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The following research fields are of interest to MotorLab members:
Motor Control
Cortical Physiology
Muscle Activity
Skeletal Biomechanics
Visual Motor Interconnection
Arm reaching
Reach to grasp
Control of dexterity
Neural Prosthetics
Robotics
Neural Statistics
System Control
A key question of volitional behavior is how the intention to move is transformed to movement execution. Movement perception is an important
component of this process. Normally we perceive our actions accurately we know how we really move. In order to dissociate the
perception of movement from the actual movement, we have designed a movement illusion. Subjects work in a virtual-reality environment in which
they cannot see their own hands. Instead, their 3D hand position is represented by a ball that appears to be floating in space. The subject’s
hand is tracked continuously and the task proceeds by placing the ball in an oval-shaped template projected in front of the chest and moving it
around the oval five times. During the task, the gain of the cursor is gradually increased so that by the last cycle, the subject’s hand is moving
in a circle. However, the subject perceives the movement as an oval. The dichotomy between action and perception is differentially represented. The
perceived movement is extracted from ventral premotor cortex while the actual movement is represented in the primary motor cortex. We are now
investigating how this disparate information flows from pre- to primary cortex in terms of single-cell transmission and population activity. This
will also be examined using multi-dimensional clustering based on cell-cell and cell-behavior correlation.
The primary motor cortex has anatomical connectivity to motoneurons in the spinal cord. This connectivity is complex and determining the causality of
any given muscle contraction is a difficult problem. We use a correlation approach to compare single-unit activity recorded in motor cortex to EMG
activity during a variety of arm movement tasks. So far, we have found that the correlation between cortical and muscle activity varies in a
consistent way within a single task. For instance, during ellipse drawing, a neuron-muscle pair will be correlated for only a small segment of the
trajectory. This correspondence appears to be determined by the ratio of a cortical cell’s preferred direction measured in a hand-centered coordinate
system and the impulse-contraction-induced movement of the hand by the studied muscle. This non-stationary functional connectivity is being
modeled and new data gathered to detail the general features of the corticomuscular system.
Over the last 10-12 years we have developed technology to transform cortical activity to a signal that controls a robotic arm during movements
such as those used for reaching and feeding. Arrays of chronic microelectrodes are implanted permanently in the motor cortical areas of
monkeys trained to move their arms in three-dimensional space. Single-unit activity recorded from these electrodes is discriminated and the resulting
firing rates are processed with an extraction algorithm that generates a velocity signal of the hand every 30 ms. Initially, the monkeys worked in
a virtual reality, reaching for targets located in different parts of the 3D space initially with their hands. In these experiments, the hand is
tracked and displayed as a ball-shaped cursor. Once the animal is trained in the task, the electrodes are implanted and the animal moves the cursor
only by modulating its cortical activity to produce a velocity signal in the absence of arm movement. Recently we have replaced the virtual reality
environment with an anthropomorphic robot arm. The extracted velocity signal is used as an input to an inverse kinematic algorithm that gives
joint-angles for each of the four robot motors. This child-sized arm has a fully mobile shoulder and elbow and is outfitted with a simple
gripper. Using the principles we developed with the VR task, monkeys have been trained to used the arm with their cortical signals to reach out,
grasp and retrieve vegetable pieces in a self-feeding task. This is accomplished with natural movements of the artificial arm. We are in the
process of extending this control to the wrist and fingers with a more elaborate effector.
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