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702 TECHNOLOGY BRIEF 32: BRAIN-MACHINE INTERFACES (BMI)

Technology Brief 32Brain-Machine Interfaces (BMI)

In most vertebrates (like you), nerves extend from thebrain (the central nervous system), through the spinalcord and out to your many organs (the nerves that lieoutside the brain are collectively called the peripheralnervous system). Peripheral nerves carry informationin both directions. On the one hand, peripheral neuronscan fire at the behest of neurons in the brain andtrigger muscle contraction or chemical release (via certainglands); on the other hand, sensor cells in the peripherycan cause peripheral neurons to fire, sending signalsto the brain to indicate pain, temperature, pressure, etc.Most of the peripheral neurons pass through the spinalcord; injuries to the spinal cord can be very dangerous,as trauma and inflammation can sever these connections,leading to paralysis, lack of sensory function, etc.

Figure TF32-1: A variety of existing, functional prosthetics. (a) The “Luke arm” built by DEKA Corp.; (b) an exoskeleton builtat the Kazerooni Lab, University of California, Berkeley; (c) an EEG-controlled wheelchair, developed by Jose del R. Millan’sgroup at the Ecole Polytechnique Federale de Lausanne (EPFL, Switzerland).

There is, of course, a long history of medical andscientific approaches to helping individuals afflictedwith motor dysfunctions (whether due to trauma orcongenital effects). Among these is the use of prostheticdevices that can supplement or replace lost function:prosthetic arms, prosthetic legs, advanced wheelchairsand exoskeletons have all been developed to aid thosewith motor problems (Fig. TF32-1). Historically, the wayto drive these prosthetics is either by making use ofmotor functions that a patient still has (using handsto drive a wheelchair or sucking on a straw to drivea keyboard, for example) or reading signals from non-damaged peripheral nerves (recording from a pectoralmuscle nerve, for example, to drive a robot elbow).

In the last decade or so, a slightly different paradigm hasarisen that—while still in its infancy—promises a radicalnew way to communicate with prosthetics. The basicidea is to directly record from neurons in the brain, usethose signals to drive a controller and communicate the

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TECHNOLOGY BRIEF 32: BRAIN-MACHINE INTERFACES (BMI) 703

FigureTF32-2: The basic BMI loop depends on (a) neural recording, (b) a computational device or controller that maps neuralsignals to control signals to the prosthetics, and (c) feedback to the user or patient. Perhaps one day BMIs will even drive theuse of portable consumer devices!

control signals to a prosthetic (Fig. TF32-2). In a sense,a computer—via a neural recording interface—recordssignals directly from the brain and uses them, withouta spinal cord, to directly drive a robot prosthetic. Thisarena is currently seeing something of a gold rush asscientific results over the last 10 years and a medical trial(BrainGate) have encouraged researchers to explore anddevelop new technologies. Most central nervous systemrecording for BMI applications involves implanting arraysof recording electrodes (see Section 4-12) into themotor cortex of the subject. This is a very dangerousprocedure (which involves a craniotomy), so research inhumans has been limited to individuals with severe dys-functions for whom the risk is appropriate. Once inserted,the subject trains over days and weeks to drive the pros-thetic via the electrode array.Among the many remarkablefindings in the recent BMI literature is that the neurons intowhich the recording array is inserted themselves learnto modify their firing behavior as the subject learns touse the BMI! That is, although scientists initially focusedon what control algorithm would best decode the neuralsignals to drive say, a robot arm, they soon found—to theirsurprise—that the brain itself would learn to use whateveralgorithm the controller employed. Researchers couldeven change algorithms and the subject could relearn thetask, eventually able to switch between controllers.

Many challenges remain and it is an area ofheavy overlap between electrical engineering, computerscience, and neuroscience. Making electrode arraysthat last an appreciable fraction of a patient’s lifetimeis still an unsolved problem: recording arrays typicallyfail after a few years. It is not at all clear whatsignals are optimal to drive a prosthetic nor whichtechnology (or energy modality) is optimal for a long-lasting implant; many approaches are currently beingexplored. It is not known what the limits of suchcontrol are: could a subject be trained to operate acomplex, multi-parameter non-motor task, for example(like imagining speech or interfacing in complex waysto a tablet)? Ultra-low power electrical recording frontends and ultra-low power radios are another area ofintense study, as these systems must ultimately beminiaturized and implanted into a person as unobtrusivelyas possible. Lastly, ethical issues abound, rangingfrom the acceptability of animal testing to the possibleenhancement possibilities.Some of this remains squarelyin the realm of science fiction, but there is no doubtthese approaches are a possible route to helpingpeople with severe motor dysfunctions, making it aworthwhile endeavor in which EE’s are making very bigcontributions.