Laboratory for Information and Decision Systems (LIDS) Laboratory for Information and Decision Systems at MIT https://hdl.handle.net/1721.1/1775 2020-11-27T16:54:25Z 2020-11-27T16:54:25Z Simulation of two methods in co-adaptive control for brain-machine interfaces Kowalski, Kevin Srinivasan, Lakshminarayan https://hdl.handle.net/1721.1/70975 2019-04-08T08:18:42Z 2012-06-01T00:00:00Z Simulation of two methods in co-adaptive control for brain-machine interfaces Kowalski, Kevin; Srinivasan, Lakshminarayan Simulation of two methods in co-adaptive control for brain-machine interfaces: cursorGoal (developed by the Shenoy Lab @ Stanford EE and the Carmena Lab @ UC Berkeley EE), and Joint RSE (developed by the Neural Signal Processing Laboratory, www.nsplab.org). The healthy volunteer represents a sensorimotor neural control network. His arm movements are captured with the Microsoft Kinect and used to drive simulated neural activity (not shown) from a point process model of primary motor cortex. This neural activity determines movements of the on-screen cursor through a brain-machine interface (BMI) algorithm. In all trials, the cursor begins at a random point on the outer circle, and the user attempts to adjust his arm movements to bring the cursor to the inner circle (target) for a specified hold period of 0.5 sec. Maximum allowed trial time is 3 sec. In the training trials of these simulations, the various BMI algorithms must both learn neural signal parameters and decode arm movement. In test trials, the neural signal parameters are fixed, and both methods use an identical filter formulation (Eden, 2004 Neural Computation) with a random walk state equation to drive arm movements. cursorGoal and Joint RSE differ markedly in the way visual feedback to the user (cursor movement) is determined during training trials, as well as in the procedure for learning neural signal parameters. The related manuscript delineates the relative contributions of these algorithmic variations to the differing performance of these co-adaptive BMI control methods, where Joint RSE consistently and substantially outperforms cursorGoal. 2012-06-01T00:00:00Z Matlab-Kinect Interface Code Kowalski, Kevin Srinivasan, Lakshminarayan https://hdl.handle.net/1721.1/70974 2019-04-08T08:18:42Z 2012-06-01T00:00:00Z Matlab-Kinect Interface Code Kowalski, Kevin; Srinivasan, Lakshminarayan This .zip file contains code and installation instructions for acquiring 3d arm movements in Matlab using the Microsoft Kinect 3d camera. The provided code has been validated in 32-bit and 64-bit Matlab with 32-bit and 64-bit Windows 7 respectively. 2012-06-01T00:00:00Z Supplementary Movie: Breaking the fixed-arrival-time restriction in reaching movements of neural prosthetic devices Srinivasan, Lakshminarayan da Silva, Marco https://hdl.handle.net/1721.1/60298 2019-04-10T12:58:07Z 2010-12-15T00:00:00Z Supplementary Movie: Breaking the fixed-arrival-time restriction in reaching movements of neural prosthetic devices Srinivasan, Lakshminarayan; da Silva, Marco This supplementary movie demonstrates three neural prosthetic algorithms in the simulated control of an overactuated 3-dimensional virtual robotic arm with a real-time inverse kinematics engine. Specifically, this movie compares the ability of these algorithms to generate movements with variously paced arrival times. Paper Abstract: We routinely generate reaching arm movements to function independently. For paralyzed users of upper-extremity neural prosthetic devices, flexible, high-performance reaching algorithms will be critical to restoring quality-of-life. Previously, algorithms called real-time reach state equations (RSE) were developed to integrate the user’s plan and execution-related neural activity to drive reaching movements to arbitrary targets. Preliminary validation under restricted conditions suggested that RSE might yield dramatic performance improvements. Unfortunately, real-world applications of RSE have been impeded because the RSE assume a fixed, known arrival time. Recent animal-based prototypes attempted to break the fixed-arrival time assumption by proposing a Standard Model (SM) that instead restricted the user’s movements to a fixed, known set of targets. Here, we leverage General Purpose Filter Design (GPFD) to break both of these critical restrictions, freeing the paralyzed user to make reaching movements to arbitrary target sets with various arrival times and definitive stopping. In silico validation predicts that the new approach, GPFD-RSE, outperforms the SM while offering greater flexibility. We demonstrate GPFD-RSE against SM in the simulated control of an overactuated 3-dimensional virtual robotic arm with a real-time inverse kinematics engine. Supplementary movie for IEEE Transactions in Biomedical Engineering paper, "Breaking the fixed-arrival-time restriction in reaching movements of neural prosthetic devices", posted December 2010. 2010-12-15T00:00:00Z Stochastic Optimal Control: The Discrete-TIme Case Bertsekas, Dimitir P. Shreve, Steven https://hdl.handle.net/1721.1/4852 2019-04-09T17:04:32Z 2004-03-03T21:32:23Z Stochastic Optimal Control: The Discrete-TIme Case Bertsekas, Dimitir P.; Shreve, Steven 2004-03-03T21:32:23Z 狠狠躁天天躁中文字幕_日韩欧美亚洲综合久久_漂亮人妻被中出中文字幕