Stanford NPSL: "A High-Level, One-Slide Research Overview"

The Neural Prosthetic Systems Laboratory (NPSL; Shenoy Group / Shenoy Lab) at Stanford University conducts neuroscience (systems & cognitive neuroscience) and neuroengineering (electrical, bio, and biomedical engineering) research. The group investigates the neural basis of motor preparation and generation, and designs neural prosthetic systems to assist disabled patients. Neural prosthesis research involves desiging, building and testing medical systems which convert electrical signals from neurons in the brain into control signals for prosthetic arms and computer cursors. This central concept is illustrated in the Figure below (see brain figure at center). The surrounding figures (a-f) go into a bit more detail, as does the associated Figure caption. Recent journal papers and (peer reviewed) conference papers are also listed below; see Publications for a full listing and for PDFs.

(a, b) Front end. Electrical signals are acquired with surgically implanted BioMEMS electrode arrays (a, photo from Cyberkinetics Neurotechnology Inc.), and these signals can be wired out to exterior circuitry. Alternatively, amplification and telemetry circuits (b, photo of "INI3" integrated with a "Utah Array" from Solzbacher/Harrison/Normann Groups at U. Utah) can be integrated on chip to achieve a fully-implanted sensor system. Done in collaboration with Profs. Solzbacher, Harrison & Normann groups at U. Utah.

  • Harrison RR, Kier RJ, Kim S, Rieth L, Warren DJ, Ledbetter NM, Clark GA, Solzbacher F, Chestek CA, Gilja V, Nuyujukian, Ryu SI, Shenoy KV (2008, talk) A wireless neural interface for chronic recording. Proc. of the IEEE Biomedical Circuits and Systems Conference, special session B3L-A "Revolutionising Prosthetics Lecture", Baltimore, MD. 125-128.
  • Harrison RR, Kier RJ, Greger B, Solzbacher F, Chestek CA, Gilja V, Nuyujukian P, Ryu SI, Shenoy KV (2008, talk) Wireless neural signal acquisition with single low-power integrated circuit. Proc. of the IEEE International Symposium on Circuits and Systems (ISCAS), Seattle, WA: 1748-1751.
  • Chestek CA, Gilja V, Nuyujukian P, Ryu SI, Kier RJ, Solzbacher F, Harrison RR, Shenoy KV. (2008, talk) HermesC: RF wireless low-power neural recording system for freely behaving primates. Proc. of the IEEE International Symposium on Circuits and Systems (ISCAS), Seattle, WA: 1752-1755.
  • Lee TT, Ofer L, Cang J, Kaneko M, Stryker MP, Smith SJ, Shenoy KV, Harris JS (2006) Integrated optical sensors for chronic, minimally-invasive imaging of brain function. Proc. of the 28th Annual International Conf. of the IEEE EMBS, New York, NY: 1025-1028.
  • Watkins PT, Santhanam G, Shenoy KV, Harrison RR (2004, talk) Validation of adaptive threshold spike detector for neural recording. Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA: 4079-4082.
  • Harrison RR, Santhanam G, Shenoy KV (2004, talk) Local field potential measurement with low-power analog integrated circuit. Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA: 4067-4070.

(c, d) Signal processing hardware & algorithms. Neural prostheses require very accurate and low power signal processing algorithms for converting neural signals into useful prosthetic control signals (d). Then these algorithms can be run on light-weight mobile platforms (c), which may eventually be implanted (e.g., included on chip as in b). Done in collaboration with Prof. Teresa Meng's group at Stanford.

  • Chestek CA*, Gilja V*, Nuyujukian P, Kier R, Solzbacher F, Ryu SI, Harrison RA, Shenoy KV (2009) HermesC: Low-power wireless neural recording system for freely moving primates. IEEE TNSRE, special issue on wireless neurotechnology. In press.
  • Cunningham JP, Gilja V, Ryu SI, Shenoy KV (2009) Methods for estimating neural firing rates and their application to brain-machine interfaces. Neural Networks, special issue on brain-machine interfaces. (Feb 24, 2009) doi:10.1016/j.neunet.2009.02.004. In press.
  • Linderman MD, Santhanam G, Kemere CT, Gilja V, O'Driscoll S, Yu BM, Afshar A, Ryu SI, Shenoy KV, Meng TH (2008) Signal processing challenges for neural prostheses. IEEE Signal Processing Magazine, special issue on brain-computer interfaces. 25:18-28.
  • Santhanam G*, Linderman MD*, Gilja V, Afshar A, Ryu SI, Meng TH, Shenoy KV (2007) HermesB: A continuous neural recording system for freely behaving primates. IEEE Transactions in Biomedical Engineering. 54:2037-2050.
  • Yu BM, Kemere C, Santhanam G, Afshar A, Ryu SI, Meng TH, Sahani M*, Shenoy KV* (2007) Mixture of trajectory models for neural decoding of goal-directed movements. Journal of Neurophysiology. 97:3763-3780.
  • Zumsteg ZS, Kemere C, O'Driscoll S, Santhanam G, Ahmed RE, Shenoy KV, Meng TH (2005) Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems. IEEE Transactions in Neural Systems and Rehabilitation Engineering. 13:272-279.
  • Kemere C, Shenoy KV, Meng TH (2004) Model-based neural decoding of reaching movements: a maximum likelihood approach. IEEE Transactions on Biomedical Engineering. 51:925-932.
  • Miranda H, Gilja V, Chestek C, Shenoy KV, Meng TH (2009) A high-rate long-range wireless transmission system for multichannel neural recording applications. Proc. of the 31st Annual International Conf. of the IEEE EMBS. In press.
  • Santhanam G, Yu BM, Gilja V, Ryu SI, Afshar A, Sahani M, Shenoy KV (2008) A factor-analysis decoder for high-performance neural prostheses. Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, NV: 5208-5211.
  • Yu BM, Cunningham JP, Shenoy KV, Sahani M (2007) Neural decoding of movements: From linear to nonlinear trajectory models. Proceedings, Lecture Notes in Computer Science. Springer (Neural Information Processing, 14th International Conference, ICONIP. Kitakyushu, Japan, November 13-16).
  • Linderman MD, Gilja V, Santhanam G, Afshar A, Ryu SI, Meng TH, Shenoy KV (2006) An autonomous, broadband, multi-channel neural recording system for freely behaving primates. Proc. of the 28th Annual International Conf. of the IEEE EMBS, New York, NY: 1212-1215.
  • Gilja V, Linderman MD, Santhanam G, Afshar A, Ryu SI, Meng TH, Shenoy KV (2006, talk) Multiday electrophysiological recordings from freely behaving primates. Proc. of the 28th Annual International Conference of the IEEE EMBS, New York, NY: 5643-4656.
  • Linderman MD, Gilja V, Santhanam G, Afshar A, Ryu SI, Meng TH, Shenoy KV (2006, talk) Neural recording stability of chronic electrode arrays in freely behaving primates. Proc. of the 28th Annual International Conf. of the IEEE EMBS, New York, NY: 4387-4391.
  • O'Driscoll S, Meng TH, Shenoy KV, Kemere C (2006, talk) Neurons to Silicon: Implantable Prosthesis Processor. International Solid State Circuits Conference (ISSCC), session 30 (program number 30.1): 552-553 & 672.
  • Kemere C, Santhanam G, Yu BM, Ryu SI, Meng TH, Shenoy KV (2004) Model-based decoding of reaching movement for prosthetic systems. Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA: 4524-4528.
  • Yu BM, Ryu SI, Santhanam G, Churchland MM, Shenoy KV (2004, talk) Improving neural prosthetic system performance by combining plan and peri-movement activity. Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA: 4516-4519.
  • Zumsteg ZS, Ahmed RE, Santhanam G, Shenoy KV, Meng TH (2004) Power feasibility of implantable digital spike-sorting circuits for neural prosthetic systems. Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA: 4237-4240
  • Santhanam G, Shenoy KV (2003) Methods for estimating neural step sequences in neural prosthetic applications. Proceedings of the IEEE EMBS 1st International Conference on Neural Engineering. 344-347.
  • Kemere C, Santhanam G, Yu B, Shenoy KV, Meng T (2002, talk) Decoding of plan and peri-movement neural signals in prosthetic systems. Proceedings of the IEEE Workshop on Signal Processing Systems (SIPS'02). 276-283.

(e) System tests: We then simulate and conduct end-to-end prosthetic systems experiments to verify and increase system performance.

  • Santhanam G, Yu BM, Gilja V, Afshar A, Ryu SI, Sahani M, Shenoy KV (2009) Factor-analysis methods for higher-performance neural prostheses. Journal of Neurophysiology. (March 18, 2009). doi:10.1152/jn.00097.2009. In press.
  • Cunningham JP, Yu BM, Gilja V, Ryu SI, Shenoy KV (2008) Toward optimal target placement for neural prosthetic devices. Journal of Neurophysiology. 100:3445-3457.
  • Kemere C, Santhanam G, Yu BM, Afshar A, Ryu SI, Meng TH, Shenoy KV (2008) Detecting neural state transitions using hidden Markov models for motor cortical prostheses. Journal of Neurophysiology. 100:2441-2452.
  • Batista AP, Yu BM, Santhanam G, Ryu SI, Afshar A, Shenoy KV (2008) Cortical neural prosthesis performance improves when eye position is monitored. IEEE Transactions in Neural Systems and Rehabilitation Engineering. 16:24-31.
  • Achtman N*, Afshar A*, Santhanam G, Yu BM, Ryu SI, Shenoy KV (2007) Free paced high-performance brain-computer interfaces. Journal of Neuroengineering, 4:336-347.
  • Santhanam G*, Ryu SI*, Yu BM, Afshar A, Shenoy KV (2006) A high-performance brain-computer interface. Nature. 442:195-198.
  • Bishop W, Yu BM, Santhanam G, Afshar A, Ryu SI, Shenoy KV (2008, talk) An efficient approximation for the real-time implementation of the mixture of trajectory models decoder. Proc. of the IEEE Biomedical Circuits and Systems Conference, special session B3L-A "Revolutionising Prosthetics Lecture", Baltimore, MD. 133-136
  • Bishop W, Yu BM, Santhanam G, Afshar A, Ryu SI, Shenoy KV, Vogelstein J, Beaty J, Harshbarger S (2008) The use of a virtual integration environment for the real-time implementation of neural decode algorithms. Proc. of the 30th Annual International Conf. of the IEEE EMBS, Vancouver, British Columbia, Canada: 628-633.
  • Shenoy KV, Santhanam G, Ryu SI, Afshar A, Yu BM, Gilja V, Linderman MD, Kalmar RS, Cunningham JP, Kemere CT, Batista AP, Churchland MM, Meng TH (2006, invited talk) Increasing the performance of cortically-controlled prostheses. Proc. of the 28th Annual International Conf. of the IEEE EMBS, New York, NY: 6652-6656.
  • Cunningham JP, Yu BM, Shenoy KV (2006) Optimal target placement for neural communication prosthses. Proc. of the 28th Annual International Conf. of the IEEE EMBS, New York, NY: 2912-2915.
  • Santhanam G, Ryu SI, Yu BM, Afshar A, Shenoy KV (2005, talk) A high performance neurally-controlled cursor positioning system. IEEE Engineering in Medicine and Biology (EMBS) 2nd International Conference on Neural Engineering. 494-500.
  • Santhanam G, Sahani M, Ryu SI, Shenoy KV (2004) An extensible infrastructure for fully automated spike sorting during online experiments. Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA: 4380-4384.

(f) Basic systems neuroscience is central to all aspects of this research: only through a deep and clear understanding of neural function can high-performance, clinically-viable electronic interface systems be designed. Our aim is to understand how arm movements are prepared and executed, which might be most clearly understood in terms of dynamical systems theory and low-dimensional state spaces. Done in collaboration with Prof. Maneesh Sahani at Gatsby/UCL.

  • Churchland MM, Yu BM, Sahani M, Shenoy KV (2007) Techniques for extracting single-trial activity patterns from large-scale neural recordings. Current Opinion in Neurobiology, special issue on new technologies. 17:609-618.
  • Chestek CA*, Batista AP*, Santhanam G, Yu BM, Afshar A, Cunningham JP, Gilja V, Ryu SI, Churchland MM, Shenoy KV (2007) Single-neuron stability during repeated reaching in macaque premotor cortex. Journal of Neuroscience, 27:10742–10750.
  • Batista AP, Santhanam G, Yu BM, Ryu SI, Afshar A, Shenoy KV (2007) Reference frames for reach planning in macaque dorsal premotor cortex. Journal of Neurophysiology, 98:966-983.
  • Churchland MM, Shenoy KV (2007) Temporal complexity and heterogeneity of single-neuron activity in premotor and motor cortex. Journal of Neurophysiology. 97:4235-4257.
  • Churchland MM, Shenoy KV (2007) Delay of movement caused by disruption of cortical preparatory activity. Journal of Neurophysiology. 97:348-359.
  • Churchland MM, Afshar A, Shenoy KV (2006) A central source of movement variability. Neuron. 52:1085-1096.
  • Churchland MM, Santhanam G, Shenoy KV (2006) Preparatory activity in premotor and motor cortex reflects the speed of the upcoming reach. Journal of Neurophysiology. 96:3130-3146.
  • Churchland MM, Yu BM, Ryu SI, Santhanam G, Shenoy KV (2006) Neural variability in premotor cortex provides a signature of motor preparation. Journal of Neuroscience. 26(14):3697-3712.
  • Yu BM, Cunningham JP, Santhanam G, Ryu SI, Shenoy KV, Sahani M (2008) Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. Advances in Neural Information Processing Systems (NIPS) 21, MIT Press, Cambridge, MA. In press.
  • Cunningham JP, Sahani M, Shenoy KV (2008) Fast gaussian process methods for point process intensity estimation. Proceedings of the 25th Annual International Conference on Machine Learning (ICML 2008), Omni Press, Helsinki, Finland: 192-199.
  • Cunningham J, Yu BM, Shenoy KV, Sahani M (2008) Inferring neural firing rates from spike trains using Gaussian processes. Advances in Neural Information Processing Systems (NIPS) 20, Editors Platt J, Koller D, Singer Y, Roweis S. MIT Press, Cambridge, MA.
  • Yu BM, Shenoy KV, Sahani M (2006) Expectation propagation for inference in non-linear dynamical models with Poisson observations. Nonlinear Statistical Signal Processing Workshop, University of Cambridge, Cambridge, England.
  • Yu BM, Afshar A, Santhanam G, Ryu SI, Shenoy KV, Sahani M (2006). Extracting dynamical structure embedded in neural activity. Neural Information Processing Systems (NIPS) 18, Editors Weiss Y, Scholkopf B, Platt J. MIT Press, Cambridge, MA. 1545-1552.
  • Shenoy KV, Churchland MM, Santhanam G, Yu BM, Ryu SI (2003, invited talk) Influence of movement speed on plan activity in monkey pre-motor cortex and implications for high-performance neural prosthetic system design. Proceedings of the IEEE EMBS 25th Annual Meeting, Cancun, Mexico. 1897-1900.

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Updated: 21 March 2009