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BOX 29.4 MOTOR NEUROPROSTHETICS The fact that a subject’s movements can be decoded from populations of neurons, combined with the recently developed ability to implant devices in the brain that record populations of neurons simultaneously, has led to the development of brain-computer interfaces (BCIs) and brain machine interfaces (BMIs). These are devices in which a number of simultaneously recorded neurophysiological signals are decoded as a population, and the decoded output then is used to control either a cursor on a computer screen (BCI) or a physical device such as a robotic arm (BMI). While recordings of neuron spikes generally provide the best decoding, other types of neurophysiological signals—local field potentials recorded from penetrating microelectrodes (LFPs), recordings made from various sites on the surface of the brain (electrocorticographic, ECoG), or recordings obtained from the scalp (electroencephalographic, EEG)— all can provide decodable information about a subject’s movements. With current systems, subjects can control the movement of an artificial arm in three-dimensional space, and can open and close the hand. The hope is that with further development of such neuroprosthetic systems, amputees may be able to have dexterous control of a prosthetic arm and hand, and paralyzed patients may be able to work on a computer or move in a normal fashion by driving electrical stimulation of their own, otherwise intact muscles (Lebedev & Nicolelis, 2006). In addition to the potential for useful devices controlled directly from the brain, the field of neuroprosthetics is expanding our understanding of the motor cortex. In a typical BCI session, neurophysiological activity is recorded while a normal subject performs arm movements that control a cursor on a computer screen, like when you move a computer mouse to control a cursor. A decoding algorithm then is derived from the recorded data to predict which patterns of neurophysiological activity correspond to which movements. Next, the decoding algorithm is used to drive a computer cursor. If in this “brain control” mode the subject again is asked to move the cursor to different locations on the screen, however, a number of interesting things often happen. The tuning relationship between neurophysiological activity and movement may change, and the subject may stop moving entirely, even though the computer cursor continues to move under direct control by the cortical activity. Exactly how the cortical activity becomes dissociated from movement of the limb (Schieber, 2011), and how the cortical activity adapts to control the BCI, are currently questions under investigation (Green & Kalaska, 2011). Marc H. Schieber References Green, A. M., & Kalaska, J. F. (2011). Learning to move machines with the mind. Trends in Neurosciences, 34, 61–75. Lebedev, M. A., & Nicolelis, M. A. (2006). Brain-machine interfaces: Past, present and future. Trends in Neurosciences, 29, 536–546. Schieber, M. H. (2011). Dissociating motor cortex from the motor. Journal of Physiology, 589, 5613– 5624.