Jose Carmena

Professor of Electrical Engineering, Cognitive Science and Neuroscience
Keywords: controls
Research Areas: controls, sensorimotor learning and control, brain-machine interfaces, neuroprosthetics, neural ensemble computation

Research Description:

Our research is concerned with investigating the neural basis of sensorimotor learning and control, and how we can use this knowledge in a brain-machine interface (BMI) context to improve the quality of life for the neurologically impaired.

BMI is about transforming thought into action, or conversely, sensation into perception. This novel paradigm contends that a user can perceive sensory information and enact voluntary motor actions through a direct interface between the brain and a prosthetic device in virtually the same way that we see, walk or grab an object with our own natural limbs. Proficient control of the prosthetic device relies on the volitional modulation of neural ensemble activity, achieved through training with any combination of visual, tactile, or auditory feedback.

BMI is also a powerful tool for modern systems neuroscience to study learning and adaptation in the brain. It allows 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.

At the BMI Systems Lab we use electrophysiological, behavioral and computational techniques to ask scientific questions about how the brain controls movement, as well as to achieve the technological milestones required to bring BMI to the clinical realm.

Selected Publications:

  • Carmena J.M., Lebedev M.A., Henriquez C.S., and Nicolelis M.A.L. Stable ensemble performance with single neuron variability during reaching movements in primates. Journal of Neuroscience 25(46): 10712-10716, 2005.
  • Lebedev M.A., Carmena J.M., O’Doherty J.E., Zacksenhouse M., Henriquez C.S., Principe J.C., and Nicolelis M.A.L. Cortical ensemble adaptation to represent velocity of an artificial actuator controlled by a brain-machine interface. Journal of Neuroscience 25(19):4681-4693, 2005.
  • Patil P.G., Carmena J.M., Nicolelis M.A.L., and Turner D.A. Ensembles of human subcortical neurons as a source of motor control signals for a brain-machine interface. Neurosurgery 55(1), pp. 27-38, 2004.
  • 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. Learning to control a brain-machine interface for reaching and grasping by primates, Public Library of Science Biology 1(2), 193-208, 2003.
  • Nicolelis M.A.L., Dimitrov D., Carmena J.M., Crist R.E., Lehew G., Kralik J., and Wise S.P. Chronic, multi site, multi electrode recordings in macaque monkeys. Proceedings of the National Academy of Sciences of the USA, 100(19), pp. 11041-11046, 2003.
  • Kim H.K., Biggs S.J., Schloerb D.W., Carmena J.M., Lebedev M.A., Nicolelis M.A.L., and Srinivasan M.A. Continuous shared control stabilizes reach and grasping with brain-machine interfaces. IEEE Transactions on Biomedical Engineering 53(6),1164-1173, 2006.
  • 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. A comparison of optimal MIMO linear and nonlinear models for brain-machine interfaces. Journal of Neuroengineering 3, pp.145-161, 2006.
  • Carmena J.M. and Hallam J.C.T. Narrowband tracking using a biomimetic sonarhead Robotics and Autonomous Systems 46(4), pp. 247-259, 2004.
  • Carmena J.M. and Hallam J.C.T. 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, 2002.
  • Carmena J.M., Kämpchen N., Kim D., and Hallam J.C.T. Artificial ears for a biomimetic sonarhead: from multiple reflectors to surfaces. Artificial Life, 7(2), pp.147–169, 2001.

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