Decoding Coordinated Hand Movement in Human Primary Motor Cortex Using High Resolution Electrocorticography
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Blakely, Timothy M
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Abstract
Electrophysiology studies of the brain allow us to study the dynamics of the cortex as subjects perform tasks and experiment. Of particular interest in human studies is the activity that occurs in motor related areas of the brain that occur during precise, dexterous movements of the hand - a unique property among primates. Previous studies of the central and peripheral motor nervous systems suggest the presence of synergistic activations of musculoskeletal groups during coordinated movement, and that these "motor primitives" may be present at the level of the spinal column and possibly in higher levels of CNS. In this thesis we explore the presence of these synergistic movements of individual digit joints during coordinated object grasping by leveraging high-resolution electrocorticographic (ECoG) recordings and the subsequent viability of this type of dimensionality reduction as a possible model of prosthetic control. We demonstrate that ECoG recordings are a stable and viable tool to investigate the underlying neural physiology, and explore the spatial distribution of activity during dexterous hand movement in medium (5mm-spacing) and high resolution (3mm) ECoG grids. High gamma (75-200Hz) activity in primary motor cortex shows high spatial preference for individual digit movements during overt finger flexions. In contrast, the average spatial activity during object grasping appears to show little unique spatial organization relative to the grasps performed. However, by applying a Kalman filter to predict the hand pose, we are able to accurately reconstruct the position of the hand in real-time. The Kalman filter coefficients suggest that a plausible model for motor movement may be the initiation of a common kinematic grasping motion by the first dimensional component, allowing the brain to modify this trajectory in real time as the hand approaches the object by modulating spatial networks associated with subsequent dimensions.
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Thesis (Ph.D.)--University of Washington, 2013
