Decomposition of EEG signals for multichannel neural activity analysis in animal experiments

Vincent Vigneron; Hsin Chen; Yen Tai Chen; Hsin Yi Lai; You Yin Chen

doi:10.1007/978-3-642-15995-4_59

Decomposition of EEG signals for multichannel neural activity analysis in animal experiments

Vincent Vigneron^*, Hsin Chen, Yen Tai Chen, Hsin Yi Lai, You Yin Chen

^*Corresponding author for this work

Department of Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

We describe in this paper some advanced protocols for the discrimination and classification of neuronal spike waveforms within multichannel electrophysiological recordings. Sparse decomposition was used to serarate the linearly independent signals underlying sensory information in cortical spike firing pat- terns. We introduce some modifications in the the IDE algorithm to take into account prior knowledge on the spike waveforms. We have investigated motor cortex responses recorded during movement in freely moving rats to provide ev- idence for the relationship between these patterns and special behavioral task.

Original language	English
Title of host publication	Latent Variable Analysis and Signal Separation - 9th International Conference, LVA/ICA 2010, Proceedings
Publisher	Springer Verlag
Pages	474-481
Number of pages	8
ISBN (Print)	364215994X, 9783642159947
DOIs	https://doi.org/10.1007/978-3-642-15995-4_59
State	Published - 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6365 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

Atomic Decomposition
IDE akgorithm
Sparse decomposition
classification
semi-supervised learning

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10.1007/978-3-642-15995-4_59

Cite this

Vigneron, V., Chen, H., Chen, Y. T., Lai, H. Y., & Chen, Y. Y. (2010). Decomposition of EEG signals for multichannel neural activity analysis in animal experiments. In Latent Variable Analysis and Signal Separation - 9th International Conference, LVA/ICA 2010, Proceedings (pp. 474-481). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6365 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-15995-4_59

Vigneron, Vincent ; Chen, Hsin ; Chen, Yen Tai et al. / Decomposition of EEG signals for multichannel neural activity analysis in animal experiments. Latent Variable Analysis and Signal Separation - 9th International Conference, LVA/ICA 2010, Proceedings. Springer Verlag, 2010. pp. 474-481 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We describe in this paper some advanced protocols for the discrimination and classification of neuronal spike waveforms within multichannel electrophysiological recordings. Sparse decomposition was used to serarate the linearly independent signals underlying sensory information in cortical spike firing pat- terns. We introduce some modifications in the the IDE algorithm to take into account prior knowledge on the spike waveforms. We have investigated motor cortex responses recorded during movement in freely moving rats to provide ev- idence for the relationship between these patterns and special behavioral task.",

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author = "Vincent Vigneron and Hsin Chen and Chen, {Yen Tai} and Lai, {Hsin Yi} and Chen, {You Yin}",

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Vigneron, V, Chen, H, Chen, YT, Lai, HY & Chen, YY 2010, Decomposition of EEG signals for multichannel neural activity analysis in animal experiments. in Latent Variable Analysis and Signal Separation - 9th International Conference, LVA/ICA 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6365 LNCS, Springer Verlag, pp. 474-481. https://doi.org/10.1007/978-3-642-15995-4_59

Decomposition of EEG signals for multichannel neural activity analysis in animal experiments. / Vigneron, Vincent; Chen, Hsin; Chen, Yen Tai et al.
Latent Variable Analysis and Signal Separation - 9th International Conference, LVA/ICA 2010, Proceedings. Springer Verlag, 2010. p. 474-481 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6365 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We describe in this paper some advanced protocols for the discrimination and classification of neuronal spike waveforms within multichannel electrophysiological recordings. Sparse decomposition was used to serarate the linearly independent signals underlying sensory information in cortical spike firing pat- terns. We introduce some modifications in the the IDE algorithm to take into account prior knowledge on the spike waveforms. We have investigated motor cortex responses recorded during movement in freely moving rats to provide ev- idence for the relationship between these patterns and special behavioral task.

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SN - 9783642159947

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Vigneron V, Chen H, Chen YT, Lai HY, Chen YY. Decomposition of EEG signals for multichannel neural activity analysis in animal experiments. In Latent Variable Analysis and Signal Separation - 9th International Conference, LVA/ICA 2010, Proceedings. Springer Verlag. 2010. p. 474-481. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-15995-4_59

Decomposition of EEG signals for multichannel neural activity analysis in animal experiments

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