Jose M. Carmena, Ph.D.
Assistant Professor
Dept. of Electrical Engineering & Computer Sciences
Helen Wills Neuroscience Institute
Program in Cognitive Science
University of California, Berkeley
517 Cory Hall, MC # 1770
Berkeley, CA 94720

Phone: (510) 643 2430
Fax: (510) 642 5745
BMI Systems Lab @ University of California, Berkeley
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Other affiliations

Teaching

Education

Research Interests


My research focuses on the neurophysiological, engineering and clinical implications of the interface between brains and machines. The brain-machine interface (BMI) paradigm contends that a user can perceive sensory information and enact voluntary motor actions through a direct interface between the brain and an artificial actuator in virtually the same way that we see, walk or grab an object with our own natural limbs.  Proficient brain-control relies on the strong coupling between the brain and the machine, achieved through training with any combination of visual, tactile, or auditory feedback. As a result of long-term use of the BMI, the brain adapts to the artificial actuator by incorporating its dynamic and physical properties into a somatosensory representation.

During my postdoctoral work in the Nicolelis laboratory at Duke University I recorded from large ensembles of neurons across multiple cortical areas using arrays of chronically implanted microelectrodes. The main asset of using this technique within the BMI context is the visualization of neural circuit function through spatiotemporal patterns of neural activity while subjects perform behavioral tasks in both manual and brain control modes of operation. This provides a novel tool for modern systems neuroscience to study learning and adaptation in the brain. At Duke we demonstrated that subjects can learn to reach and grasp virtual objects by controlling a robot arm through a BMI using visual feedback, even in the absence of overt arm movements. Learning to operate the BMI was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMI were incorporated into motor and sensory cortical representations. Further analysis has shown how the activity of individual neurons and neuronal populations became less representative of the subject’s hand movements while favoring the movements of the actuator. These results demonstrate that during BMI control, cortical ensembles represent behaviorally significant motor parameters, even if these are not associated with movements of the subject’s own limb.

Another focus of my research is to translate this research into the clinical realm to improve the quality of life for people with motor disabilities. In recent collaborative work at Duke we applied the BMI technology in the human intraoperative setting to asses the feasibility of using neuronal activity from subcortical areas of the human brain to drive a BMI. We showed that acute neuronal recordings from motor thalamus and subthalamic nucleus can predict motor function and therefore have the potential to provide informative control signals to drive a neuroprosthetic device.


Journal Publications

  1. Kim H.K., Carmena J.M., Biggs S.J., Hanson T.L., Nicolelis M.A.L., and Srinivasan M.A. (2007). The muscle activation method: An approach to impedance control of brain-machine interfaces through a musculoskeletal model of the arm. IEEE Transactions on Biomedical Engineering 54(8), pp. 1520-1529.
  2. Zacksenhouse M., Lebedev M.A., Carmena J.M.,  O’Doherty J.E., Henriquez C.S. and Nicolelis M.A.L. (2007). Cortical modulations increase in early sessions with brain-machine interface. Public Library of Science One 7, e619.
  3. Kim S.-P., Sanchez J.C., Rao Y.N., Erdogmus D., Carmena J.M., Lebedev M.A., Nicolelis M.A.L., and Principe J.C. (2006). A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces. Journal of Neural Engineering 3, pp. 145-161.
  4. Kim H.K., Biggs S.J., Schloerb D.W., Carmena J.M., Lebedev M.A., Nicolelis M.A.L., and Srinivasan M.A. (2006). Continuous shared control stabilizes reach and grasping with brain-machine interfaces. IEEE Transactions on Biomedical Engineering 53(6), pp. 1164-1173.
  5. Gutierrez R., Carmena J.M., Nicolelis M.A.L., and Simon S.A. (2006). Temporal specific ensembles of rat orbitofrontal neurons represent the drinking of liquid rewards. Journal of Neurophysiology 95: 119-133.
  6. Carmena J.M., Lebedev M.A., Henriquez C.S., and Nicolelis M.A.L. (2005). Stable ensemble performance with single neuron variability during reaching movements in primates. Journal of Neuroscience 25(46):10712-10716.
  7. Lebedev M.A., Carmena J.M., O’Doherty J.E., Zacksenhouse M., Henriquez C.S., Principe J.C., and Nicolelis M.A.L. (2005). Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. Journal of Neuroscience 25(19):4681-4693.
  8. Carmena J.M. and Hallam J.C.T. (2004). Narrowband tracking using a biomimetic sonarhead. Robotics and Autonomous Systems 46(4), pp. 247-259.
  9. Patil P.G., Carmena J.M., Nicolelis M.A.L., and Turner D.A. (2004). Ensembles of human subcortical neurons as a source of motor control signals for a brain-machine interface. Neurosurgery 55(1), pp. 27-38.
  10. Sanchez J.C., Carmena J.M., Lebedev M.A., Nicolelis M.A.L., Harris J.G., and Principe J.C (2004). Ascertaining the importance of neurons to develop better brain-machine interfaces, IEEE Transactions on Biomedical Engineering 51(6), pp. 943-953.
  11. Bossetti C.A., Carmena J.M., Nicolelis M.A.L., and Wolf P.D. (2004). Data telemetry limitations in real-time neural signal processing systems, IEEE Transactions on Biomedical Engineering 51(6), pp. 919-924.
  12. Carmena J.M. and Hallam J.C.T. (2004). The use of Doppler in Sonar-based mobile robot navigation: inspirations from Biology. Information Sciences 161(1-2), pp. 71-94.
  13. Carmena J.M., Lebedev M.A., Crist R.E., O’Doherty J.E., Santucci D.M., Dimitrov D.F., Patil P.G., Henriquez C.S. and Nicolelis M.A.L. (2003). Learning to control a brain-machine interface for reaching and grasping by primates, Public Library of Science Biology 1(2), pp. 193-208.
  14. Nicolelis M.A.L., Dimitrov D., Carmena J.M., Crist R.E., Lehew G., Kralik J., and Wise S.P. (2003). Chronic, multi site, multi electrode recordings in macaque monkeys. Proceedings of the National Academy of the Sciences of the USA, 100(19), pp. 11041-11046.
  15. Carmena J.M. and Hallam J.C.T. (2002). Estimating Doppler-shift using Bat-inspired Cochlear Filterbank Models: A Comparison of Methods for Echoes from Single and Multiple Reflectors. Adaptive Behavior, 9(3–4), pp.241–261.
  16. Carmena J.M., Kämpchen N., Kim D., and Hallam J.C.T.  (2001). Artificial ears for a biomimetic sonarhead: from multiple reflectors to surfaces. Artificial Life, 7(2), pp.147–169.


Curriculum Vitae


Last updated August 1, 2007.