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Siddharth Dangi

Showing results (1-10 of 8) with videos related to

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|October 11, 2013
Likelihood Gradient Ascent (LGA): a closed-loop decoder adaptation algorithm for brain-machine interfacesSiddharth Dangi, Suraj Gowda, Jose M Carmena
IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society|July 10, 2012
Closed-loop decoder adaptation on intermediate time-scales facilitates rapid BMI performance improvements independent of decoder initialization conditionsAmy L Orsborn, Siddharth Dangi, Helene G Moorman, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|January 19, 2012
Exploring time-scales of closed-loop decoder adaptation in brain-machine interfacesAmy L Orsborn, Siddharth Dangi, Helene G Moorman, et al.
Neural Computation|April 24, 2013
Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfacesSiddharth Dangi, Amy L Orsborn, Helene G Moorman, et al.
Journal of Neural Engineering|February 8, 2014
Subject-specific modulation of local field potential spectral power during brain-machine interface control in primatesKelvin So, Siddharth Dangi, Amy L Orsborn, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|October 11, 2013
Brain-machine interface control using broadband spectral power from local field potentialsSiddharth Dangi, Kelvin So, Amy L Orsborn, et al.
Nature Communications|January 7, 2017
Rapid control and feedback rates enhance neuroprosthetic controlMaryam M Shanechi, Amy L Orsborn, Helene G Moorman, et al.
Neural Computation|June 13, 2014
Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfacesSiddharth Dangi, Suraj Gowda, Helene G Moorman, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|October 11, 2013
Likelihood Gradient Ascent (LGA): a closed-loop decoder adaptation algorithm for brain-machine interfacesSiddharth Dangi, Suraj Gowda, Jose M Carmena
IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society|July 10, 2012
Closed-loop decoder adaptation on intermediate time-scales facilitates rapid BMI performance improvements independent of decoder initialization conditionsAmy L Orsborn, Siddharth Dangi, Helene G Moorman, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|January 19, 2012
Exploring time-scales of closed-loop decoder adaptation in brain-machine interfacesAmy L Orsborn, Siddharth Dangi, Helene G Moorman, et al.
Neural Computation|April 24, 2013
Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfacesSiddharth Dangi, Amy L Orsborn, Helene G Moorman, et al.
Journal of Neural Engineering|February 8, 2014
Subject-specific modulation of local field potential spectral power during brain-machine interface control in primatesKelvin So, Siddharth Dangi, Amy L Orsborn, et al.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|October 11, 2013
Brain-machine interface control using broadband spectral power from local field potentialsSiddharth Dangi, Kelvin So, Amy L Orsborn, et al.
Nature Communications|January 7, 2017
Rapid control and feedback rates enhance neuroprosthetic controlMaryam M Shanechi, Amy L Orsborn, Helene G Moorman, et al.
Neural Computation|June 13, 2014
Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfacesSiddharth Dangi, Suraj Gowda, Helene G Moorman, et al.
Pageof 1