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Behavior discrimination using a discrete wavelet based approach for feature extraction on local field potentials in the cortex and striatum

Belić, Jovana J; Halje, Pär LU ; Richter, Ulrike LU ; Petersson, Per LU and Kotaleski, Jeanette Hellgren (2015) 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 In 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 p.964-967
Abstract

Linkage between behavioral states and neural activity is one of the most important challenges in neuroscience. The network activity patterns in the awake resting state and in the actively behaving state in rodents are not well understood, and a better tool for differentiating these states can provide insights on healthy brain functions and its alteration with disease. Therefore, we simultaneously recorded local field potentials (LFPs) bilaterally in motor cortex and striatum, and measured locomotion from healthy, freely behaving rats. Here we analyze spectral characteristics of the obtained signals and present an algorithm for automatic discrimination of the awake resting and the behavioral states. We used the Support Vector Machine... (More)

Linkage between behavioral states and neural activity is one of the most important challenges in neuroscience. The network activity patterns in the awake resting state and in the actively behaving state in rodents are not well understood, and a better tool for differentiating these states can provide insights on healthy brain functions and its alteration with disease. Therefore, we simultaneously recorded local field potentials (LFPs) bilaterally in motor cortex and striatum, and measured locomotion from healthy, freely behaving rats. Here we analyze spectral characteristics of the obtained signals and present an algorithm for automatic discrimination of the awake resting and the behavioral states. We used the Support Vector Machine (SVM) classifier and utilized features obtained by applying discrete wavelet transform (DWT) on LFPs, which arose as a solution with high accuracy.

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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
pages
964 - 967
publisher
IEEE Computer Society
conference name
7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
external identifiers
  • scopus:84940386288
ISBN
9781467363891
DOI
10.1109/NER.2015.7146786
language
English
LU publication?
yes
id
9cbcd751-aa42-4ce2-b983-de41ff39ebea
date added to LUP
2017-06-16 09:52:25
date last changed
2017-09-17 09:49:19
@inproceedings{9cbcd751-aa42-4ce2-b983-de41ff39ebea,
  abstract     = {<p>Linkage between behavioral states and neural activity is one of the most important challenges in neuroscience. The network activity patterns in the awake resting state and in the actively behaving state in rodents are not well understood, and a better tool for differentiating these states can provide insights on healthy brain functions and its alteration with disease. Therefore, we simultaneously recorded local field potentials (LFPs) bilaterally in motor cortex and striatum, and measured locomotion from healthy, freely behaving rats. Here we analyze spectral characteristics of the obtained signals and present an algorithm for automatic discrimination of the awake resting and the behavioral states. We used the Support Vector Machine (SVM) classifier and utilized features obtained by applying discrete wavelet transform (DWT) on LFPs, which arose as a solution with high accuracy.</p>},
  author       = {Belić, Jovana J and Halje, Pär and Richter, Ulrike and Petersson, Per and Kotaleski, Jeanette Hellgren},
  booktitle    = {7th International IEEE/EMBS Conference on Neural Engineering, NER 2015},
  isbn         = {9781467363891},
  language     = {eng},
  month        = {07},
  pages        = {964--967},
  publisher    = {IEEE Computer Society},
  title        = {Behavior discrimination using a discrete wavelet based approach for feature extraction on local field potentials in the cortex and striatum},
  url          = {http://dx.doi.org/10.1109/NER.2015.7146786},
  year         = {2015},
}