Optimizing the decoding of movement goals from local field potentials in macaque cortex

David A. Markowitz, Yan T. Wong, Charles M. Gray, Bijan Pesaran

Research output: Contribution to journalArticleResearchpeer-review

64 Citations (Scopus)

Abstract

The successful development of motor neuroprosthetic devices hinges on the ability to accurately and reliably decode signals from the brain. Motor neuroprostheses are widely investigated in behaving non-human primates, but technical constraints have limited progress in optimizing performance. In particular, the organization of movement-related neuronal activity across cortical layers remains poorly understood due, in part, to the widespread use of fixed-geometry multielectrode arrays. In this study, we use chronically implanted multielectrode arrays with individually movable electrodes to examine how the encoding of movement goals depends on cortical depth. In a series of recordings spanning several months, we varied the depth of each electrode in the prearcuate gyrus of frontal cortex in two monkeys as they performed memory-guided eye movements. We decode eye movement goals from local field potentials (LFPs) and multiunit spiking activity recorded across a range of depths up to 3mm from the cortical surface. We show that both LFP and multiunit signals yield the highest decoding performance at superficial sites, within 0.5 mm of the cortical surface, while performance degrades substantially at sites deeper than 1 mm. We also analyze performance by varying band pass filtering characteristics and simulating changes in microelectrode array channel count and density. The results indicate that the performance of LFP-based neuroprostheses strongly depends on recording configuration and that recording depth is a critical parameter limiting system performance.
Original languageEnglish
Pages (from-to)18412-18422
Number of pages11
JournalJournal of Neuroscience
Volume31
Issue number50
DOIs
Publication statusPublished - 14 Dec 2011
Externally publishedYes

Cite this

Markowitz, David A. ; Wong, Yan T. ; Gray, Charles M. ; Pesaran, Bijan. / Optimizing the decoding of movement goals from local field potentials in macaque cortex. In: Journal of Neuroscience. 2011 ; Vol. 31, No. 50. pp. 18412-18422.
@article{a353af22d2f6406a8ea6ea4aaa9a5cd8,
title = "Optimizing the decoding of movement goals from local field potentials in macaque cortex",
abstract = "The successful development of motor neuroprosthetic devices hinges on the ability to accurately and reliably decode signals from the brain. Motor neuroprostheses are widely investigated in behaving non-human primates, but technical constraints have limited progress in optimizing performance. In particular, the organization of movement-related neuronal activity across cortical layers remains poorly understood due, in part, to the widespread use of fixed-geometry multielectrode arrays. In this study, we use chronically implanted multielectrode arrays with individually movable electrodes to examine how the encoding of movement goals depends on cortical depth. In a series of recordings spanning several months, we varied the depth of each electrode in the prearcuate gyrus of frontal cortex in two monkeys as they performed memory-guided eye movements. We decode eye movement goals from local field potentials (LFPs) and multiunit spiking activity recorded across a range of depths up to 3mm from the cortical surface. We show that both LFP and multiunit signals yield the highest decoding performance at superficial sites, within 0.5 mm of the cortical surface, while performance degrades substantially at sites deeper than 1 mm. We also analyze performance by varying band pass filtering characteristics and simulating changes in microelectrode array channel count and density. The results indicate that the performance of LFP-based neuroprostheses strongly depends on recording configuration and that recording depth is a critical parameter limiting system performance.",
author = "Markowitz, {David A.} and Wong, {Yan T.} and Gray, {Charles M.} and Bijan Pesaran",
year = "2011",
month = "12",
day = "14",
doi = "10.1523/JNEUROSCI.4165-11.2011",
language = "English",
volume = "31",
pages = "18412--18422",
journal = "Journal of Neuroscience",
issn = "0270-6474",
publisher = "Society for Neuroscience",
number = "50",

}

Optimizing the decoding of movement goals from local field potentials in macaque cortex. / Markowitz, David A.; Wong, Yan T.; Gray, Charles M.; Pesaran, Bijan.

In: Journal of Neuroscience, Vol. 31, No. 50, 14.12.2011, p. 18412-18422.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Optimizing the decoding of movement goals from local field potentials in macaque cortex

AU - Markowitz, David A.

AU - Wong, Yan T.

AU - Gray, Charles M.

AU - Pesaran, Bijan

PY - 2011/12/14

Y1 - 2011/12/14

N2 - The successful development of motor neuroprosthetic devices hinges on the ability to accurately and reliably decode signals from the brain. Motor neuroprostheses are widely investigated in behaving non-human primates, but technical constraints have limited progress in optimizing performance. In particular, the organization of movement-related neuronal activity across cortical layers remains poorly understood due, in part, to the widespread use of fixed-geometry multielectrode arrays. In this study, we use chronically implanted multielectrode arrays with individually movable electrodes to examine how the encoding of movement goals depends on cortical depth. In a series of recordings spanning several months, we varied the depth of each electrode in the prearcuate gyrus of frontal cortex in two monkeys as they performed memory-guided eye movements. We decode eye movement goals from local field potentials (LFPs) and multiunit spiking activity recorded across a range of depths up to 3mm from the cortical surface. We show that both LFP and multiunit signals yield the highest decoding performance at superficial sites, within 0.5 mm of the cortical surface, while performance degrades substantially at sites deeper than 1 mm. We also analyze performance by varying band pass filtering characteristics and simulating changes in microelectrode array channel count and density. The results indicate that the performance of LFP-based neuroprostheses strongly depends on recording configuration and that recording depth is a critical parameter limiting system performance.

AB - The successful development of motor neuroprosthetic devices hinges on the ability to accurately and reliably decode signals from the brain. Motor neuroprostheses are widely investigated in behaving non-human primates, but technical constraints have limited progress in optimizing performance. In particular, the organization of movement-related neuronal activity across cortical layers remains poorly understood due, in part, to the widespread use of fixed-geometry multielectrode arrays. In this study, we use chronically implanted multielectrode arrays with individually movable electrodes to examine how the encoding of movement goals depends on cortical depth. In a series of recordings spanning several months, we varied the depth of each electrode in the prearcuate gyrus of frontal cortex in two monkeys as they performed memory-guided eye movements. We decode eye movement goals from local field potentials (LFPs) and multiunit spiking activity recorded across a range of depths up to 3mm from the cortical surface. We show that both LFP and multiunit signals yield the highest decoding performance at superficial sites, within 0.5 mm of the cortical surface, while performance degrades substantially at sites deeper than 1 mm. We also analyze performance by varying band pass filtering characteristics and simulating changes in microelectrode array channel count and density. The results indicate that the performance of LFP-based neuroprostheses strongly depends on recording configuration and that recording depth is a critical parameter limiting system performance.

UR - http://www.scopus.com/inward/record.url?scp=83455179139&partnerID=8YFLogxK

U2 - 10.1523/JNEUROSCI.4165-11.2011

DO - 10.1523/JNEUROSCI.4165-11.2011

M3 - Article

VL - 31

SP - 18412

EP - 18422

JO - Journal of Neuroscience

JF - Journal of Neuroscience

SN - 0270-6474

IS - 50

ER -