Honors & Awards


  • Career Award at the Scientific Interface, Burroughs Wellcome Fund (2019)
  • IOP Publishing Outstanding Reviewer Award, Journal of Neural Engineering (2018)
  • Interdisciplinary Postdoctoral Scholar Fellowship, Stanford Neurosciences Institute (2018)
  • Postdoctoral Research Fellowship & Career Development Award, A. P. Giannini Foundation (2018)
  • Milton Safenowitz Postdoctoral Fellowship, ALS Association (2016)
  • 1st place, BRAIN Best Student Paper Competition, 36th Annual Meeting of the IEEE Engineering in Medicine and Biology Society (2014)
  • Graduate Research Fellowship, National Science Foundation (2013)
  • IGERT Video and Poster Competition Winner, National Science Foundation (2013)
  • Outstanding Teaching Assistant Award, Stanford University School of Medicine (2013)
  • IGERT Fellowship, National Science Foundation (2011)
  • Magna Cum Laude, Brown University (2008)
  • Phi Beta Kappa, Brown University Chapter (2008)
  • Prize for Undergraduate Distinction in Neurosciences, Brown University (2008)

Boards, Advisory Committees, Professional Organizations


  • Member, IEEE Engineering in Medicine and Biology (2014 - Present)
  • Member, Society for Neuroscience (2008 - Present)

Professional Education


  • Doctor of Philosophy, Stanford University, NEURS-PHD (2016)
  • Bachelor of Science, Brown University, Neurocience (2008)

Community and International Work


  • Co-organizer, Simons Collaboration on the Global Brain West Coast Postdoc Meeting Series

    Location

    Bay Area

    Ongoing Project

    Yes

    Opportunities for Student Involvement

    No

  • “Brain Day”

    Location

    International

    Ongoing Project

    No

    Opportunities for Student Involvement

    No

  • Guest lecturer, SIMR, EXPLORE

    Location

    International

    Ongoing Project

    No

    Opportunities for Student Involvement

    No

  • Stanford Neurosciences Ph.D. Program Student Program Committee

    Location

    International

    Ongoing Project

    No

    Opportunities for Student Involvement

    No

  • Judge for “Innovate to Mitigate”

    Location

    International

    Ongoing Project

    No

    Opportunities for Student Involvement

    No

Patents


  • N Even-Chen, KV Shenoy, JC Kao, S Stavisky. "United States Patent 15/234,844 Task-outcome error signals and their use in brain-machine interfaces", Stanford University
  • D Sussillo, JC Kao, S Stavisky, KV Shenoy. "United States Patent 10,223,634 Multiplicative recurrent neural network for fast and robust intracortical brain machine interface decoders", Stanford University, Mar 5, 2019

Current Research and Scholarly Interests


I'm trying to restore movement and communication -- and thus, independence -- to people with paralysis. To do so, I'm discovering ways to use neurotechnology to allow people to communicate far more information from their brain to the outside world.

One branch of my brain-computer interface research is to read out complex (high degree-of-freedom) arm movement commands from motor areas of cortex, so that patients can make dextrous movements with, for example, a robotic arm. The goal is to provide enough range of movement that people can perform essential activities of daily living and take care of themselves.

A second branch is to build speech brain-computer interfaces by decoding the neural signals associated with trying to talk. More specifically, I'm trying to reconstruct speech from the movement commands that the brain would normally send to the lips, tongue, jaw, etc. Most work in this space has been using electrocorticography, whereas I'm using electrodes that go into the brain, where we can potentially access more information thanks to the ability to detect individual neurons' action potentials.

My Ph.D. research spanned both fundamental motor neuroscience and applied neural engineering. On the basic science side, I investigated 1) how "internal models" of how the brain's output effects the arm are used by motor cortex. 2) how sensory information carrying information about movement errors is prevented from interfering with motor cortical output until it is "ready" to generate the appropriate output; and 3) how the dynamical rules governing motor cortical activity restrict the kinds of outputs it can generate for the purpose of commanding a neural prosthesis.

My Ph.D. neural engineering work focused on 1) how to robustly decode a user's intended movement despite minute-by-minute and day-to-day changes in neural signals, and 2) sensors' gradually losing the ability to record neuronal action potential. I also studied (3) the effect of ongoing sensory feedback on this signal, and how we can exploit this information to detect and automatically correct for errors.

Graduate and Fellowship Programs


All Publications


  • Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions. Neuron Stavisky, S. D., Kao, J. C., Ryu, S. I., Shenoy, K. V. 2017; 95 (1): 195–208.e9

    Abstract

    Neural circuits must transform new inputs into outputs without prematurely affecting downstream circuits while still maintaining other ongoing communication with these targets. We investigated how this isolation is achieved in the motor cortex when macaques received visual feedback signaling a movement perturbation. To overcome limitations in estimating the mapping from cortex to arm movements, we also conducted brain-machine interface (BMI) experiments where we could definitively identify neural firing patterns as output-null or output-potent. This revealed that perturbation-evoked responses were initially restricted to output-null patterns that cancelled out at the neural population code readout and only later entered output-potent neural dimensions. This mechanism was facilitated by the circuit's large null space and its ability to strongly modulate output-potent dimensions when generating corrective movements. These results show that the nervous system can temporarily isolate portions of a circuit's activity from its downstream targets by restricting this activity to the circuit's output-null neural dimensions.

    View details for PubMedID 28625485

  • Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements (vol 8, 16357, 2018) SCIENTIFIC REPORTS Stavisky, S. D., Kao, J. C., Nuyujukian, P., Pandarinath, C., Blabe, C., Ryu, S. I., Hochberg, L. R., Henderson, J. M., Shenoy, K. V. 2019; 9
  • Principled BCI Decoder Design and Parameter Selection Using a Feedback Control Model. Scientific reports Willett, F. R., Young, D. R., Murphy, B. A., Memberg, W. D., Blabe, C. H., Pandarinath, C., Stavisky, S. D., Rezaii, P., Saab, J., Walter, B. L., Sweet, J. A., Miller, J. P., Henderson, J. M., Shenoy, K. V., Simeral, J. D., Jarosiewicz, B., Hochberg, L. R., Kirsch, R. F., Bolu Ajiboye, A. 2019; 9 (1): 8881

    Abstract

    Decoders optimized offline to reconstruct intended movements from neural recordings sometimes fail to achieve optimal performance online when they are used in closed-loop as part of an intracortical brain-computer interface (iBCI). This is because typical decoder calibration routines do not model the emergent interactions between the decoder, the user, and the task parameters (e.g. target size). Here, we investigated the feasibility of simulating online performance to better guide decoder parameter selection and design. Three participants in the BrainGate2 pilot clinical trial controlled a computer cursor using a linear velocity decoder under different gain (speed scaling) and temporal smoothing parameters and acquired targets with different radii and distances. We show that a user-specific iBCI feedback control model can predict how performance changes under these different decoder and task parameters in held-out data. We also used the model to optimize a nonlinear speed scaling function for the decoder. When used online with two participants, it increased the dynamic range of decoded speeds and decreased the time taken to acquire targets (compared to an optimized standard decoder). These results suggest that it is feasible to simulate iBCI performance accurately enough to be useful for quantitative decoder optimization and design.

    View details for DOI 10.1038/s41598-019-44166-7

    View details for PubMedID 31222030

  • Accurate Estimation of Neural Population Dynamics without Spike Sorting. Neuron Trautmann, E. M., Stavisky, S. D., Lahiri, S., Ames, K. C., Kaufman, M. T., O'Shea, D. J., Vyas, S., Sun, X., Ryu, S. I., Ganguli, S., Shenoy, K. V. 2019

    Abstract

    A central goal of systems neuroscience is to relate an organism's neural activity to behavior. Neural population analyses often reduce the data dimensionality to focus on relevant activity patterns. A major hurdle to data analysis is spike sorting, and this problem is growing as the number of recorded neurons increases. Here, we investigate whether spike sorting is necessary to estimate neural population dynamics. The theory of random projections suggests that we can accurately estimate the geometry of low-dimensional manifolds from a small number of linear projections of the data. We recorded data using Neuropixels probes in motor cortex of nonhuman primates and reanalyzed data from three previous studies and found that neural dynamics and scientific conclusions are quite similar using multiunit threshold crossings rather than sorted neurons. This finding unlocks existing data for new analyses and informs the design and use of new electrode arrays for laboratory and clinical use.

    View details for DOI 10.1016/j.neuron.2019.05.003

    View details for PubMedID 31171448

  • Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements. Scientific reports Stavisky, S. D., Kao, J. C., Nuyujukian, P., Pandarinath, C., Blabe, C., Ryu, S. I., Hochberg, L. R., Henderson, J. M., Shenoy, K. V. 2018; 8 (1): 16357

    Abstract

    Brain-machine interfaces (BMIs) that decode movement intentions should ignore neural modulation sources distinct from the intended command. However, neurophysiology and control theory suggest that motor cortex reflects the motor effector's position, which could be a nuisance variable. We investigated motor cortical correlates of BMI cursor position with or without concurrent arm movement. We show in two monkeys that subtracting away estimated neural correlates of position improves online BMI performance only if the animals were allowed to move their arm. To understand why, we compared the neural variance attributable to cursor position when the same task was performed using arm reaching, versus arms-restrained BMI use. Firing rates correlated with both BMI cursor and hand positions, but hand positional effects were greater. To examine whether BMI position influences decoding in people with paralysis, we analyzed data from two intracortical BMI clinical trial participants and performed an online decoder comparison in one participant. We found only small motor cortical correlates, which did not affect performance. These results suggest that arm movement and proprioception are the major contributors to position-related motor cortical correlates. Cursor position visual feedback is therefore unlikely to affect the performance of BMI-driven prosthetic systems being developed for people with paralysis.

    View details for PubMedID 30397281

  • Inferring single-trial neural population dynamics using sequential auto-encoders NATURE METHODS Pandarinath, C., O'Shea, D. J., Collins, J., Jozefowicz, R., Stavisky, S. D., Kao, J. C., Trautmann, E. M., Kaufman, M. T., Ryu, S. I., Hochberg, L. R., Henderson, J. M., Shenoy, K. V., Abbott, L. F., Sussillo, D. 2018; 15 (10): 805-+

    Abstract

    Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems, a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, latent factor analysis via dynamical systems accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping recording sessions spanning months to improve inference of underlying dynamics.

    View details for PubMedID 30224673

  • Neural Population Dynamics Underlying Motor Learning Transfer NEURON Vyas, S., Even-Chen, N., Stavisky, S. D., Ryu, S. I., Nuyujukian, P., Shenoy, K. V. 2018; 97 (5): 1177-+

    Abstract

    Covert motor learning can sometimes transfer to overt behavior. We investigated the neural mechanism underlying transfer by constructing a two-context paradigm. Subjects performed cursor movements either overtly using arm movements, or covertly via a brain-machine interface that moves the cursor based on motor cortical activity (in lieu of arm movement). These tasks helped evaluate whether and how cortical changes resulting from "covert rehearsal" affect overt performance. We found that covert learning indeed transfers to overt performance and is accompanied by systematic population-level changes in motor preparatory activity. Current models of motor cortical function ascribe motor preparation to achieving initial conditions favorable for subsequent movement-period neural dynamics. We found that covert and overt contexts share these initial conditions, and covert rehearsal manipulates them in a manner that persists across context changes, thus facilitating overt motor learning. This transfer learning mechanism might provide new insights into other covert processes like mental rehearsal.

    View details for PubMedID 29456026

    View details for PubMedCentralID PMC5843544

  • Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain-Computer Interfaces. IEEE transactions on bio-medical engineering Even-Chen, N., Stavisky, S. D., Pandarinath, C., Nuyujukian, P., Blabe, C. H., Hochberg, L. R., Henderson, J. M., Shenoy, K. V. 2018; 65 (8): 1771–84

    Abstract

    Brain-computer interfaces (BCIs) aim to help people with impaired movement ability by directly translating their movement intentions into command signals for assistive technologies. Despite large performance improvements over the last two decades, BCI systems still make errors that need to be corrected manually by the user. This decreases system performance and is also frustrating for the user. The deleterious effects of errors could be mitigated if the system automatically detected when the user perceives that an error was made and automatically intervened with a corrective action; thus, sparing users from having to make the correction themselves. Our previous preclinical work with monkeys demonstrated that task-outcome correlates exist in motor cortical spiking activity and can be utilized to improve BCI performance. Here, we asked if these signals also exist in the human hand area of motor cortex, and whether they can be decoded with high accuracy.We analyzed posthoc the intracortical neural activity of two BrainGate2 clinical trial participants who were neurally controlling a computer cursor to perform a grid target selection task and a keyboard-typing task.Our key findings are that: 1) there exists a putative outcome error signal reflected in both the action potentials and local field potentials of the human hand area of motor cortex, and 2) target selection outcomes can be classified with high accuracy (70-85%) of errors successfully detected with minimal (0-3%) misclassifications of success trials, based on neural activity alone.These offline results suggest that it will be possible to improve the performance of clinical intracortical BCIs by incorporating a real-time error detect-and-undo system alongside the decoding of movement intention.

    View details for DOI 10.1109/TBME.2017.2776204

    View details for PubMedID 29989931

  • 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

    Abstract

    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

  • Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model. journal of neuroscience Stavisky, S. D., Kao, J. C., Ryu, S. I., Shenoy, K. V. 2017; 37 (7): 1721-1732

    Abstract

    Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain-machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses.SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis.

    View details for DOI 10.1523/JNEUROSCI.1091-16.2016

    View details for PubMedID 28087767

    View details for PubMedCentralID PMC5320605

  • The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces EXPERIMENTAL NEUROLOGY O'Shea, D. J., Tiautmann, E., Chandrasekaran, C., Stavisky, S., Kao, J. C., Sahani, M., Ryu, S., Deisseroth, K., Shenoy, K. V. 2017; 287: 437-451
  • Augmenting intracortical brain-machine interface with neurally driven error detectors. Journal of neural engineering Even-Chen, N., Stavisky, S. D., Kao, J. C., Ryu, S. I., Shenoy, K. V. 2017; 14 (6): 066007

    Abstract

    Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs.We report here for the first time a putative outcome error signal in spiking activity within these cortices when rhesus macaques performed an intracortical BMI computer cursor task.We decoded BMI trial outcomes shortly after and even before a trial ended with 96% and 84% accuracy, respectively. This led us to develop and implement in real-time a first-of-its-kind intracortical BMI error 'detect-and-act' system that attempts to automatically 'undo' or 'prevent' mistakes. The detect-and-act system works independently and in parallel to a kinematic BMI decoder. In a challenging task that resulted in substantial errors, this approach improved the performance of a BMI employing two variants of the ubiquitous Kalman velocity filter, including a state-of-the-art decoder (ReFIT-KF).Detecting errors in real-time from the same brain regions that are commonly used to control BMIs should improve the clinical viability of BMIs aimed at restoring motor function to people with paralysis.

    View details for PubMedID 29130452

    View details for PubMedCentralID PMC5742283

  • Making brain-machine interfaces robust to future neural variability NATURE COMMUNICATIONS Sussillo, D., Stavisky, S. D., Kao, J. C., Ryu, S. I., Shenoy, K. V. 2016; 7

    Abstract

    A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to-kinematic mappings and became more robust with larger training data sets. Here we demonstrate that when tested with a non-human primate preclinical BMI model, this decoder is robust under conditions that disabled a state-of-the-art Kalman filter-based decoder. These results validate a new BMI strategy in which accumulated data history are effectively harnessed, and may facilitate reliable BMI use by reducing decoder retraining downtime.

    View details for DOI 10.1038/ncomms13749

    View details for Web of Science ID 000389627100001

    View details for PubMedID 27958268

    View details for PubMedCentralID PMC5159828

  • The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces. Experimental neurology O'Shea, D. J., Trautmann, E., Chandrasekaran, C., Stavisky, S., Kao, J. C., Sahani, M., Ryu, S., Deisseroth, K., Shenoy, K. V. 2016

    Abstract

    A central goal of neuroscience is to understand how populations of neurons coordinate and cooperate in order to give rise to perception, cognition, and action. Nonhuman primates (NHPs) are an attractive model with which to understand these mechanisms in humans, primarily due to the strong homology of their brains and the cognitively sophisticated behaviors they can be trained to perform. Using electrode recordings, the activity of one to a few hundred individual neurons may be measured electrically, which has enabled many scientific findings and the development of brain-machine interfaces. Despite these successes, electrophysiology samples sparsely from neural populations and provides little information about the genetic identity and spatial micro-organization of recorded neurons. These limitations have spurred the development of all-optical methods for neural circuit interrogation. Fluorescent calcium signals serve as a reporter of neuronal responses, and when combined with post-mortem optical clearing techniques such as CLARITY, provide dense recordings of neuronal populations, spatially organized and annotated with genetic and anatomical information. Here, we advocate that this methodology, which has been of tremendous utility in smaller animal models, can and should be developed for use with NHPs. We review here several of the key opportunities and challenges for calcium-based optical imaging in NHPs. We focus on motor neuroscience and brain-machine interface design as representative domains of opportunity within the larger field of NHP neuroscience.

    View details for DOI 10.1016/j.expneurol.2016.08.003

    View details for PubMedID 27511294

    View details for PubMedCentralID PMC5154795

  • Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Even-Chen, N., Stavisky, S. D., Kao, J. C., Ryu, S. I., Shenoy, K. V. 2015; 2015: 71-75

    Abstract

    Brain machine interfaces (BMIs) aim to assist people with paralysis by increasing their independence and ability to communicate, e.g., by using a cursor-based virtual keyboard. Current BMI clinical trials are hampered by modest performance that causes selection of wrong characters (errors) and thus reduces achieved typing rate. If it were possible to detect these errors without explicit knowledge of the task goal, this could be used to automatically "undo" wrong selections or even prevent upcoming wrong selections. We decoded imminent or recent errors during closed-loop BMI control from intracortical spiking neural activity. In our experiment, a non-human primate controlled a neurally-driven BMI cursor to acquire targets on a grid, which simulates a virtual keyboard. In offline analyses of this closed-loop BMI control data, we identified motor cortical neural signals indicative of task error occurrence. We were able to detect task outcomes (97% accuracy) and even predict upcoming task outcomes (86% accuracy) using neural activity alone. This novel strategy may help increase the performance and clinical viability of BMIs.

    View details for DOI 10.1109/EMBC.2015.7318303

    View details for PubMedID 26736203

  • A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes. Journal of neural engineering Stavisky, S. D., Kao, J. C., Nuyujukian, P., Ryu, S. I., Shenoy, K. V. 2015; 12 (3): 036009-?

    Abstract

    Brain-machine interfaces (BMIs) seek to enable people with movement disabilities to directly control prosthetic systems with their neural activity. Current high performance BMIs are driven by action potentials (spikes), but access to this signal often diminishes as sensors degrade over time. Decoding local field potentials (LFPs) as an alternative or complementary BMI control signal may improve performance when there is a paucity of spike signals. To date only a small handful of LFP decoding methods have been tested online; there remains a need to test different LFP decoding approaches and improve LFP-driven performance. There has also not been a reported demonstration of a hybrid BMI that decodes kinematics from both LFP and spikes. Here we first evaluate a BMI driven by the local motor potential (LMP), a low-pass filtered time-domain LFP amplitude feature. We then combine decoding of both LMP and spikes to implement a hybrid BMI.Spikes and LFP were recorded from two macaques implanted with multielectrode arrays in primary and premotor cortex while they performed a reaching task. We then evaluated closed-loop BMI control using biomimetic decoders driven by LMP, spikes, or both signals together.LMP decoding enabled quick and accurate cursor control which surpassed previously reported LFP BMI performance. Hybrid decoding of both spikes and LMP improved performance when spikes signal quality was mediocre to poor.These findings show that LMP is an effective BMI control signal which requires minimal power to extract and can substitute for or augment impoverished spikes signals. Use of this signal may lengthen the useful lifespan of BMIs and is therefore an important step towards clinically viable BMIs.

    View details for DOI 10.1088/1741-2560/12/3/036009

    View details for PubMedID 25946198

    View details for PubMedCentralID PMC4457459

  • Neural Point-and-Click Communication by a Person With Incomplete Locked-In Syndrome NEUROREHABILITATION AND NEURAL REPAIR Bacher, D., Jarosiewicz, B., Masse, N. Y., Stavisky, S. D., Simeral, J. D., Newell, K., Oakley, E. M., Cash, S. S., Friehs, G., Hochberg, L. R. 2015; 29 (5): 462-471

    Abstract

    A goal of brain-computer interface research is to develop fast and reliable means of communication for individuals with paralysis and anarthria. We evaluated the ability of an individual with incomplete locked-in syndrome enrolled in the BrainGate Neural Interface System pilot clinical trial to communicate using neural point-and-click control. A general-purpose interface was developed to provide control of a computer cursor in tandem with one of two on-screen virtual keyboards. The novel BrainGate Radial Keyboard was compared to a standard QWERTY keyboard in a balanced copy-spelling task. The Radial Keyboard yielded a significant improvement in typing accuracy and speed-enabling typing rates over 10 correct characters per minute. The participant used this interface to communicate face-to-face with research staff by using text-to-speech conversion, and remotely using an internet chat application. This study demonstrates the first use of an intracortical brain-computer interface for neural point-and-click communication by an individual with incomplete locked-in syndrome.

    View details for DOI 10.1177/1545968314554624

    View details for Web of Science ID 000355414600008

    View details for PubMedID 25385765

  • System identification of brain-machine interface control using a cursor jump perturbation 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER) Stavisky, S. D., Kao, J. C., Sorokin, J. M., Ryu, S. I., Shenoy, K. V. 2015

    View details for DOI 10.1109/NER.2015.7146705

  • Performance sustaining intracortical neural prostheses. Journal of neural engineering Nuyujukian, P., Kao, J. C., Fan, J. M., Stavisky, S. D., Ryu, S. I., Shenoy, K. V. 2014; 11 (6): 066003-?

    Abstract

    Objective. Neural prostheses, or brain-machine interfaces, aim to restore efficient communication and movement ability to those suffering from paralysis. A major challenge these systems face is robust performance, particularly with aging signal sources. The aim in this study was to develop a neural prosthesis that could sustain high performance in spite of signal instability while still minimizing retraining time. Approach. We trained two rhesus macaques implanted with intracortical microelectrode arrays 1-4 years prior to this study to acquire targets with a neurally-controlled cursor. We measured their performance via achieved bitrate (bits per second, bps). This task was repeated over contiguous days to evaluate the sustained performance across time. Main results. We found that in the monkey with a younger (i.e., two year old) implant and better signal quality, a fixed decoder could sustain performance for a month at a rate of 4 bps, the highest achieved communication rate reported to date. This fixed decoder was evaluated across 22 months and experienced a performance decline at a rate of 0.24 bps yr(-1). In the monkey with the older (i.e., 3.5 year old) implant and poorer signal quality, a fixed decoder could not sustain performance for more than a few days. Nevertheless, performance in this monkey was maintained for two weeks without requiring additional online retraining time by utilizing prior days' experimental data. Upon analysis of the changes in channel tuning, we found that this stability appeared partially attributable to the cancelling-out of neural tuning fluctuations when projected to two-dimensional cursor movements. Significance. The findings in this study (1) document the highest-performing communication neural prosthesis in monkeys, (2) confirm and extend prior reports of the stability of fixed decoders, and (3) demonstrate a protocol for system stability under conditions where fixed decoders would otherwise fail. These improvements to decoder stability are important for minimizing training time and should make neural prostheses more practical to use.

    View details for DOI 10.1088/1741-2560/11/6/066003

    View details for PubMedID 25307561

  • Non-causal spike filtering improves decoding of movement intention for intracortical BCIs JOURNAL OF NEUROSCIENCE METHODS Masse, N. Y., Jarosiewicz, B., Simeral, J. D., Bacher, D., Stavisky, S. D., Cash, S. S., Oakley, E. M., Berhanu, E., Eskandar, E., Friehs, G., Hochberg, L. R., Donoghue, J. P. 2014; 236: 58-67

    Abstract

    Multiple types of neural signals are available for controlling assistive devices through brain-computer interfaces (BCIs). Intracortically recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on "sorting" action potentials.We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4ms lag between recording and filtering neural signals.Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant's intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study.Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs.

    View details for DOI 10.1016/j.jneumeth.2014.08.004

    View details for Web of Science ID 000343390700008

    View details for PubMedID 25128256

  • Information Systems Opportunities in Brain-Machine Interface Decoders PROCEEDINGS OF THE IEEE Kao, J. C., Stavisky, S. D., Sussillo, D., Nuyujukian, P., Shenoy, K. V. 2014; 102 (5): 666-682
  • Hybrid decoding of both spikes and low-frequency local field potentials for brain-machine interfaces. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference Stavisky, S. D., Kao, J. C., Nuyujukian, P., Ryu, S. I., Shenoy, K. V. 2014; 2014: 3041-3044

    Abstract

    The best-performing brain-machine interfaces (BMIs) to date decode movement intention from intracortically recorded spikes, but these signals may be lost over time. A way to increase the useful lifespan of BMIs is to make more comprehensive use of available neural signals. Recent studies have demonstrated that the local field potential (LFP), a potentially more robust signal, can also be used to control a BMI. However, LFP-driven performance has fallen short of the best spikes-driven performance. Here we report a biomimetic BMI driven by low-frequency LFP that enabled a rhesus monkey to acquire and hold randomly placed targets with 99% success rate. Although LFP-driven performance was still worse than when decoding spikes, to the best of our knowledge this represents the highest-performing LFP-based BMI. We also demonstrate a new hybrid BMI that decodes cursor velocity using both spikes and LFP. This hybrid decoder improved performance over spikes-only decoding. Our results suggest that LFP can complement spikes when spikes are available or provide an alternative control signal if spikes are absent.

    View details for DOI 10.1109/EMBC.2014.6944264

    View details for PubMedID 25570632

  • Continuous Control of the DLR Light-Weight Robot III by a Human with Tetraplegia Using the BrainGate2 Neural Interface System Experimental Robotics Vogel, J., Haddadin, S., Simeral, J. D., Stavisky, S. D., Bacher, D., Hochberg, L. R., Donoghue, J. P., van der Smagt, P. Springer. 2014: 125–136
  • Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Kao, J. C., Nuyujukian, P., Stavisky, S., Ryu, S. I., Ganguli, S., Shenoy, K. V. 2013; 2013: 293-298

    Abstract

    The intraday robustness of brain-machine interfaces (BMIs) is important to their clinical viability. In particular, BMIs must be robust to intraday perturbations in neuron firing rates, which may arise from several factors including recording loss and external noise. Using a state-of-the-art decode algorithm, the Recalibrated Feedback Intention Trained Kalman filter (ReFIT-KF) [1] we introduce two novel modifications: (1) a normalization of the firing rates, and (2) a reduction of the dimensionality of the data via principal component analysis (PCA). We demonstrate in online studies that a ReFIT-KF equipped with normalization and PCA (NPC-ReFIT-KF) (1) achieves comparable performance to a standard ReFIT-KF when at least 60% of the neural variance is captured, and (2) is more robust to the undetected loss of channels. We present intuition as to how both modifications may increase the robustness of BMIs, and investigate the contribution of each modification to robustness. These advances, which lead to a decoder achieving state-of-the-art performance with improved robustness, are important for the clinical viability of BMI systems.

    View details for DOI 10.1109/EMBC.2013.6609495

    View details for PubMedID 24109682

  • A recurrent neural network for closed-loop intracortical brain-machine interface decoders JOURNAL OF NEURAL ENGINEERING Sussillo, D., Nuyujukian, P., Fan, J. M., Kao, J. C., Stavisky, S. D., Ryu, S., Shenoy, K. 2012; 9 (2)

    Abstract

    Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships in time series data with complex temporal dependences. In this paper, we explore the ability of a simplified type of RNN, one with limited modifications to the internal weights called an echostate network (ESN), to effectively and continuously decode monkey reaches during a standard center-out reach task using a cortical brain-machine interface (BMI) in a closed loop. We demonstrate that the RNN, an ESN implementation termed a FORCE decoder (from first order reduced and controlled error learning), learns the task quickly and significantly outperforms the current state-of-the-art method, the velocity Kalman filter (VKF), using the measure of target acquire time. We also demonstrate that the FORCE decoder generalizes to a more difficult task by successfully operating the BMI in a randomized point-to-point task. The FORCE decoder is also robust as measured by the success rate over extended sessions. Finally, we show that decoded cursor dynamics are more like naturalistic hand movements than those of the VKF. Taken together, these results suggest that RNNs in general, and the FORCE decoder in particular, are powerful tools for BMI decoder applications.

    View details for DOI 10.1088/1741-2560/9/2/026027

    View details for Web of Science ID 000302144100027

    View details for PubMedID 22427488

    View details for PubMedCentralID PMC3638090