simulation using a database of experimentally gathered center-out reaches and corresponding neural data generated from synthetic models.

Authors+Show Affiliations

Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA. ckemere@mojave.stanford.eduNo affiliation info availableNo affiliation info available

Pub Type(s)

Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

15188860

Citation

Kemere, Caleb, et al. "Model-based Neural Decoding of Reaching Movements: a Maximum Likelihood Approach." IEEE Transactions On Bio-medical Engineering, vol. 51, no. 6, 2004, pp. 925-32.
Kemere C, Shenoy KV, Meng TH. Model-based neural decoding of reaching movements: a maximum likelihood approach. IEEE Trans Biomed Eng. 2004;51(6):925-32.
Kemere, C., Shenoy, K. V., & Meng, T. H. (2004). Model-based neural decoding of reaching movements: a maximum likelihood approach. IEEE Transactions On Bio-medical Engineering, 51(6), 925-32.
Kemere C, Shenoy KV, Meng TH. Model-based Neural Decoding of Reaching Movements: a Maximum Likelihood Approach. IEEE Trans Biomed Eng. 2004;51(6):925-32. PubMed PMID: 15188860.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Model-based neural decoding of reaching movements: a maximum likelihood approach. AU - Kemere,Caleb, AU - Shenoy,Krishna V, AU - Meng,Teresa H, PY - 2004/6/11/pubmed PY - 2004/9/2/medline PY - 2004/6/11/entrez SP - 925 EP - 32 JF - IEEE transactions on bio-medical engineering JO - IEEE Trans Biomed Eng VL - 51 IS - 6 N2 - A new paradigm for decoding reaching movements from the signals of an ensemble of individual neurons is presented. This new method not only provides a novel theoretical basis for the task, but also results in a significant decrease in the error of reconstructed hand trajectories. By using a model of movement as a foundation for the decoding system, we show that the number of neurons required for reconstruction of the trajectories of point-to-point reaching movements in two dimensions can be halved. Additionally, using the presented framework, other forms of neural information, specifically neural "plan" activity, can be integrated into the trajectory decoding process. The decoding paradigm presented is tested in simulation using a database of experimentally gathered center-out reaches and corresponding neural data generated from synthetic models. SN - 0018-9294 UR - https://www.unboundmedicine.com/medline/citation/15188860/Model_based_neural_decoding_of_reaching_movements:_a_maximum_likelihood_approach_ L2 - https://doi.org/10.1109/TBME.2004.826675 DB - PRIME DP - Unbound Medicine ER -