Neural Decoding of Hand Motion Using a Linear State-Space Model With Hidden States

The Kalman filter has been proposed as a model to decode neural activity measured from the motor cortex in order to obtain real-time estimates of hand motion in behavioral neurophysiological experiments. However, currently used linear state-space models underlying the Kalman filter do not take into...

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Bibliographic Details
Published in:	IEEE transactions on neural systems and rehabilitation engineering Vol. 17; no. 4; pp. 370 - 378
Main Authors:	Wu, Wei, Kulkarni, Jayant E., Hatsopoulos, Nicholas G., Paninski, Liam
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.08.2009 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Brain modeling Computer Simulation Electroencephalography - methods Evoked Potentials, Motor - physiology Expectation-maximization algorithms Filters Hand - physiology Hidden states Humans Kalman filter Linear Models Maximum likelihood decoding Models, Neurological Motion estimation Motion measurement motor cortex Motor Cortex - physiology Movement - physiology Multidimensional systems neural decoding State estimation state-space model Statistics Studies Vectors
ISSN:	1534-4320, 1558-0210, 1558-0210
Online Access:	Get full text
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