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.
- 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
Entire publication list can be found here.
- Bedell HW, Hermann JK, Ravikumar M, Lin S, Rein A, Li X, Molinich E, Smith PD, Selkirk SM, Miller RH, Sidik S, Taylor DM, Capadona JR. Targeting CD14 on blood derived cells improves intracortical microelectrode performance. Biomaterials. 2018 May;163:163-173. doi: 10.1016/j.biomaterials.2018.02.014. Epub 2018 Feb 13. PubMed PMID: 29471127; PubMed Central PMCID: PMC5841759.
- Hermann JK, Ravikumar M, Shoffstall AJ, Ereifej ES, Kovach KM, Chang J, Soffer A, Wong C, Srivastava V, Smith P, Protasiewicz G, Jiang J, Selkirk SM, Miller RH, Sidik S, Ziats NP, Taylor DM, Capadona JR. Inhibition of the cluster of differentiation 14 innate immunity pathway with IAXO-101 improves chronic microelectrode performance. J Neural Eng. 2018 Apr;15(2):025002. doi: 10.1088/1741-2552/aaa03e. PubMed PMID: 29219114; PubMed Central PMCID: PMC5818286.
- Jiang J, Marathe AR, Keene JC, Taylor DM. A testbed for optimizing electrodes embedded in the skull or in artificial skull replacement pieces used after injury. J Neurosci Methods. 2017 Feb 1;277:21-29. doi: 10.1016/j.jneumeth.2016.12.005. Epub 2016 Dec 12. PubMed PMID: 27979758; PubMed Central PMCID: PMC5253247.
- Jiang J, Willett FR, Taylor DM. Relationship between microelectrode array impedance and chronic recording quality of single units and local field potentials. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:3045–3048. doi: 10.1109/EMBC.2014.6944265 Pubmed PMID: 25570633
- Vadera S, Marathe AR, Gonzalez-Martinez J, Taylor DM. Stereoelectroencephalography for continuous two-dimensional cursor control in a brain-machine interface. Neurosurg Focus. 2013 Jun;34(6):E3. doi: 10.3171/2013.3.FOCUS1373 Pubmed PMID: 23724837
- Foldes ST, Taylor DM. Speaking and cognitive distractions during EEG-based brain control of a virtual neuroprosthesis-arm. J Neuroeng Rehabil. 2013;10:116. doi: 10.1186/1743-0003-10-116Pubmed PMID: 24359452
- Shoffstall AJ, Taylor DM, Lavik EB. Engineering therapies in the CNS: what works and what can be translated. Neurosci Lett. 2012 Jun 25;519(2):147–154. doi: 10.1016/j.neulet.2012.01.058Pubmed PMID: 22330751
- Chadwick EK, Blana D, Simeral JD, Lambrecht J, Kim SP, Cornwell AS, Taylor DM, Hochberg LR, Donoghue JP, Kirsch RF. Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia. J Neural Eng. 2011 Jun;8(3):034003. doi: 10.1088/1741-2560/8/3/034003Pubmed PMID: 21543840
- Muralidharan A, Chae J, Taylor DM. Extracting Attempted Hand Movements from EEGs in People with Complete Hand Paralysis Following Stroke. Front Neurosci. 2011;5:39. doi: 10.3389/fnins.2011.00039 Pubmed PMID: 21472032