Bio

Bio


2017:
Research Coordinator for the Pacific Udall Center studying Parkinson's Disease.

2010-2017:
Clinical Neurotechnology Research Assistant for the BrainGate2 clinical trials at Stanford, a team working to develop and test novel neurotechnologies to help people with neurologic disease, injury, or limb loss.

Research assistant for Stanford's Neural Prosthetics Translational Laboratory (NPTL). A team that conducts research aimed at providing clinically useful neural prostheses for people with paralysis.

Current Role at Stanford


2017
Clinical Research Coordinator II Pacific Udall Center studying Parkinson's Disease

2010-2017
Research Assistant, Neurosurgery

Clinical Neurotechnologist for the BrainGate2 clinical trials

Education & Certifications


  • M.S., CSUFullerton, Kinesiology (2009)
  • B.S., CSUFullerton, Kinesiology (2005)

Service, Volunteer and Community Work


  • Home Builder Habitat for Humanity, Habitat for Humanity (2000 - 2010)

    Location

    New Orleans, LA & Mexico

  • Palo Alto YoungLife Volunteer Leader (2012 - 2014)

    Location

    Palo Alto, CA

  • St. Jude Medical Center Volunteer, St. Jude Medical Center (January 2000 - July 2009)

    Visited older adults in their homes, served as a companion and did tasks for them. Volunteer of the Month, November 2002.

    Location

    Fullerton, CA

Professional

Professional Interests


I enjoy working with people with paralysis and movement disorders.

Professional Affiliations and Activities


  • Member, Society for Neuroscience (2011 - Present)

Publications

All Publications


  • High performance communication by people with paralysis using an intracortical brain-computer interface. eLife Pandarinath, C., Nuyujukian, P., Blabe, C. H., Sorice, B. L., Saab, J., Willett, F. R., Hochberg, L. R., Shenoy, K. V., Henderson, J. M. 2017; 6

    Abstract

    Brain-computer interfaces (BCIs) have the potential to restore communication for people with tetraplegia and anarthria by translating neural activity into control signals for assistive communication devices. While previous pre-clinical and clinical studies have demonstrated promising proofs-of-concept (Serruya et al., 2002; Simeral et al., 2011; Bacher et al., 2015; Nuyujukian et al., 2015; Aflalo et al., 2015; Gilja et al., 2015; Jarosiewicz et al., 2015; Wolpaw et al., 1998; Hwang et al., 2012; Spüler et al., 2012; Leuthardt et al., 2004; Taylor et al., 2002; Schalk et al., 2008; Moran, 2010; Brunner et al., 2011; Wang et al., 2013; Townsend and Platsko, 2016; Vansteensel et al., 2016; Nuyujukian et al., 2016; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Hochberg et al., 2006; Ganguly et al., 2011; O'Doherty et al., 2011; Gilja et al., 2012), the performance of human clinical BCI systems is not yet high enough to support widespread adoption by people with physical limitations of speech. Here we report a high-performance intracortical BCI (iBCI) for communication, which was tested by three clinical trial participants with paralysis. The system leveraged advances in decoder design developed in prior pre-clinical and clinical studies (Gilja et al., 2015; Kao et al., 2016; Gilja et al., 2012). For all three participants, performance exceeded previous iBCIs (Bacher et al., 2015; Jarosiewicz et al., 2015) as measured by typing rate (by a factor of 1.4-4.2) and information throughput (by a factor of 2.2-4.0). This high level of performance demonstrates the potential utility of iBCIs as powerful assistive communication devices for people with limited motor function.Clinical Trial No: NCT00912041.

    View details for DOI 10.7554/eLife.18554

    View details for PubMedID 28220753

  • Feedback control policies employed by people using intracortical brain-computer interfaces. Journal of neural engineering Willett, F. R., Pandarinath, C., Jarosiewicz, B., Murphy, B. A., Memberg, W. D., Blabe, C. H., Saab, J., Walter, B. L., Sweet, J. A., Miller, J. P., Henderson, J. M., Shenoy, K. V., Simeral, J. D., Hochberg, L. R., Kirsch, R. F., Ajiboye, A. B. 2017; 14 (1): 016001

    Abstract

    When using an intracortical BCI (iBCI), users modulate their neural population activity to move an effector towards a target, stop accurately, and correct for movement errors. We call the rules that govern this modulation a 'feedback control policy'. A better understanding of these policies may inform the design of higher-performing neural decoders.We studied how three participants in the BrainGate2 pilot clinical trial used an iBCI to control a cursor in a 2D target acquisition task. Participants used a velocity decoder with exponential smoothing dynamics. Through offline analyses, we characterized the users' feedback control policies by modeling their neural activity as a function of cursor state and target position. We also tested whether users could adapt their policy to different decoder dynamics by varying the gain (speed scaling) and temporal smoothing parameters of the iBCI.We demonstrate that control policy assumptions made in previous studies do not fully describe the policies of our participants. To account for these discrepancies, we propose a new model that captures (1) how the user's neural population activity gradually declines as the cursor approaches the target from afar, then decreases more sharply as the cursor comes into contact with the target, (2) how the user makes constant feedback corrections even when the cursor is on top of the target, and (3) how the user actively accounts for the cursor's current velocity to avoid overshooting the target. Further, we show that users can adapt their control policy to decoder dynamics by attenuating neural modulation when the cursor gain is high and by damping the cursor velocity more strongly when the smoothing dynamics are high.Our control policy model may help to build better decoders, understand how neural activity varies during active iBCI control, and produce better simulations of closed-loop iBCI movements.

    View details for DOI 10.1088/1741-2560/14/1/016001

    View details for PubMedID 27900953

  • Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts' law. Journal of neural engineering Willett, F. R., Murphy, B. A., Memberg, W. D., Blabe, C. H., Pandarinath, C., Walter, B. L., Sweet, J. A., Miller, J. P., Henderson, J. M., Shenoy, K. V., Hochberg, L. R., Kirsch, R. F., Ajiboye, A. B. 2017; 14 (2): 026010

    Abstract

    Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts' law: [Formula: see text] (where MT is movement time, D is target distance, R is target radius, and [Formula: see text] are parameters). Fitts' law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio [Formula: see text]) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to [Formula: see text]).Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts' law.We found that movement times were better described by the equation [Formula: see text], which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the [Formula: see text] ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user's motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder.The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts' law-like relationship to iBCI movements may require non-linear decoding strategies.

    View details for DOI 10.1088/1741-2552/aa5990

    View details for PubMedID 28177925

  • Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. Science translational medicine Jarosiewicz, B., Sarma, A. A., Bacher, D., Masse, N. Y., Simeral, J. D., Sorice, B., Oakley, E. M., Blabe, C., Pandarinath, C., Gilja, V., Cash, S. S., Eskandar, E. N., Friehs, G., Henderson, J. M., Shenoy, K. V., Donoghue, J. P., Hochberg, L. R. 2015; 7 (313): 313ra179-?

    Abstract

    Brain-computer interfaces (BCIs) promise to restore independence for people with severe motor disabilities by translating decoded neural activity directly into the control of a computer. However, recorded neural signals are not stationary (that is, can change over time), degrading the quality of decoding. Requiring users to pause what they are doing whenever signals change to perform decoder recalibration routines is time-consuming and impractical for everyday use of BCIs. We demonstrate that signal nonstationarity in an intracortical BCI can be mitigated automatically in software, enabling long periods (hours to days) of self-paced point-and-click typing by people with tetraplegia, without degradation in neural control. Three key innovations were included in our approach: tracking the statistics of the neural activity during self-timed pauses in neural control, velocity bias correction during neural control, and periodically recalibrating the decoder using data acquired during typing by mapping neural activity to movement intentions that are inferred retrospectively based on the user's self-selected targets. These methods, which can be extended to a variety of neurally controlled applications, advance the potential for intracortical BCIs to help restore independent communication and assistive device control for people with paralysis.

    View details for DOI 10.1126/scitranslmed.aac7328

    View details for PubMedID 26560357

  • Clinical translation of a high-performance neural prosthesis. Nature medicine Gilja, V., Pandarinath, C., Blabe, C. H., Nuyujukian, P., Simeral, J. D., Sarma, A. A., Sorice, B. L., Perge, J. A., Jarosiewicz, B., Hochberg, L. R., Shenoy, K. V., Henderson, J. M. 2015; 21 (10): 1142-1145

    Abstract

    Neural prostheses have the potential to improve the quality of life of individuals with paralysis by directly mapping neural activity to limb- and computer-control signals. We translated a neural prosthetic system previously developed in animal model studies for use by two individuals with amyotrophic lateral sclerosis who had intracortical microelectrode arrays placed in motor cortex. Measured more than 1 year after implant, the neural cursor-control system showed the highest published performance achieved by a person to date, more than double that of previous pilot clinical trial participants.

    View details for DOI 10.1038/nm.3953

    View details for PubMedID 26413781

  • Assessment of brain-machine interfaces from the perspective of people with paralysis JOURNAL OF NEURAL ENGINEERING Blabe, C. H., Gilja, V., Chestek, C. A., Shenoy, K. V., Anderson, K. D., Henderson, J. M. 2015; 12 (4)
  • Neural population dynamics in human motor cortex during movements in people with ALS ELIFE Pandarinath, C., Gilja, V., Blabe, C. H., Nuyujukian, P., Sarma, A. A., Sorice, B. L., Eskandar, E. N., Hochberg, L. R., Henderson, J. M., Shenoy, K. V. 2015; 4
  • Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas JOURNAL OF NEURAL ENGINEERING Chestek, C. A., Gilja, V., Blabe, C. H., Foster, B. L., Shenoy, K. V., Parvizi, J., Henderson, J. M. 2013; 10 (2)

Footer Links:

Stanford Medicine Resources: