Vikash Gilja

Summary

Affiliation: Stanford University
Country: USA

Publications

  1. ncbi request reprint An autonomous, broadband, multi-channel neural recording system for freely behaving primates
    Michael D Linderman
    Department of Electrical Engineering, Stanford University, CA, USA
    Conf Proc IEEE Eng Med Biol Soc 1:1212-5. 2006
  2. pmc A high-performance neural prosthesis enabled by control algorithm design
    Vikash Gilja
    Department of Computer Science, Stanford University, Stanford, California, USA
    Nat Neurosci 15:1752-7. 2012
  3. pmc Challenges and opportunities for next-generation intracortically based neural prostheses
    Vikash Gilja
    Department of Computer Science and SINTN, Stanford University, Stanford, CA 94305, USA
    IEEE Trans Biomed Eng 58:1891-9. 2011
  4. ncbi request reprint Multiday electrophysiological recordings from freely behaving primates
    Vikash Gilja
    Department of Computer Science, Stanford University, California, USA
    Conf Proc IEEE Eng Med Biol Soc 1:5643-6. 2006
  5. pmc Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex
    Cynthia A Chestek
    Department of Electrical Engineering, Stanford University, Stanford, CA, USA
    J Neural Eng 8:045005. 2011
  6. ncbi request reprint HermesB: a continuous neural recording system for freely behaving primates
    Gopal Santhanam
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
    IEEE Trans Biomed Eng 54:2037-50. 2007
  7. doi request reprint Neural prosthetic systems: current problems and future directions
    Cindy A Chestek
    Dept of Electrical Engineering, Stanford University, Stanford, CA, USA
    Conf Proc IEEE Eng Med Biol Soc 2009:3369-75. 2009
  8. ncbi request reprint Single-neuron stability during repeated reaching in macaque premotor cortex
    Cynthia A Chestek
    Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA
    J Neurosci 27:10742-50. 2007
  9. pmc Methods for estimating neural firing rates, and their application to brain-machine interfaces
    John P Cunningham
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305 4075, USA
    Neural Netw 22:1235-46. 2009
  10. pmc Toward optimal target placement for neural prosthetic devices
    John P Cunningham
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305 4075, USA
    J Neurophysiol 100:3445-57. 2008

Collaborators

Detail Information

Publications20

  1. ncbi request reprint An autonomous, broadband, multi-channel neural recording system for freely behaving primates
    Michael D Linderman
    Department of Electrical Engineering, Stanford University, CA, USA
    Conf Proc IEEE Eng Med Biol Soc 1:1212-5. 2006
    ..The recording system, called HermesB, is self-contained, autonomous, programmable and capable of recording broadband neural and head acceleration data to a removable compact flash card for up to 48 hours...
  2. pmc A high-performance neural prosthesis enabled by control algorithm design
    Vikash Gilja
    Department of Computer Science, Stanford University, Stanford, California, USA
    Nat Neurosci 15:1752-7. 2012
    ..Using this algorithm, we demonstrate repeatable high performance for years after implantation in two monkeys, thereby increasing the clinical viability of neural prostheses...
  3. pmc Challenges and opportunities for next-generation intracortically based neural prostheses
    Vikash Gilja
    Department of Computer Science and SINTN, Stanford University, Stanford, CA 94305, USA
    IEEE Trans Biomed Eng 58:1891-9. 2011
    ..If these challenges can be largely or fully met, intracortically based neural prostheses may achieve true clinical viability and help increasing numbers of disabled patients...
  4. ncbi request reprint Multiday electrophysiological recordings from freely behaving primates
    Vikash Gilja
    Department of Computer Science, Stanford University, California, USA
    Conf Proc IEEE Eng Med Biol Soc 1:5643-6. 2006
    ..These initial results motivate the use of such data sets for testing neural prosthetics systems and for finding the neural correlates of natural behaviors...
  5. pmc Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex
    Cynthia A Chestek
    Department of Electrical Engineering, Stanford University, Stanford, CA, USA
    J Neural Eng 8:045005. 2011
    ..This suggests that neural prosthetic systems may provide high performance over multiple years in human clinical trials...
  6. ncbi request reprint HermesB: a continuous neural recording system for freely behaving primates
    Gopal Santhanam
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
    IEEE Trans Biomed Eng 54:2037-50. 2007
    ..These results demonstrate the utility of the HermesB system and motivate using this type of system to advance neural prosthetics and electrophysiological experiments...
  7. doi request reprint Neural prosthetic systems: current problems and future directions
    Cindy A Chestek
    Dept of Electrical Engineering, Stanford University, Stanford, CA, USA
    Conf Proc IEEE Eng Med Biol Soc 2009:3369-75. 2009
    ..In all, this study suggests that research in cortically-controlled prosthetic systems may require reprioritization to achieve performance that is acceptable for a clinically viable human system...
  8. ncbi request reprint Single-neuron stability during repeated reaching in macaque premotor cortex
    Cynthia A Chestek
    Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA
    J Neurosci 27:10742-50. 2007
    ....
  9. pmc Methods for estimating neural firing rates, and their application to brain-machine interfaces
    John P Cunningham
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305 4075, USA
    Neural Netw 22:1235-46. 2009
    ..This study serves as a review of available spike train smoothers and a first quantitative comparison of their performance for brain-machine interfaces...
  10. pmc Toward optimal target placement for neural prosthetic devices
    John P Cunningham
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305 4075, USA
    J Neurophysiol 100:3445-57. 2008
    ..The optimal target placement algorithm developed here is the first algorithm of its kind, and it should both improve decode accuracy and help automate target placement for neural prostheses...
  11. doi request reprint HermesC: low-power wireless neural recording system for freely moving primates
    Cynthia A Chestek
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
    IEEE Trans Neural Syst Rehabil Eng 17:330-8. 2009
    ..The HermesC-INI3 system was used to record and telemeter one channel of broadband neural data at 15.7 kSps from a monkey performing routine daily activities in the home cage...
  12. doi request reprint A brain machine interface control algorithm designed from a feedback control perspective
    Vikash Gilja
    Dept of Computer Science, Stanford University, Stanford, CA, USA
    Conf Proc IEEE Eng Med Biol Soc 2012:1318-22. 2012
    ....
  13. pmc Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas
    Cynthia A Chestek
    Stanford Institute for Neuroinnovation and Translational Neuroscience, W100 A, James H Clark Center, Stanford University, Stanford, CA 94305 5436, USA
    J Neural Eng 10:026002. 2013
    ..We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system...
  14. pmc Factor-analysis methods for higher-performance neural prostheses
    Gopal Santhanam
    Department of Electrical Engineering, Stanford University, Stanford, California 94305 4075, USA
    J Neurophysiol 102:1315-30. 2009
    ..We propose that FA-based methods are effective in modeling correlated trial-to-trial neural variability and can be used to substantially increase overall prosthetic system performance...
  15. ncbi request reprint Increasing the performance of cortically-controlled prostheses
    Krishna V Shenoy
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
    Conf Proc IEEE Eng Med Biol Soc . 2006
    ..Taken together, these results should substantially increase the clinical viability of cortical prostheses...
  16. doi request reprint Monkey models for brain-machine interfaces: the need for maintaining diversity
    Paul Nuyujukian
    Department of Bioengineering and Stanford Medical School, Stanford University, Stanford, CA 94305, USA
    Conf Proc IEEE Eng Med Biol Soc 2011:1301-5. 2011
    ..Given the physiological diversity of neurological injury and disease, we suggest a need to maintain the current diversity of animal models and to explore additional alternatives, as each mimic different aspects of injury or disease...
  17. pmc A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces
    John P Cunningham
    Department of Electrical Engineering, Stanford University, Stanford, CA 94305 4075, USA
    J Neurophysiol 105:1932-49. 2011
    ..These findings illustrate the type of discovery made possible by the OPS, and so we hypothesize that this novel testing approach will help in the design of prosthetic systems that will translate well to human patients...
  18. pmc Autonomous head-mounted electrophysiology systems for freely behaving primates
    Vikash Gilja
    Dept of Computer Science, Stanford University, Stanford, CA 94305, USA
    Curr Opin Neurobiol 20:676-86. 2010
    ..Moving forward, this class of technologies, along with advances in neural signal processing and behavioral monitoring, have the potential to dramatically expand the scope and scale of electrophysiological studies...
  19. ncbi request reprint Neural recording stability of chronic electrode arrays in freely behaving primates
    Michael D Linderman
    Dept of Electr Eng, Stanford Univ, CA, USA
    Conf Proc IEEE Eng Med Biol Soc 1:4387-91. 2006
    ..These preliminary results suggest that spike sorting algorithms can no longer assume stable neural signals and will need to transition to adaptive signal processing methodologies to maximize performance...
  20. ncbi request reprint Electrical signals propagate unbiased in cortex
    Vikash Gilja
    Department of Computer Science, Stanford University, Stanford, CA 94305, USA
    Neuron 55:684-6. 2007
    ..However, in this issue of Neuron, Logothetis and colleagues show that signal propagation within cortex is largely unbiased across different frequencies, thus suggesting a more functional and interpretable basis of LFP coherence...