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Learning to control a brain-machine interface for reaching and grasping by primates.

Carmena JM, Lebedev MA, Crist RE, O'Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MA - PLoS Biol. (2003)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology, Duke University, Durham, North Carolina, USA.

ABSTRACT
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.

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Plasticity of Directional Tuning during Training in Brain Control without Arm MovementsConventions are as in Figure 4.(A) Directional tuning profiles during four sessions in pole control (task 1). Percentages of correctly performed trials are indicated for each session.(B) Scatterplots comparing directional tuning during pole versus brain control for the same sessions. For each day, DTD was on average higher in pole control.(C) Directional tuning during brain control for the same sessions as in (A). Note the emergence of a population pattern in which a group of neurons (with some exceptions) exhibits a similar preferred direction. This is manifested by a decrease in the spread of preferred directions (shown near polar plots). Notice also a gradual rotation of the population preferred direction (see polar plots) with training.(D) Gradual changes in DTE during one representative session of brain control without arm movements. This 60-min session was split into 5-min periods, five of which are shown.(E) Improvement in behavioral performance during a single session (same as in [D]) .(F) Decrease in the spread of preferred directions during that session.(G) Increase in average tuning depth during the same session.
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pbio.0000042-g005: Plasticity of Directional Tuning during Training in Brain Control without Arm MovementsConventions are as in Figure 4.(A) Directional tuning profiles during four sessions in pole control (task 1). Percentages of correctly performed trials are indicated for each session.(B) Scatterplots comparing directional tuning during pole versus brain control for the same sessions. For each day, DTD was on average higher in pole control.(C) Directional tuning during brain control for the same sessions as in (A). Note the emergence of a population pattern in which a group of neurons (with some exceptions) exhibits a similar preferred direction. This is manifested by a decrease in the spread of preferred directions (shown near polar plots). Notice also a gradual rotation of the population preferred direction (see polar plots) with training.(D) Gradual changes in DTE during one representative session of brain control without arm movements. This 60-min session was split into 5-min periods, five of which are shown.(E) Improvement in behavioral performance during a single session (same as in [D]) .(F) Decrease in the spread of preferred directions during that session.(G) Increase in average tuning depth during the same session.

Mentions: Operating the BMIc without making movements was characterized by an appearance of peculiar patterns of directional tuning at the population level. Figure 5A and 5C displays the evolution of DTC and DTE for the same neural ensemble during four task 1 sessions with the robot in the loop. Whereas in each case DTCs during brain control resembled those in pole control, they evolved toward a more organized distribution. Although certain diversity in DTCs remained, clear groups of neurons sharing similar DTCs appeared as a result of training (Figure 5C). Quantitatively, this effect was manifested by a decrease in the spread of preferred directions. This effect was also evident in the polar plots showing population-average tuning (i.e., DTE). The DTE became progressively sharper and rotated clockwise. Throughout the four sessions depicted in Figure 5, tuning depth remained higher during pole than brain control operation of the BMIc (Figure 5B).


Learning to control a brain-machine interface for reaching and grasping by primates.

Carmena JM, Lebedev MA, Crist RE, O'Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MA - PLoS Biol. (2003)

Plasticity of Directional Tuning during Training in Brain Control without Arm MovementsConventions are as in Figure 4.(A) Directional tuning profiles during four sessions in pole control (task 1). Percentages of correctly performed trials are indicated for each session.(B) Scatterplots comparing directional tuning during pole versus brain control for the same sessions. For each day, DTD was on average higher in pole control.(C) Directional tuning during brain control for the same sessions as in (A). Note the emergence of a population pattern in which a group of neurons (with some exceptions) exhibits a similar preferred direction. This is manifested by a decrease in the spread of preferred directions (shown near polar plots). Notice also a gradual rotation of the population preferred direction (see polar plots) with training.(D) Gradual changes in DTE during one representative session of brain control without arm movements. This 60-min session was split into 5-min periods, five of which are shown.(E) Improvement in behavioral performance during a single session (same as in [D]) .(F) Decrease in the spread of preferred directions during that session.(G) Increase in average tuning depth during the same session.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC261882&req=5

pbio.0000042-g005: Plasticity of Directional Tuning during Training in Brain Control without Arm MovementsConventions are as in Figure 4.(A) Directional tuning profiles during four sessions in pole control (task 1). Percentages of correctly performed trials are indicated for each session.(B) Scatterplots comparing directional tuning during pole versus brain control for the same sessions. For each day, DTD was on average higher in pole control.(C) Directional tuning during brain control for the same sessions as in (A). Note the emergence of a population pattern in which a group of neurons (with some exceptions) exhibits a similar preferred direction. This is manifested by a decrease in the spread of preferred directions (shown near polar plots). Notice also a gradual rotation of the population preferred direction (see polar plots) with training.(D) Gradual changes in DTE during one representative session of brain control without arm movements. This 60-min session was split into 5-min periods, five of which are shown.(E) Improvement in behavioral performance during a single session (same as in [D]) .(F) Decrease in the spread of preferred directions during that session.(G) Increase in average tuning depth during the same session.
Mentions: Operating the BMIc without making movements was characterized by an appearance of peculiar patterns of directional tuning at the population level. Figure 5A and 5C displays the evolution of DTC and DTE for the same neural ensemble during four task 1 sessions with the robot in the loop. Whereas in each case DTCs during brain control resembled those in pole control, they evolved toward a more organized distribution. Although certain diversity in DTCs remained, clear groups of neurons sharing similar DTCs appeared as a result of training (Figure 5C). Quantitatively, this effect was manifested by a decrease in the spread of preferred directions. This effect was also evident in the polar plots showing population-average tuning (i.e., DTE). The DTE became progressively sharper and rotated clockwise. Throughout the four sessions depicted in Figure 5, tuning depth remained higher during pole than brain control operation of the BMIc (Figure 5B).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurobiology, Duke University, Durham, North Carolina, USA.

ABSTRACT
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.

Show MeSH