Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface

Abstract

Monkeys can learn to directly control the movements of an artificial actuator by using a brain-machine interface (BMI) driven by the activity of a sample of cortical neurons. Eventually, they can do so without moving their limbs. Neuronal adaptations underlying the transition from control of the limb to control of the actuator are poorly understood. Here, we show that rapid modifications in neuronal representation of velocity of the hand and actuator occur in multiple cortical areas during the operation of a BMI. Initially, monkeys controlled the actuator by moving a hand-held pole. During this period, the BMI was trained to predict the actuator velocity. As the monkeys started using their cortical activity to control the actuator, the activity of individual neurons and neuronal populations became less representative of the animal's hand movements while representing the movements of the actuator. As a result of this adaptation, the animals could eventually stop moving their hands yet continue to control the actuator. These results show that, during BMI control, cortical ensembles represent behaviorally significant motor parameters, even if these are not associated with movements of the animal's own limb.

Advanced Neurotechnologies for Chronic Neural Interfaces: New Horizons and Clinical Opportunities

“…Recordings from the surface of the brain Invasive BCIs with implanted intracortical microelectrode arrays use local activity from neurons recorded within the brain (Georgopoulos et al, 1986;Serruya et al, 2002;Taylor et al, 2002;Shenoy et al, 2003;Andersen et al, 2004;Musallam et al, 2004;Lebedev et al, 2005;Hochberg et al, 2006;Santhanam et al, 2006;Lee et al, 2008;Velliste et al, 2008). Signals recorded within cortex have high fidelity, but the stability of intracortical recordings can be variable and decays with time (Santhanam et al, 2007).…”

Primate Reaching Cued by Multichannel Spatiotemporal Cortical Microstimulation

“…With the development of multielectrode implants and the concurrent advances of brain-machine interfaces (Chapin et al, 1999;Wessberg et al, 2000;Taylor et al, 2002;Carmena et al, 2003;Lebedev et al, 2005;Lebedev and Nicolelis, 2006), there is renewed interest in microstimulation as a means of providing the brain with an artificial sensory channel. Such a channel could recover sensation lost because of a neurological disorder or it could convey information from sensors of a prosthetic limb (Berger et al, 2005;Middlebrooks et al, 2005;Lebedev and Nicolelis, 2006;Wickelgren, 2006).…”

Stable Ensemble Performance with Single-Neuron Variability during Reaching Movements in Primates

“…In recent years, the idea that neuronal modulations can be used to control a brainmachine interface (BMI) has received significant attention Nicolelis, 2001;Taylor et al, 2002;Carmena et al, 2003;Nicolelis, 2003;Musallam et al, 2004;Lebedev et al, 2005). There are, however, different opinions in the literature about the size of the neuronal sample needed to operate a BMI efficiently.…”