The use of a virtual integration environment for the real-time implementation of neural decode algorithms

Bishop, William E.; Yu, Byron M.; Santhanam, Gopal; Afshar, Afsheen; Ryu, Stephen I.; Shenoy, Krishna V.; Vogelstein, R. Jacob; Beaty, James D.; Harshbarger, Stuart

doi:10.1109/iembs.2008.4649231

Cited by 5 publications

(7 citation statements)

References 14 publications

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“…These include the first real-time implementation of the recently published Mixture of Trajectory Models algorithm [16] for decoding end point trajectory (see companion paper for more details [20]), methods of decoding individual and simultaneous finger movement [17], and linear filters [18] for estimating end effector position.…”

Section: A Real-time Decode Of Neural Signals From Pre-recorded Datamentioning

confidence: 99%

A real-time virtual integration environment for the design and development of neural prosthetic systems

Bishop

Armiger

Burck

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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We have developed a virtual integration environment (VIE) for the development of neural prosthetic systems. The VIE is a software environment that modularizes the core functions of a neural prosthetic system--receiving signals, decoding signals and controlling a real or simulated device. Complete prosthetic systems can be quickly assembled by linking pre-existing modules together through standard interfaces. Systems can be simulated in real-time, and simulated components can be swapped out for real hardware. This paper is the first of two companion papers that describe the VIE and its use. In this paper, we first describe the architecture of the VIE and review implemented modules. We then describe the use of the VIE for the real-time validation of neural decode algorithms from pre-recorded data, the use of the VIE in closed loop primate experiments and the use of the VIE in the clinic.

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Section: A Real-time Decode Of Neural Signals From Pre-recorded Datamentioning

confidence: 99%

A real-time virtual integration environment for the design and development of neural prosthetic systems

Bishop

Armiger

Burck

et al. 2008

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…A tetrapalegic subject was able to control a dynamic two degree-of-freedom system through six muscle actuators [10]. The virtual system designed by Bishop et al was used to decode two degree-offreedom center out reaching movements of a Rhesus Macaque monkey [18].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Control of an Interactive Impulsive Virtual Prosthesis

Bunderson

2014

IEEE Trans. Neural Syst. Rehabil. Eng.

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An interactive virtual dynamic environment for testing control strategies for neural machine interfacing with artificial limbs offers several advantages. The virtual environment is low-cost, easily configured, and offers a wealth of data for post-hoc analysis compared with real physical prostheses and robots. For use with prosthetics and research involving amputee subjects it allows the valuable time with the subject to be spent in experiments rather than fixing hardware issues. The usefulness of the virtual environment increases as the realism of the environment increases. Most tasks performed with limbs require interactions with objects in the environment. To simulate these tasks the dynamics of frictional contact, in addition to inertial limb dynamics must be modeled. Here, subjects demonstrate real-time control of an eight degree-of-freedom virtual prosthesis while performing an interactive box-and-blocks task. With practice, four nonamputee subjects and one shoulder disarticulation subject were able to successfully transfer blocks in the virtual environment at an average rate of just under two blocks per minute. The virtual environment is configurable in terms of the virtual arm design, control strategy, and task.

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“…The mixture of trajectory models (MTM) decoder is one such algorithm for estimating end effector trajectory during goal directed reaches [1]. A real-time implementation of this algorithm has recently been demonstrated [2] using a personal computer (PC). While this is a positive step, it is not clear the implementation demonstrated in [2] would successfully transition to platforms with limited processing capacity or restricted power budgets.…”

Section: Introductionmentioning

confidence: 99%

“…A key concern when considering the use of previously demonstrated versions of the MTM algorithm for real-time decoding is the reliance of those implementations on Newton's method to find the mode of the state posteriors [1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…Be definition, a real-time system must approximate each state posterior within a fixed time step (1 ms in previous implementations [2]). For Newton's method, the number of iterations required to reach convergence is unknown a priori, and it is possible that the mode of a state posterior may not be found in the time allotted for these calculations.…”

Section: Introductionmentioning

confidence: 99%

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An efficient approximation for the real-time implementation of the Mixture of Trajectory Models decoder

Bishop

Yuy²,

Santhanam³

et al. 2008

2008 IEEE Biomedical Circuits and Systems Conference

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Abstract-The Mixture of Trajectory Models (MTM) decoder has been used to reconstruct arm trajectories from neural activity. While it produces reasonable results, the computational demands of previously published versions may be too high for many real-time systems. We have developed a novel method of approximating the MTM state posteriors that does not require the use of Newton's method. We show that this method results in only a small decrease in decoding performance yet reduces computational cost by 56.4%. Additionally, an MTM algorithm using this method of approximating the state posteriors produces more accurate decoded trajectories when using small bin sizes than an MTM algorithm using a Gaussian observation model. The more efficient formulation of the MTM algorithm presented here provides an alternative approximation of this algorithm for use on resource constrained embedded systems.

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The use of a virtual integration environment for the real-time implementation of neural decode algorithms

Cited by 5 publications

References 14 publications

A real-time virtual integration environment for the design and development of neural prosthetic systems

A real-time virtual integration environment for the design and development of neural prosthetic systems

Real-Time Control of an Interactive Impulsive Virtual Prosthesis

An efficient approximation for the real-time implementation of the Mixture of Trajectory Models decoder

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