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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01NS065186-05
Application #
8645764
Study Section
Neurotechnology Study Section (NT)
Program Officer
Ludwig, Kip A
Project Start
Project End
Budget Start
Budget End
Support Year
5
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Washington
Department
Neurosurgery
Type
Schools of Medicine
DUNS #
City
Seattle
State
WA
Country
United States
Zip Code
98195
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
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 128:238-51
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 127:277-84
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
Miller, Kai J; Hermes, Dora; Witthoft, Nathan et al. (2015) The physiology of perception in human temporal lobe is specialized for contextual novelty. J Neurophysiol 114:256-63
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
Ritaccio, Anthony; Brunner, Peter; Gunduz, Aysegul et al. (2014) Proceedings of the Fifth International Workshop on Advances in Electrocorticography. Epilepsy Behav 41:183-92
Blakely, Timothy; Ojemann, Jeffrey G; Rao, Rajesh P N (2014) Short-time windowed covariance: a metric for identifying non-stationary, event-related covariant cortical sites. J Neurosci Methods 222:24-33
Miller, Kai J; Honey, Christopher J; Hermes, Dora et al. (2014) Broadband changes in the cortical surface potential track activation of functionally diverse neuronal populations. Neuroimage 85 Pt 2:711-20
Blakely, Tim M; Olson, Jared D; Miller, Kai J et al. (2014) Neural correlates of learning in an electrocorticographic motor-imagery brain-computer interface. Brain Comput Interfaces (Abingdon) 1:147-157

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