Robert D. Flint, Ph.D.

Affiliations: 
Drexel University, Philadelphia, PA, United States 
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Karen A. Moxon grad student 2010 Drexel
 (The neurorobotic interface for decoding a skilled hindlimb movement before and after a spinal cord injury.)
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Publications

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Flint RD, Li Y, Wang P, et al. (2022) Noninvasively recorded high-gamma signals improve synchrony of force feedback in a novel neurorehabilitation brain-machine interface for brain injury. Journal of Neural Engineering
Flint RD, Tate MC, Li K, et al. (2020) The representation of finger movement and force in human motor and premotor cortices. Eneuro
Vaidya M, Flint RD, Wang PT, et al. (2019) Hemicraniectomy in traumatic brain injury: a noninvasive platform to investigate high gamma activity for brain machine interfaces. Ieee Transactions On Neural Systems and Rehabilitation Engineering : a Publication of the Ieee Engineering in Medicine and Biology Society
Barroso FO, Yoder B, Tentler D, et al. (2019) Decoding neural activity to predict rat locomotion using intracortical and epidural arrays. Journal of Neural Engineering
Slutzky MW, Flint RD. (2017) Physiological Properties of Brain Machine Interface Input Signals. Journal of Neurophysiology. jn.00070.2017
Flint RD, Rosenow JM, Tate MC, et al. (2016) Continuous decoding of human grasp kinematics using epidural and subdural signals. Journal of Neural Engineering. 14: 016005
Flint RD, Scheid MR, Wright ZA, et al. (2016) Long-Term Stability of Motor Cortical Activity: Implications for Brain Machine Interfaces and Optimal Feedback Control. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 36: 3623-32
Flint RD, Wang PT, Wright ZA, et al. (2014) Extracting kinetic information from human motor cortical signals. Neuroimage. 101: 695-703
Mugler EM, Patton JL, Flint RD, et al. (2014) Direct classification of all American English phonemes using signals from functional speech motor cortex. Journal of Neural Engineering. 11: 035015
Flint RD, Wright ZA, Scheid MR, et al. (2013) Long term, stable brain machine interface performance using local field potentials and multiunit spikes. Journal of Neural Engineering. 10: 056005
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