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Modeling and decoding motor cortical activity using a switching Kalman filter.
IEEE Trans Biomed Eng. 2004 Jun; 51(6):933-42.IT

Abstract

We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A "hidden state" models the probability of each mixture component and evolves over time in a Markov chain. The model generalizes previous encoding and decoding methods, addresses the non-Gaussian nature of firing rates, and can cope with crudely sorted neural data common in on-line prosthetic applications.

Authors+Show Affiliations

Division of Applied Mathematics, Brown University, Providence, RI 02912, USA. weiwu@dam.brown.eduNo 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, U.S. Gov't, Non-P.H.S.
Research Support, U.S. Gov't, P.H.S.

Language

eng

PubMed ID

15188861

Citation

Wu, Wei, et al. "Modeling and Decoding Motor Cortical Activity Using a Switching Kalman Filter." IEEE Transactions On Bio-medical Engineering, vol. 51, no. 6, 2004, pp. 933-42.
Wu W, Black MJ, Mumford D, et al. Modeling and decoding motor cortical activity using a switching Kalman filter. IEEE Trans Biomed Eng. 2004;51(6):933-42.
Wu, W., Black, M. J., Mumford, D., Gao, Y., Bienenstock, E., & Donoghue, J. P. (2004). Modeling and decoding motor cortical activity using a switching Kalman filter. IEEE Transactions On Bio-medical Engineering, 51(6), 933-42.
Wu W, et al. Modeling and Decoding Motor Cortical Activity Using a Switching Kalman Filter. IEEE Trans Biomed Eng. 2004;51(6):933-42. PubMed PMID: 15188861.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Modeling and decoding motor cortical activity using a switching Kalman filter. AU - Wu,Wei, AU - Black,Michael J, AU - Mumford,David, AU - Gao,Yun, AU - Bienenstock,Elie, AU - Donoghue,John P, PY - 2004/6/11/pubmed PY - 2004/9/2/medline PY - 2004/6/11/entrez SP - 933 EP - 42 JF - IEEE transactions on bio-medical engineering JO - IEEE Trans Biomed Eng VL - 51 IS - 6 N2 - We present a switching Kalman filter model for the real-time inference of hand kinematics from a population of motor cortical neurons. Firing rates are modeled as a Gaussian mixture where the mean of each Gaussian component is a linear function of hand kinematics. A "hidden state" models the probability of each mixture component and evolves over time in a Markov chain. The model generalizes previous encoding and decoding methods, addresses the non-Gaussian nature of firing rates, and can cope with crudely sorted neural data common in on-line prosthetic applications. SN - 0018-9294 UR - https://www.unboundmedicine.com/medline/citation/15188861/Modeling_and_decoding_motor_cortical_activity_using_a_switching_Kalman_filter_ L2 - https://doi.org/10.1109/TBME.2004.826666 DB - PRIME DP - Unbound Medicine ER -