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LiaoThesis_2014_6_30.pdf (13.66 MB)

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Evaluating Multi-Modal Brain-Computer Interfaces for Controlling Arm Movements Using a Simulator of Human Reaching

Liao, James Yu-Chang
http://rave.ohiolink.edu/etdc/view?acc_num=case1404138858

Abstract Details

2014, Doctor of Philosophy, Case Western Reserve University, Biomedical Engineering.
Brain-Computer Interfaces (BCIs) provide a potential means for individuals with tetraplegia to command arm prostheses such as Functional Electrical Stimulation (FES) systems and regain the ability to make functional movements of their arms. Many BCI implementations focus on decoding parameters of intended movement such as instantaneous position or velocity. However, specifying movements in terms of instantaneous kinematics may not be the best way to command arm prostheses to perform reaching tasks, because many reaching tasks are inherently goal-oriented. Our motivation was to explore how neurons tuned for movement goal affect the performance of BCIs in reaching tasks. We used a simulation approach to generate goal-tuned neurons and evaluated how performance varied with the number of position, velocity, and goal neurons. To accomplish this, we first developed an experimentally trained closed-loop model of human reaching movements that was capable of producing error corrections and provided a set of command signals that included position, velocity, and goal. Then, firing rates tuned for position, velocity, and goal-tuned neurons were simulated based on these commands. We implemented a decoder capable of utilizing all three information modalities. Our results suggest that goal-tuned neurons could be used to drive a BCI with enough precision to perform functional arm reaching tasks. However, the precision afforded was not as high as with velocity-tuned cells. We anticipate that our findings and the approach itself will inform future BCI research directions and ultimately improve the treatment options for individuals with tetraplegia.
Robert Kirsch, PhD (Advisor)
Dominique Durand, PhD (Committee Member)
A. Bolu Ajiboye, PhD (Committee Member)
Dawn Taylor, PhD (Committee Member)
Benjamin Walter, MD (Committee Member)
185 p.
Biomedical Engineering
motor control; brain-computer-interface; closed-loop; simulator; BCI; submovements; minimum-jerk; arm movements; motor cortex; goal; position; velocity; cognitive; neuron; neural tuning; error corrections; kinematics;

Recommended Citations

Citations

  • Liao, J. Y.-C. (2014). Evaluating Multi-Modal Brain-Computer Interfaces for Controlling Arm Movements Using a Simulator of Human Reaching [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1404138858

    APA Style (7th edition)

  • Liao, James. Evaluating Multi-Modal Brain-Computer Interfaces for Controlling Arm Movements Using a Simulator of Human Reaching. 2014. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1404138858.

    MLA Style (8th edition)

  • Liao, James. "Evaluating Multi-Modal Brain-Computer Interfaces for Controlling Arm Movements Using a Simulator of Human Reaching." Doctoral dissertation, Case Western Reserve University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1404138858

    Chicago Manual of Style (17th edition)

case1404138858
358
© 2014, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.