HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) All Versions of this Article: 97/5/3763    most recent 00482.2006v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (3) Citing Articles via Google Scholar Google Scholar Articles by Yu, B. M. Articles by Shenoy, K. V. Search for Related Content PubMed PubMed Citation Articles by Yu, B. M. Articles by Shenoy, K. V.

INNOVATIVE METHODOLOGY

Mixture of Trajectory Models for Neural Decoding of Goal-Directed Movements

¹Department of Electrical Engineering, ²Medical Scientist Training Program, ³Department of Neurosurgery, and ⁴Neurosciences Program, Stanford University, Stanford, California; and ⁵Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom

Submitted 6 May 2006; accepted in final form 11 February 2007

Probabilistic decoding techniques have been used successfully to infer time-evolving physical state, such as arm trajectory or the path of a foraging rat, from neural data. A vital element of such decoders is the trajectory model, expressing knowledge about the statistical regularities of the movements. Unfortunately, trajectory models that both 1) accurately describe the movement statistics and 2) admit decoders with relatively low computational demands can be hard to construct. Simple models are computationally inexpensive, but often inaccurate. More complex models may gain accuracy, but at the expense of higher computational cost, hindering their use for real-time decoding. Here, we present a new general approach to defining trajectory models that simultaneously meets both requirements. The core idea is to combine simple trajectory models, each accurate within a limited regime of movement, in a probabilistic mixture of trajectory models (MTM). We demonstrate the utility of the approach by using an MTM decoder to infer goal-directed reaching movements to multiple discrete goals from multi-electrode neural data recorded in monkey motor and premotor cortex. Compared with decoders using simpler trajectory models, the MTM decoder reduced the decoding error by 38 (48) percent in two monkeys using 98 (99) units, without a necessary increase in running time. When available, prior information about the identity of the upcoming reach goal can be incorporated in a principled way, further reducing the decoding error by 20 (11) percent. Taken together, these advances should allow prosthetic cursors or limbs to be moved more accurately toward intended reach goals.

This article has been cited by other articles:

				G. Czanner, U. T. Eden, S. Wirth, M. Yanike, W. A. Suzuki, and E. N. Brown Analysis of Between-Trial and Within-Trial Neural Spiking Dynamics J Neurophysiol, May 1, 2008; 99(5): 2672 - 2693. [Abstract] [Full Text] [PDF]

				W. Truccolo, G. M. Friehs, J. P. Donoghue, and L. R. Hochberg Primary Motor Cortex Tuning to Intended Movement Kinematics in Humans with Tetraplegia J. Neurosci., January 30, 2008; 28(5): 1163 - 1178. [Abstract] [Full Text] [PDF]

				C. A. Chestek, A. P. Batista, G. Santhanam, B. M. Yu, A. Afshar, J. P. Cunningham, V. Gilja, S. I. Ryu, M. M. Churchland, and K. V. Shenoy Single-Neuron Stability during Repeated Reaching in Macaque Premotor Cortex J. Neurosci., October 3, 2007; 27(40): 10742 - 10750. [Abstract] [Full Text] [PDF]

				A. P. Batista, G. Santhanam, B. M. Yu, S. I. Ryu, A. Afshar, and K. V. Shenoy Reference Frames for Reach Planning in Macaque Dorsal Premotor Cortex J Neurophysiol, August 1, 2007; 98(2): 966 - 983. [Abstract] [Full Text] [PDF]