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Improved multi-unit decoding at the brain-machine interface using population temporal linear filtering.
J Neural Eng. 2010 Aug; 7(4):046012.JN

Abstract

Current efforts to decode control signals from multi-unit (MU) recordings rely on the use of spike sorting to differentiate neurons and the use of firing rates estimated over tens of milliseconds to reconstruct sensorimotor signals. The computational bottleneck associated with the need to identify and sort individual neuron responses poses challenges for the development of portable, real-time, neural decoding systems that can be incorporated into assistive and prosthetic devices for the disabled. Here, we investigate the ability of spike-based linear filtering to reduce computational overhead and improve the accuracy of decoding neuronal signals for populations of spiking neurons. Using a population temporal (PT) decoding framework, the speed and accuracy of spike-based MU decoding were compared with firing rate-based approaches using simulated populations of motor neurons tuned for the velocity of intended movement. For the two linear filtering approaches, the accuracy of decoded movements was examined as a function of the number of recorded neurons, amount of noise, with and without spike sorting, and for training and test motions whose statistics were either similar or dissimilar. Our results suggest that the use of a PT decoding framework can offset the loss in accuracy associated with decoding unsorted MU neural signals. Coupled with up to a 20-fold reduction in the number of decoding weights and the ability to implement the filtering in hardware, this approach could reduce the computational requirements and thus increase the portability of next generation brain-machine interfaces.

Authors+Show Affiliations

Department of Biomedical Engineering, Marquette University, PO Box 1881, Milwaukee, WI 53201-1881, USA.No affiliation info available

Pub Type(s)

Comparative Study
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

20644245

Citation

Herzfeld, D J., and S A. Beardsley. "Improved Multi-unit Decoding at the Brain-machine Interface Using Population Temporal Linear Filtering." Journal of Neural Engineering, vol. 7, no. 4, 2010, p. 046012.
Herzfeld DJ, Beardsley SA. Improved multi-unit decoding at the brain-machine interface using population temporal linear filtering. J Neural Eng. 2010;7(4):046012.
Herzfeld, D. J., & Beardsley, S. A. (2010). Improved multi-unit decoding at the brain-machine interface using population temporal linear filtering. Journal of Neural Engineering, 7(4), 046012. https://doi.org/10.1088/1741-2560/7/4/046012
Herzfeld DJ, Beardsley SA. Improved Multi-unit Decoding at the Brain-machine Interface Using Population Temporal Linear Filtering. J Neural Eng. 2010;7(4):046012. PubMed PMID: 20644245.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Improved multi-unit decoding at the brain-machine interface using population temporal linear filtering. AU - Herzfeld,D J, AU - Beardsley,S A, Y1 - 2010/07/19/ PY - 2010/7/21/entrez PY - 2010/7/21/pubmed PY - 2010/12/14/medline SP - 046012 EP - 046012 JF - Journal of neural engineering JO - J Neural Eng VL - 7 IS - 4 N2 - Current efforts to decode control signals from multi-unit (MU) recordings rely on the use of spike sorting to differentiate neurons and the use of firing rates estimated over tens of milliseconds to reconstruct sensorimotor signals. The computational bottleneck associated with the need to identify and sort individual neuron responses poses challenges for the development of portable, real-time, neural decoding systems that can be incorporated into assistive and prosthetic devices for the disabled. Here, we investigate the ability of spike-based linear filtering to reduce computational overhead and improve the accuracy of decoding neuronal signals for populations of spiking neurons. Using a population temporal (PT) decoding framework, the speed and accuracy of spike-based MU decoding were compared with firing rate-based approaches using simulated populations of motor neurons tuned for the velocity of intended movement. For the two linear filtering approaches, the accuracy of decoded movements was examined as a function of the number of recorded neurons, amount of noise, with and without spike sorting, and for training and test motions whose statistics were either similar or dissimilar. Our results suggest that the use of a PT decoding framework can offset the loss in accuracy associated with decoding unsorted MU neural signals. Coupled with up to a 20-fold reduction in the number of decoding weights and the ability to implement the filtering in hardware, this approach could reduce the computational requirements and thus increase the portability of next generation brain-machine interfaces. SN - 1741-2552 UR - https://www.unboundmedicine.com/medline/citation/20644245/Improved_multi_unit_decoding_at_the_brain_machine_interface_using_population_temporal_linear_filtering_ L2 - https://doi.org/10.1088/1741-2560/7/4/046012 DB - PRIME DP - Unbound Medicine ER -