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Sensorimotor control and the involvement of motor brain regions has been extensively studied, but the role nonmotor brain regions play during movements has been overlooked. This is particularly due to the difficulty of recording from multiple regions in the brain during motor control. In this study, we utilize stereoelectroencephalography (SEEG) recording techniques to explore the role nonmotor brain areas have on the way we move. Nine humans were implanted with SEEG depth electrodes for clinical purposes, which rendered access to local field potential (LFP) activity in deep and peripheral nonmotor structures. Participants performed fast and slow arm reaching movements using a robotic manipulandum. In this study, we explored whether neural activity in a given nonmotor brain structure correlated to movement path metrics including: path length, path deviation, and path speed. Statistical analysis revealed correlations between averaged neural activity in middle temporal gyrus, supramarginal gyrus, and fusiform gyrus and our path metrics both within and across the subjects. Furthermore, we split trials across subjects into two groups: one group consisted of trials with high values of each path metric and the other with low values. We then found significant differences in LFP power in specific frequency bands (e.g. beta) during movement between each group. These results suggest that nonmotor regions may dynamically encode path-related information during movement.

Neural prostheses have generally relied on signals from cortical motor regions to control reaching movements of a robotic arm. However, little work has been done in exploring the involvement of nonmotor cortical and associative regions during motor tasks. In this study, we identify regions which may encode direction during planning and movement of a center-out motor task. Local field potentials were collected using stereoelectroencephalography (SEEG) from nine epilepsy patients implanted with multiple depth electrodes for clinical purposes. Spectral analysis of the recorded data was performed using nonparametric statistical techniques to identify regions that may encode direction of movements during the motor task. The analysis revealed several nonmotor regions; including the right insular cortex, right temporal pole, right superior parietal lobule, and the right lingual gyrus, that encode directionality before and after movement onset. We observed that each of these regions encode direction in different frequency bands. This preliminary study suggests that nonmotor regions may be useful in assisting in neural prosthetic control.

This paper introduces Chance Constrained Gaussian Process-Motion Planning (CCGP-MP), a motion planning algorithm for robotic systems under motion and state estimate uncertainties. The paper's key idea is to capture the variations in the distance-to-collision measurements caused by the uncertainty in state estimation techniques using a Gaussian Process (GP) model. We formulate the planning problem as a chance constraint problem and propose a deterministic constraint that uses the modeled distance function to verify the chance-constraints. We apply Simplicial Homology Global Optimization (SHGO) approach to find the global minimum of the deterministic constraint function along the trajectory and use the minimum value to verify the chance-constraints. Under this formulation, we can show that the optimization function is smooth under certain conditions and that SHGO converges to the global minimum. Therefore, CCGP-MP will always guarantee that all points on a planned trajectory satisfy the given chance-constraints. The experiments in this paper show that CCGP-MP can generate paths that reduce collisions and meet optimality criteria under motion and state uncertainties. The implementation of our robot models and path planning algorithm can be found on GitHub 1 .

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