Selecting neural subsets for kinematics decoding by information theoretical analysis in motor Brain Machine Interfaces

Yiwen Wang; Justin C. Sanchez; Jose C. Principe

doi:10.1109/IJCNN.2009.5178809

Selecting neural subsets for kinematics decoding by information theoretical analysis in motor Brain Machine Interfaces

Yiwen Wang^*, Justin C. Sanchez, Jose C. Principe

^*Corresponding author for this work

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

4 Citations (Scopus)

Abstract

Previous decoding algorithms for Brain Machine Interfaces (BMIs) reconstruct the kinematics from recorded activities of hundreds of neurons, which are not all related to the movement task. Decoding from all neurons not only brings problem towards model generalization but also a significant computation burden. Knowledge of neural receptive fields helps ascertain the neuron importance associate with the movements. We propose to apply information theoretical analysis based on an instantaneous tuning model to extract the candidate neuron subsets, which also reduces the computation complexity for the decoding process. The cortical distribution of extracted neuron subsets is analyzed and the statistical decoding performances using neuron subset selection are compared to the one by the full neuron ensemble.

Original language	English
Title of host publication	2009 International Joint Conference on Neural Networks, IJCNN 2009
Pages	3275-3280
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2009.5178809
Publication status	Published - 2009
Externally published	Yes
Event	2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States Duration: 14 Jun 2009 → 19 Jun 2009

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks

Conference

Conference	2009 International Joint Conference on Neural Networks, IJCNN 2009
Country/Territory	United States
City	Atlanta, GA
Period	14/06/09 → 19/06/09

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10.1109/IJCNN.2009.5178809

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Wang, Y., Sanchez, J. C., & Principe, J. C. (2009). Selecting neural subsets for kinematics decoding by information theoretical analysis in motor Brain Machine Interfaces. In 2009 International Joint Conference on Neural Networks, IJCNN 2009 (pp. 3275-3280). Article 5178809 (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2009.5178809

@inproceedings{8eae4eeb4837466aafd14ef370420f49,

title = "Selecting neural subsets for kinematics decoding by information theoretical analysis in motor Brain Machine Interfaces",

abstract = "Previous decoding algorithms for Brain Machine Interfaces (BMIs) reconstruct the kinematics from recorded activities of hundreds of neurons, which are not all related to the movement task. Decoding from all neurons not only brings problem towards model generalization but also a significant computation burden. Knowledge of neural receptive fields helps ascertain the neuron importance associate with the movements. We propose to apply information theoretical analysis based on an instantaneous tuning model to extract the candidate neuron subsets, which also reduces the computation complexity for the decoding process. The cortical distribution of extracted neuron subsets is analyzed and the statistical decoding performances using neuron subset selection are compared to the one by the full neuron ensemble.",

author = "Yiwen Wang and Sanchez, \{Justin C.\} and Principe, \{Jose C.\}",

year = "2009",

doi = "10.1109/IJCNN.2009.5178809",

language = "English",

isbn = "9781424435531",

series = "Proceedings of the International Joint Conference on Neural Networks",

pages = "3275--3280",

booktitle = "2009 International Joint Conference on Neural Networks, IJCNN 2009",

note = "2009 International Joint Conference on Neural Networks, IJCNN 2009 ; Conference date: 14-06-2009 Through 19-06-2009",

}

Wang, Y, Sanchez, JC & Principe, JC 2009, Selecting neural subsets for kinematics decoding by information theoretical analysis in motor Brain Machine Interfaces. in 2009 International Joint Conference on Neural Networks, IJCNN 2009., 5178809, Proceedings of the International Joint Conference on Neural Networks, pp. 3275-3280, 2009 International Joint Conference on Neural Networks, IJCNN 2009, Atlanta, GA, United States, 14/06/09. https://doi.org/10.1109/IJCNN.2009.5178809

Selecting neural subsets for kinematics decoding by information theoretical analysis in motor Brain Machine Interfaces. / Wang, Yiwen; Sanchez, Justin C.; Principe, Jose C.
2009 International Joint Conference on Neural Networks, IJCNN 2009. 2009. p. 3275-3280 5178809 (Proceedings of the International Joint Conference on Neural Networks).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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AU - Sanchez, Justin C.

AU - Principe, Jose C.

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N2 - Previous decoding algorithms for Brain Machine Interfaces (BMIs) reconstruct the kinematics from recorded activities of hundreds of neurons, which are not all related to the movement task. Decoding from all neurons not only brings problem towards model generalization but also a significant computation burden. Knowledge of neural receptive fields helps ascertain the neuron importance associate with the movements. We propose to apply information theoretical analysis based on an instantaneous tuning model to extract the candidate neuron subsets, which also reduces the computation complexity for the decoding process. The cortical distribution of extracted neuron subsets is analyzed and the statistical decoding performances using neuron subset selection are compared to the one by the full neuron ensemble.

AB - Previous decoding algorithms for Brain Machine Interfaces (BMIs) reconstruct the kinematics from recorded activities of hundreds of neurons, which are not all related to the movement task. Decoding from all neurons not only brings problem towards model generalization but also a significant computation burden. Knowledge of neural receptive fields helps ascertain the neuron importance associate with the movements. We propose to apply information theoretical analysis based on an instantaneous tuning model to extract the candidate neuron subsets, which also reduces the computation complexity for the decoding process. The cortical distribution of extracted neuron subsets is analyzed and the statistical decoding performances using neuron subset selection are compared to the one by the full neuron ensemble.

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M3 - Conference Paper published in a book

SN - 9781424435531

T3 - Proceedings of the International Joint Conference on Neural Networks

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BT - 2009 International Joint Conference on Neural Networks, IJCNN 2009

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Y2 - 14 June 2009 through 19 June 2009

ER -

Selecting neural subsets for kinematics decoding by information theoretical analysis in motor Brain Machine Interfaces

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