Classification of spoken words using surface local field potentials.

Spencer Kellis, Kai Miller, Kyle Thomson, Richard Brown, Paul House, Bradley Greger

Research output: Contribution to journalArticle

Abstract

Cortical surface potentials recorded by electrocorticography (ECoG) have enabled robust motor classification algorithms in large part because of the close proximity of the electrodes to the cortical surface. However, standard clinical ECoG electrodes are large in both diameter and spacing relative to the underlying cortical column architecture in which groups of neurons process similar types of stimuli. The potential for surface micro-electrodes closely spaced together to provide even higher fidelity in recording surface field potentials has been a topic of recent interest in the neural prosthetic community. This study describes the classification of spoken words from surface local field potentials (LFPs) recorded using grids of subdural, nonpenetrating high impedance micro-electrodes. Data recorded from these micro-ECoG electrodes supported accurate and rapid classification. Furthermore, electrodes spaced millimeters apart demonstrated varying classification characteristics, suggesting that cortical surface LFPs may be recorded with high temporal and spatial resolution to enable even more robust algorithms for motor classification.

Original languageEnglish (US)
Pages (from-to)3827-3830
Number of pages4
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
StatePublished - 2010
Externally publishedYes

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Electrodes
Group Processes
Surface potential
Prosthetics
Electric Impedance
Neurons
Electrocorticography

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

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abstract = "Cortical surface potentials recorded by electrocorticography (ECoG) have enabled robust motor classification algorithms in large part because of the close proximity of the electrodes to the cortical surface. However, standard clinical ECoG electrodes are large in both diameter and spacing relative to the underlying cortical column architecture in which groups of neurons process similar types of stimuli. The potential for surface micro-electrodes closely spaced together to provide even higher fidelity in recording surface field potentials has been a topic of recent interest in the neural prosthetic community. This study describes the classification of spoken words from surface local field potentials (LFPs) recorded using grids of subdural, nonpenetrating high impedance micro-electrodes. Data recorded from these micro-ECoG electrodes supported accurate and rapid classification. Furthermore, electrodes spaced millimeters apart demonstrated varying classification characteristics, suggesting that cortical surface LFPs may be recorded with high temporal and spatial resolution to enable even more robust algorithms for motor classification.",
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AU - Thomson, Kyle

AU - Brown, Richard

AU - House, Paul

AU - Greger, Bradley

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AB - Cortical surface potentials recorded by electrocorticography (ECoG) have enabled robust motor classification algorithms in large part because of the close proximity of the electrodes to the cortical surface. However, standard clinical ECoG electrodes are large in both diameter and spacing relative to the underlying cortical column architecture in which groups of neurons process similar types of stimuli. The potential for surface micro-electrodes closely spaced together to provide even higher fidelity in recording surface field potentials has been a topic of recent interest in the neural prosthetic community. This study describes the classification of spoken words from surface local field potentials (LFPs) recorded using grids of subdural, nonpenetrating high impedance micro-electrodes. Data recorded from these micro-ECoG electrodes supported accurate and rapid classification. Furthermore, electrodes spaced millimeters apart demonstrated varying classification characteristics, suggesting that cortical surface LFPs may be recorded with high temporal and spatial resolution to enable even more robust algorithms for motor classification.

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