2017 - present:
Research Coordinator for the Pacific Udall Center studying Parkinson's Disease. Purpose of the study is to better characterize the motor and cognitive function as well as behavior in people with Parkinson's Disease over time.

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

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

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)


    New Orleans, LA & Mexico

  • Palo Alto YoungLife Volunteer Leader (2012 - 2014)


    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.


    Fullerton, CA

  • GLIDE Feed The Hungry Volunteer, GLIDE (2013 - 2014)


    San Francisco, CA

Personal Interests

I enjoy working with the older adult population and pediatric population, specifically people with paralysis and movement disorders.


Professional Affiliations and Activities

  • Member, Society for Neuroscience (2011 - Present)


All Publications

  • Rapid calibration of an intracortical brain-computer interface for people with tetraplegia. Journal of neural engineering Brandman, D. M., Hosman, T., Saab, J., Burkhart, M. C., Shanahan, B. E., Ciancibello, J. G., Sarma, A. A., Milstein, D. J., Vargas-Irwin, C. E., Franco, B., Kelemen, J., Blabe, C., Murphy, B. A., Young, D. R., Willett, F. R., Pandarinath, C., Stavisky, S. D., Kirsch, R. F., Walter, B. L., Bolu Ajiboye, A., Cash, S. S., Eskandar, E. N., Miller, J. P., Sweet, J. A., Shenoy, K. V., Henderson, J. M., Jarosiewicz, B., Harrison, M. T., Simeral, J. D., Hochberg, L. R. 2018; 15 (2): 026007


    Brain-computer interfaces (BCIs) can enable individuals with tetraplegia to communicate and control external devices. Though much progress has been made in improving the speed and robustness of neural control provided by intracortical BCIs, little research has been devoted to minimizing the amount of time spent on decoder calibration.We investigated the amount of time users needed to calibrate decoders and achieve performance saturation using two markedly different decoding algorithms: the steady-state Kalman filter, and a novel technique using Gaussian process regression (GP-DKF).Three people with tetraplegia gained rapid closed-loop neural cursor control and peak, plateaued decoder performance within 3 min of initializing calibration. We also show that a BCI-naïve user (T5) was able to rapidly attain closed-loop neural cursor control with the GP-DKF using self-selected movement imagery on his first-ever day of closed-loop BCI use, acquiring a target 37 s after initiating calibration.These results demonstrate the potential for an intracortical BCI to be used immediately after deployment by people with paralysis, without the need for user learning or extensive system calibration.

    View details for DOI 10.1088/1741-2552/aa9ee7

    View details for PubMedID 29363625

  • Stable long-term BCI-enabled communication in ALS and locked-in syndrome using LFP signals. Journal of neurophysiology Milekovic, T., Sarma, A. A., Bacher, D., Simeral, J. D., Saab, J., Pandarinath, C., Sorice, B. L., Blabe, C., Oakley, E. M., Tringale, K. R., Eskandar, E., Cash, S. S., Henderson, J. M., Shenoy, K. V., Donoghue, J. P., Hochberg, L. R. 2018


    Restoring communication for people with locked-in syndrome remains a challenging clinical problem without a reliable solution. Recent studies have shown that people with paralysis can use brain-computer interfaces (BCIs) based on intracortical spiking activity to efficiently type messages. However, due to neuronal signal instability, most intracortical BCIs have required frequent calibration and continuous assistance of skilled engineers to maintain performance. Here, an individual with locked-in syndrome due to brainstem stroke and an individual with tetraplegia secondary to amyotrophic lateral sclerosis (ALS) used a simple communication BCI based on intracortical local field potentials (LFPs) for 76 and 138 days, respectively, without recalibration and without significant loss of performance. BCI spelling rates of 3.07 and 6.88 correct characters/minute allowed the participants to type messages and write emails. Our results indicate that people with locked-in syndrome could soon use a slow but reliable LFP-based BCI for everyday communication without ongoing intervention from a technician or caregiver.

    View details for DOI 10.1152/jn.00493.2017

    View details for PubMedID 29694279

  • 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


    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

    View details for PubMedCentralID PMC5319839

  • 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)


    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 Web of Science ID 000390362600001

    View details for PubMedID 27900953

    View details for PubMedCentralID PMC5239755

  • 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


    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-?


    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


    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


    The prevailing view of motor cortex holds that motor cortical neural activity represents muscle or movement parameters. However, recent studies in non-human primates have shown that neural activity does not simply represent muscle or movement parameters; instead, its temporal structure is well-described by a dynamical system where activity during movement evolves lawfully from an initial pre-movement state. In this study, we analyze neuronal ensemble activity in motor cortex in two clinical trial participants diagnosed with Amyotrophic Lateral Sclerosis (ALS). We find that activity in human motor cortex has similar dynamical structure to that of non-human primates, indicating that human motor cortex contains a similar underlying dynamical system for movement generation.

    View details for DOI 10.7554/eLife.07436

    View details for Web of Science ID 000356720100001

    View details for PubMedID 26099302

    View details for PubMedCentralID PMC4475900

  • 194 High Performance Computer Cursor Control Using Neuronal Ensemble Recordings From the Motor Cortex of a Person With ALS. Neurosurgery Henderson, J. M., Gilja, V., Pandarinath, C., Blabe, C., Hochberg, L. R., Shenoy, K. V. 2013; 60: 184-?


    Chronically implanted brain-computer interface systems have been demonstrated in several human research participants, with encouraging early results. A major aim of the current project is to provide improved speed and accuracy of computer cursor control for people with paralysis.A 50-year-old woman with Amyotrophic Lateral Sclerosis (ALS) and weakness of all 4 limbs (but with some retained upper extremity function) underwent implantation of an array of 100 silicon microelectrodes into the 'hand knob' area of the precentral gyrus as part of a multi-site pilot clinical trial (Braingate2, IDE). Beginning 1 month following implantation, twice-weekly recording sessions were carried out in the participant's home. A circular cursor and several targets were displayed on a computer monitor. The participant performed a 'center-out' cursor task by moving her finger on a trackpad to acquire the targets while neural activity was recorded. This neural activity was correlated with finger movement to produce a velocity-based Kalman filter, which was in turn used to derive on-screen cursor movement from neural activity. Under neural control, the participant acquired 1 of either 4 or 8 peripheral targets, placed between 150 and 225 pixels from a central target. Each block consisted of 160 consecutive trials. Targets were acquired by touching the target with the neurally controlled cursor, with or without a required dwell time. All targets had a diameter of 100 pixels: Accuracy and acquisition time varied across 36 blocks, with more recent sessions tending toward higher performance. Best performance in the 8 target task with 250 msec dwell was 92% accuracy, with average acquisition time of 1.89 ± 1.09 seconds.Our research participant was able to acquire targets using neural control with high speed and accuracy. Optimizations are being explored to increase performance further, with the eventual goal of providing cursor control approaching that achievable by able-bodied computer users.

    View details for DOI 10.1227/01.neu.0000432784.58847.74

    View details for PubMedID 23839461

  • 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)