Neurological injury (such as from stroke, traumatic brain injury, and spinal cord injury) is a major cause of permanent disability. Recent advances in the field of neuroprosthetics hold enormous potential for the development of brain-computer interfaces to restore neurological function. This project will lead to a system that can control a robotic hand using recordings from the surface of the brain. Interfaces based directly from brain signals may allow for direct decoding of control signals for maximally efficient prosthetics. This project, a collaboration between neurosurgery, computer science, and physics departments, will explore the brain signals underlying hand movement using electrocorticography, or ECoG. We have previously shown that high frequency (>75Hz) components of the ECoG carry information about local brain activity. In the first aim, we will expand our understanding of the high-frequency signal components that correlate with individual finger movements. We will extract broadband changes in ECoG from non-specific alpha and beta rhythms using PCA and enhance finger classification with machine learning algorithms. In the second aim, we will look for control signals reflecting different hand functions, rather than movement of different fingers. For instance, we will examine if pinch and grasp behaviors give more separable high- frequency ECoG signals. We will also examine the behavior of these movements at higher spatial resolution. In the third aim, we will measure ECoG changes associated with imagined movement and how these changes are altered with visual feedback when applied to a robotic hand. In the final aim, we will add tactile feedback to the control to optimize ECoG-based control of a hand prosthesis. By increasingly advancing the complexity of the control signal, and the complexity of the robotic hand output, we will establish if ECoG is a viable source of control signal for a hand neuroprosthetic device.
The development of a hand neuroprosthetic, or artificial device that interacts with the nervous system could restore function to those afflicted by stroke, brain injury, spinal cord injury, or neurodegenerative diseases that have damaged the use of a hand or arm. This project examines whether signals recorded directly from the human brain (during surgery for epilepsy) could be used to control a robotic hand.
|Weaver, Kurt E; Poliakov, Andrew; Novotny, Edward J et al. (2018) Electrocorticography and the early maturation of high-frequency suppression within the default mode network. J Neurosurg Pediatr 21:133-140|
|Casimo, Kaitlyn; Levinson, Lila H; Zanos, Stavros et al. (2017) An interspecies comparative study of invasive electrophysiological functional connectivity. Brain Behav 7:e00863|
|Collins, Kelly L; Guterstam, Arvid; Cronin, Jeneva et al. (2017) Ownership of an artificial limb induced by electrical brain stimulation. Proc Natl Acad Sci U S A 114:166-171|
|Casimo, Kaitlyn; Weaver, Kurt E; Wander, Jeremiah et al. (2017) BCI Use and Its Relation to Adaptation in Cortical Networks. IEEE Trans Neural Syst Rehabil Eng 25:1697-1704|
|Miller, Kai J; Schalk, Gerwin; Hermes, Dora et al. (2016) Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Change. PLoS Comput Biol 12:e1004660|
|Darvas, Felix; Mehi?, Edin; Caler, Connor J et al. (2016) Toward Deep Brain Monitoring with Superficial EEG Sensors Plus Neuromodulatory Focused Ultrasound. Ultrasound Med Biol 42:1834-47|
|Olson, Jared D; Wander, Jeremiah D; Johnson, Lise et al. (2016) Comparison of subdural and subgaleal recordings of cortical high-gamma activity in humans. Clin Neurophysiol 127:277-284|
|Weaver, Kurt E; Wander, Jeremiah D; Ko, Andrew L et al. (2016) Directional patterns of cross frequency phase and amplitude coupling within the resting state mimic patterns of fMRI functional connectivity. Neuroimage 128:238-251|
|Brunton, Bingni W; Johnson, Lise A; Ojemann, Jeffrey G et al. (2016) Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition. J Neurosci Methods 258:1-15|
|Sun, Hai; Blakely, Timothy M; Darvas, Felix et al. (2015) Sequential activation of premotor, primary somatosensory and primary motor areas in humans during cued finger movements. Clin Neurophysiol 126:2150-61|
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