Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

By Jose M. Carmena, Mikhail A. Lebedev, R. Crist, Joseph E. O’Doherty, David M. Santucci, Dragan F. Dimitrov, Parag G. Patil, Craig S. Henriquez, Miguel A. L. Nicolelis. PLoS Biology, 2003

1,850 citations recorded by the source corpus.

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/23bd1da0ad4d2ec956346655f0bb0206e13556b8

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Citations in the corpus

High performance communication by people with paralysis using an intracortical brain-computer interface (2017, neutral): “…2008 ; Moran, 2010 ; Brunner et al., 2011 ; Wang et al., 2013 ), and intracortical electrical signals ( Serruya et al., 2002 ; Taylor et al., 2002 ; Carmena et al., 2003 ; Musallam et al., 2004 ; Santhanam et al., 2006 ; Hochberg et al., 2006 ; Ganguly et al., 2011 ; O’Doherty et al., 2011 ; Gilja…”
A reward-modulated Hebbian learning rule can explain experimentally observed network reorganization in a brain control task (2010, methodology): “Monkeys using BCIs to control cursors or robotic arms improve with practice (Taylor et al., 2002; Carmena et al., 2003; Musallam et al., 2004; Schwartz, 2007; Ganguly and Carmena, 2009), indicating that learning-related changes are funneling through the set of neurons being recorded.”
Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019, methodology): “It has been argued that motor neurons encode cursor velocity during neural cursor control (41), with numerous decoding algorithms using such properties to drastically improve user performance over classical techniques (38).”
Cognitive Enhancement: Methods, Ethics, Regulatory Challenges (2009, background): “…hardware side, where multielectrode recordings from more than 300 electrodes permanently implanted in the brain are currently state-of-the art, and on the software side, with computers learning to interpret the signals and commands (Nicolelis et al. 2003; Shenoy et al. 2003; Carmena et al. 2003).”
Neural syntax: cell assemblies, synapsembles and readers (2010, neutral): “…dozens of simultaneously recorded neurons from either the motor cortex or other areas improve the prediction only by a modest 10% to 15% (Figure 5B; Carmena et al., 2003; Hochberg et al., 2006; Serruya et al., 2002; Taylor et al., 2002; Wessberg et al., 2000; cf., Nicolelis and Lebedev, 2009).”
Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. (2006, methodology): “prohibit their use formotor restoration ormotor neuroprosthesis applications (Carmena et al., 2003; Taylor, Tillery, & Schwartz, 2002).”
Brain-machine interfaces: past, present and future. (2006, methodology): “For example, studies in which several independent linear models were run in parallel revealed that several motor parameters, such as arm position, velocity, acceleration and hand gripping force, could be predicted simultaneously by separate combinations of the activity of the same original pool of n”
A high-performance brain–computer interface (2006, background): “Thehandwasalsonotallowedtomoveuntilthegocuewaspresented,providingaproxyfor thecorticalfunctionofaparalysedsubject 1–3 .”