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The Journal of Neuroscience, October 31, 2007, 27(44):11842-11846; doi:10.1523/JNEUROSCI.3516-07.2007

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Symposia and Mini-Symposia
Biomimetic Brain Machine Interfaces for the Control of Movement

Andrew H. Fagg,¹ Nicholas G. Hatsopoulos,^3,4,5 Victor de Lafuente,⁶ Karen A. Moxon,⁷ Shamim Nemati,² James M. Rebesco,⁸ Ranulfo Romo,⁶ Sara A. Solla,^8,9 Jake Reimer,³ Dennis Tkach,⁴ Eric A. Pohlmeyer,^8,10 and Lee E. Miller^8,10

¹School of Computer Science and ²Department of Mathematics, University of Oklahoma, Norman, Oklahoma 73019, ³Committee on Neurobiology, ⁴Committee on Computational Neuroscience, and ⁵Department of Organismal Biology and Anatomy, University of Chicago, Chicago, Illinois 60637, ⁶Institute of Cellular Physiology, National Autonomous University of Mexico, Mexico City, 04510, Mexico, ⁷School of Biomedical Engineering, Drexel University, Philadelphia, Pennsylvania 19104, ⁸Department of Physiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, and Departments of ⁹Physics and Astronomy and ¹⁰Biomedical Engineering, Northwestern University, Evanston, Illinois 60208

Correspondence should be addressed to Lee E. Miller, Department of Physiology, Feinberg School of Medicine, Northwestern University, 303 East Chicago Avenue, Chicago, IL 60611. Email: lm{at}northwestern.edu

Quite recently, it has become possible to use signals recorded simultaneously from large numbers of cortical neurons for real-time control. Such brain machine interfaces (BMIs) have allowed animal subjects and human patients to control the position of a computer cursor or robotic limb under the guidance of visual feedback. Although impressive, such approaches essentially ignore the dynamics of the musculoskeletal system, and they lack potentially critical somatosensory feedback. In this mini-symposium, we will initiate a discussion of systems that more nearly mimic the control of natural limb movement. The work that we will describe is based on fundamental observations of sensorimotor physiology that have inspired novel BMI approaches. We will focus on what we consider to be three of the most important new directions for BMI development related to the control of movement. (1) We will present alternative methods for building decoders, including structured, nonlinear models, the explicit incorporation of limb state information, and novel approaches to the development of decoders for paralyzed subjects unable to generate an output signal. (2) We will describe the real-time prediction of dynamical signals, including joint torque, force, and EMG, and the real-time control of physical plants with dynamics like that of the real limb. (3) We will discuss critical factors that must be considered to incorporate somatosensory feedback to the BMI user, including its potential benefits, the differing representations of sensation and perception across cortical areas, and the changes in the cortical representation of tactile events after spinal injury.

Key words: brain machine interface; monkey; movement; EMG; electromyogram; motor cortex; muscle; muscle paralysis; somatosensory cortex; spinal cord injury

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Received Aug. 2, 2007; revised Sept. 5, 2007; accepted Sept. 5, 2007.

Correspondence should be addressed to Lee E. Miller, Department of Physiology, Feinberg School of Medicine, Northwestern University, 303 East Chicago Avenue, Chicago, IL 60611. Email: lm{at}northwestern.edu

This article has been cited by other articles:

				S. M. Radhakrishnan, S. N. Baker, and A. Jackson Learning a Novel Myoelectric-Controlled Interface Task J Neurophysiol, October 1, 2008; 100(4): 2397 - 2408. [Abstract] [Full Text] [PDF]

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