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

Content Provider	IEEE Xplore Digital Library
Author	Wei Wu Kulkarni, J.E. Hatsopoulos, N.G. Paninski, L.
Copyright Year	2001
Abstract	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 account other behavioral states such as muscular activity or the subject's level of attention, which are often unobservable during experiments but may play important roles in characterizing neural controlled hand movement. To address this issue, we depict these unknown states as one multidimensional hidden state in the linear state-space framework. This new model assumes that the observed neural firing rate is directly related to this hidden state. The dynamics of the hand state are also allowed to impact the dynamics of the hidden state, and vice versa. The parameters in the model can be identified by a conventional expectation-maximization algorithm. Since this model still uses the linear Gaussian framework, hand-state decoding can be performed by the efficient Kalman filter algorithm. Experimental results show that this new model provides a more appropriate representation of the neural data and generates more accurate decoding. Furthermore, we have used recently developed computationally efficient methods by incorporating a priori information of the targets of the reaching movement. Our results show that the hidden-state model with target-conditioning further improves decoding accuracy.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	9
File Size	585576
Starting Page	370
Ending Page	378
File Format	PDF
ISSN	15344320
Volume Number	17
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood decoding Brain modeling Statistics Vectors Filters Motion measurement Motion estimation State estimation Multidimensional systems Expectation-maximization algorithms state-space model Hidden states Kalman filter motor cortex neural decoding
Content Type	Text
Resource Type	Article
Subject	Neuroscience Rehabilitation Internal Medicine Biomedical Engineering Computer Science Applications

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