Classification of spoken words using surface local field potentials

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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