Optimizing the Decoding of Movement Goals from Local Field Potentials in Macaque Cortex
Open accessAbstract
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 3 mm 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 bandpass 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.
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Recent studies have shown that high-density electrocorticography can decode speech representations, allowing words and short sentences to be articulated.
“More recent work has demonstrated the utility of high-density ECoG in decoding neural representations of speech, enabling the articulation of words and short sentences 6,69,99 .”
From The Layer 7 Cortical Interface: A Scalable and Minimally Invasive Brain–Computer Interface Platform (2022) · cites this paper as [99]
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- Authors
- Markowitz, D. A., Wong, Y. T., Gray, C. M., & Pesaran, B.
- Affiliations
- New York University (US)
- Montana State University (US)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2011-12-14
- Volume
- 31
- Issue
- 50
- Pages
- 18412-18422
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3315593
- OpenAlex
- W1978997151
- Cited by
- 110
- References
- 57
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 5 · 2023: 6 · 2022: 8 · 2021: 8 · 2020: 6 · 2019: 7 · 2018: 11 · 2017: 11 · 2016: 8 · 2015: 10 · 2014: 16 · 2013: 7 · 2012: 1
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (0955701)
- Burroughs Wellcome Fund
- McKnight Foundation
- National Institutes of Health
- National Institute of Mental Health (R01MH087882)
- National Eye Institute (T32EY007158)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- local field potentials, cortical depth, frontal cortex, microelectrode arrays, bandpass filtering, macaque
- MeSH terms
- Action Potentials, Animals, Goals, Macaca mulatta, Male, Memory, Motor Activity, Motor Cortex, Movement, Neurons, Saccades
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