Taylor, Dawn, Ph.D.

Research Statement

Many assistive devices are available to help severely paralyzed individuals interact with the world (e.g. wheelchair-mountable robotic arms, neuroprosthetic systems that restore movement to ones own arm, assistive computer software, etc.). Severely paralyzed individuals have limited options for telling their assistive devices what movement or actions to make. My research focuses on allowing paralyzed individuals to use their natural thoughts of movement to control their assistive devices. One's intended movements can be decoded in real time from recorded brain signals and then used to control various devices, such as an upper limb neuroprosthesis for restoring arm and hand function. In this case, extracting movement commands from the brain would allow paralyzed individuals to move their limbs the same way everyone else does - just by thinking of doing so.

I am investigating ways to extract one's intended arm and hand movements from both invasive and non-invasive brain recording electrodes. Recording options include arrays of tiny microelectrodes inserted a few millimeters into the cortex, thin sheets of electrodes that sit on top of the brain, electrodes embedded in the skull, and electrodes placed either on or just beneath the scalp. We can test our algorithms for translating brain signals into intended arm movements by having individuals control a virtual model of a paralyzed arm on a computer screen. This tool allows us to refine and optimize the brain decoding algorithms before actually implementing this technology in individuals with upper limb neuroprostheses. We are also evaluating the use of brain signals to control a wheelchair-mountable assistive robot for people who are not candidates for implanted neuroprostheses. Brain decoding algorithms can also be used to enable severely paralyzed individuals control a wide variety of useful computer programs directly.

The brain has a remarkable capacity to learn new things. One important aspect of this type of work is developing ways to facilitate this relearning process so that people can learn to use their recorded brain signals more effectively. Neural retraining requires the development of an appropriate training environment and adaptive decoding algorithms that can track changes in brain pattern generation over time. Direct brain control of both real and virtual arm and hand movements are being used to refine these retraining methods.

Professional Affiliations

• Investigator, Cleveland FES Center
• Assistant Professor of Neuroscience, Cleveland Clinic Foundation, Cleveland O
• Researcher Scientist, Louis Stokes Cleveland VA Medical Center, Cleveland, OH
  
• Assistant Professor of Biomedical Engineering and  Molecular Medicine, Case Western Reserve University, Cleveland, OH
• Bioscientific Staff, Department of Orthopaedics, MetroHealth Medical Center, Cleveland, OH
  

Publications (Select)

• Foldes ST, Taylor DM. Discreet Discrete Commands for Assistive and Neuroprosthetic Devices. IEEE Trans Neural Syst Rehabil Eng. 2009 Oct 6. [Epubahead of print] PubMed PMID: 20064765.
• Marathe AR, Carey HL, Taylor DM. Virtual reality hardware and graphic display options for brain-machine interfaces. J Neurosci Methods. 2008 Jan 15;167(1):2-14. Epub 2007 Sep 29. PubMed PMID: 18006069.
• Tillery SI, Taylor DM. Signal acquisition and analysis for cortical control of neuroprosthetics. Curr Opin Neurobiol. 2004 Dec;14(6):758-62. Review. PubMed PMID: 15582380.
• Taylor DM, Tillery SI, Schwartz AB. Information conveyed through brain-control: cursor versus robot. IEEE Trans Neural Syst Rehabil Eng. 2003 Jun;11(2):195-9. PubMed PMID: 12899273.
• Taylor DM, Tillery SI, Schwartz AB. Direct cortical control of 3D neuroprosthetic devices. Science. 2002 Jun 7;296(5574):1829-32. PubMed PMID: 12052948.

Research Programs

• Intra vs. Extracortical signals for control of six dimensional movements, National Institutes of Health
• Minimally Invasive Brain Recordings for Control of FES/Robotic Systems, Dept of Veterans Affairs
• Restoration of Hand and Arm Function by Functional Neuromuscular Stimulation, National Institutes of Health
• Controller Development for Upper Limb Movement, National Institutes of Health
  

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