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Statistical encoding model for a primary motor cortical brain-machine interface.
IEEE Trans Biomed Eng. 2005 Jul; 52(7):1312-22.IT

Abstract

A number of studies of the motor system suggest that the majority of primary motor cortical neurons represent simple movement-related kinematic and dynamic quantities in their time-varying activity patterns. An example of such an encoding relationship is the cosine tuning of firing rate with respect to the direction of hand motion. We present a systematic development of statistical encoding models for movement-related motor neurons using multielectrode array recordings during a two-dimensional (2-D) continuous pursuit-tracking task. Our approach avoids massive averaging of responses by utilizing 2-D normalized occupancy plots, cascaded linear-nonlinear (LN) system models and a method for describing variability in discrete random systems. We found that the expected firing rate of most movement-related motor neurons is related to the kinematic values by a linear transformation, with a significant nonlinear distortion in about 1/3 of the neurons. The measured variability of the neural responses is markedly non-Poisson in many neurons and is well captured by a "normalized-Gaussian" statistical model that is defined and introduced here. The statistical model is seamlessly integrated into a nearly-optimal recursive method for decoding movement from neural responses based on a Sequential Monte Carlo filter.

Authors+Show Affiliations

Faculty of Biomedical Engineering, the Technion, Israel Institute of Technology, Haifa 32000, Israel. sshoham@bm.technion.ac.ilNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Evaluation Study
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.
Validation Study

Language

eng

PubMed ID

16041995

Citation

Shoham, Shy, et al. "Statistical Encoding Model for a Primary Motor Cortical Brain-machine Interface." IEEE Transactions On Bio-medical Engineering, vol. 52, no. 7, 2005, pp. 1312-22.
Shoham S, Paninski LM, Fellows MR, et al. Statistical encoding model for a primary motor cortical brain-machine interface. IEEE Trans Biomed Eng. 2005;52(7):1312-22.
Shoham, S., Paninski, L. M., Fellows, M. R., Hatsopoulos, N. G., Donoghue, J. P., & Normann, R. A. (2005). Statistical encoding model for a primary motor cortical brain-machine interface. IEEE Transactions On Bio-medical Engineering, 52(7), 1312-22.
Shoham S, et al. Statistical Encoding Model for a Primary Motor Cortical Brain-machine Interface. IEEE Trans Biomed Eng. 2005;52(7):1312-22. PubMed PMID: 16041995.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Statistical encoding model for a primary motor cortical brain-machine interface. AU - Shoham,Shy, AU - Paninski,Liam M, AU - Fellows,Matthew R, AU - Hatsopoulos,Nicholas G, AU - Donoghue,John P, AU - Normann,Richard A, PY - 2005/7/27/pubmed PY - 2005/8/24/medline PY - 2005/7/27/entrez SP - 1312 EP - 22 JF - IEEE transactions on bio-medical engineering JO - IEEE Trans Biomed Eng VL - 52 IS - 7 N2 - A number of studies of the motor system suggest that the majority of primary motor cortical neurons represent simple movement-related kinematic and dynamic quantities in their time-varying activity patterns. An example of such an encoding relationship is the cosine tuning of firing rate with respect to the direction of hand motion. We present a systematic development of statistical encoding models for movement-related motor neurons using multielectrode array recordings during a two-dimensional (2-D) continuous pursuit-tracking task. Our approach avoids massive averaging of responses by utilizing 2-D normalized occupancy plots, cascaded linear-nonlinear (LN) system models and a method for describing variability in discrete random systems. We found that the expected firing rate of most movement-related motor neurons is related to the kinematic values by a linear transformation, with a significant nonlinear distortion in about 1/3 of the neurons. The measured variability of the neural responses is markedly non-Poisson in many neurons and is well captured by a "normalized-Gaussian" statistical model that is defined and introduced here. The statistical model is seamlessly integrated into a nearly-optimal recursive method for decoding movement from neural responses based on a Sequential Monte Carlo filter. SN - 0018-9294 UR - https://www.unboundmedicine.com/medline/citation/16041995/Statistical_encoding_model_for_a_primary_motor_cortical_brain_machine_interface_ L2 - https://doi.org/10.1109/TBME.2005.847542 DB - PRIME DP - Unbound Medicine ER -