Biomedical subjects

Stephen I Helms Tillery

Publications and source records attributed to Stephen I Helms Tillery.

6 recordsLinked to original sources

PubMed2006

Selection and parameterization of cortical neurons for neuroprosthetic control.

When designing neuroprosthetic interfaces for motor function, it is crucial to have a system that can extract reliable information from available neural signals and produce an output suitable for real life applications. Systems designed to date have relied on establishing a relationship between neural discharge patterns in motor cortical areas and limb movement, an approach not suitable for patients who require such implants but who are unable to provide proper motor behavior to initially tune the system. We describe here a method that allows rapid tuning of a population vector-based system for neural control without arm movements. We trained highly motivated primates to observe a 3D center-out task as the computer played it very slowly. Based on only 10-12 s of neuronal activity observed in M1 and PMd, we generated an initial mapping between neural activity and device motion that the animal could successfully use for neuroprosthetic control. Subsequent tunings of the parameters led to improvements in control, but the initial selection of neurons and estimated preferred direction for those cells remained stable throughout the remainder of the day. Using this system, we have observed that the contribution of individual neurons to the overall control of the system is very heterogeneous. We thus derived a novel measure of unit quality and an indexing scheme that allowed us to rate each neuron's contribution to the overall control. In offline tests, we found that fewer than half of the units made positive contributions to the performance. We tested this experimentally by having the animals control the neuroprosthetic system using only the 20 best neurons. We found that performance in this case was better than when the entire set of available neurons was used. Based on these results, we believe that, with careful task design, it is feasible to parameterize control systems without any overt behaviors and that subsequent control system design will be enhanced with cautious unit selection. These improvements can lead to systems demanding lower bandwidth and computational power, and will pave the way for more feasible clinical systems.

Action Potentials↗

PubMed2005

Control of hand orientation and arm movement during reach and grasp.

We studied the coordination of arm and wrist motion in a task requiring fine control of hand orientation. Subjects were instructed to reach and grasp one of two targets positioned in the frontal plane at various orientations. The task was performed under three target conditions: fixed orientation, predictably perturbed orientation, and randomly perturbed orientation. For fixed target orientations, the hand began to rotate to the required orientation from the beginning of the reach. Hand peak supination angles scaled linearly with target orientations. The trajectories of hand/arm joint angles also had a one-to-one relationship with different target orientations. These demonstrate that target orientation is a constraint on the hand/arm final orientation, a control variable to be specified in advance by the central nervous system (CNS). Under perturbation conditions, subjects were still able to complete the task smoothly. In the early trials of the predictable perturbation, the hand rotated first to the original target orientation and then corrected for the final target orientation. Initial corrections occurred about 200 ms after the onset of perturbation. This latency decreased as the subjects adapted to the perturbation, and the hand orientation trajectory shifted to match the unperturbed trajectory for the final orientation. By contrast, we observed no clear changes in orientation trajectory under the randomly perturbed conditions. These suggest that feedback control is important to the execution of the movement, but that the CNS tends to optimize feedforward planning rather than feedback correction when the disturbance information is predictable.

Adult↗

PubMed2005

Determining natural arm configuration along a reaching trajectory.

Owing to the flexibility and redundancy of neuromuscular and skeletal systems, humans can trace the same hand trajectory in space with various arm configurations. However, the joint trajectories of typical unrestrained movements tend to be consistent both within and across subjects. In this paper we propose a method to solve the 3-D inverse kinematics problem based on minimizing the magnitude of total work done by joint torques. We examined the fit of the joint-space trajectories against those observed from human performance in a variety of movement paths in 3-D workspace. The results showed that the joint-space trajectories produced by the method are in good agreement with the subjects' arm movements (r2>0.98), with the exception of shoulder adduction/abduction (where, in the worst case, r2 approximately 0.8). Comparison of humeral rotation predicted by our algorithm with other models showed that the correlation coefficient r2) between actual data and our predictions is extremely high (mostly >0.98, 11 out of 15 cases, with a few exceptions, 4 of 15, in the range of 0.8-0.9) and the slope of linear regression is much closer to one (<0.05 distortion in 12 out of 15 cases, with only one case >0.15). However, the discrepancy in shoulder adduction/abduction indicated that when only the hand path is known, additional constraint(s) may be required to generate a complete match with human performance.

Algorithms↗

PubMed2004

Signal acquisition and analysis for cortical control of neuroprosthetics.

Work in cortically controlled neuroprosthetic systems has concentrated on decoding natural behaviors from neural activity, with the idea that if the behavior could be fully decoded it could be duplicated using an artificial system. Initial estimates from this approach suggested that a high-fidelity signal comprised of many hundreds of neurons would be required to control a neuroprosthetic system successfully. However, recent studies are showing hints that these systems can be controlled effectively using only a few tens of neurons. Attempting to decode the pre-existing relationship between neural activity and natural behavior is not nearly as important as choosing a decoding scheme that can be more readily deployed and trained to generate the desired actions of the artificial system. These artificial systems need not resemble or behave similarly to any natural biological system. Effective matching of discrete and continuous neural command signals to appropriately configured device functions will enable effective control of both natural and abstract artificial systems using compatible thought processes.

Action Potentials↗

PubMed2003

Information conveyed through brain-control: cursor versus robot.

Microwire electrode arrays were implanted in the motor and premotor cortical areas of rhesus macaques. The recorded activity was used to control the three-dimensional movements of a virtual cursor and of a robotic arm in real time. The goal was to move the cursor or robot to one of eight targets. Average information conveyed about the intended target was calculated from the observed trajectories at 30-ms intervals throughout the movements. Most of the information about intended target was conveyed within the first second of the movement. For the brain-controlled cursor, the instantaneous information transmission rate was at its maximum at the beginning of each movement (averaged 4.8 to 5.5 bits/s depending on the calculation method used). However, this instantaneous rate quickly slowed down as the movement progressed and additional information became redundant. Information was conveyed more slowly through the brain-controlled robot due to the dynamics and noise of the robot system. The brain-controlled cursor data was also used to demonstrate a method for optimizing information transmission rate in the case where repeated cursor movements are used to make long strings of sequential choices such as in a typing task.

Animals↗

PubMed2002

Direct cortical control of 3D neuroprosthetic devices.

Three-dimensional (3D) movement of neuroprosthetic devices can be controlled by the activity of cortical neurons when appropriate algorithms are used to decode intended movement in real time. Previous studies assumed that neurons maintain fixed tuning properties, and the studies used subjects who were unaware of the movements predicted by their recorded units. In this study, subjects had real-time visual feedback of their brain-controlled trajectories. Cell tuning properties changed when used for brain-controlled movements. By using control algorithms that track these changes, subjects made long sequences of 3D movements using far fewer cortical units than expected. Daily practice improved movement accuracy and the directional tuning of these units.

Algorithms↗