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

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Standard

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; Richter, Ulrike; Petersson, Per; Kotaleski, Jeanette Hellgren.

7th International IEEE/EMBS Conference on Neural Engineering, NER 2015. IEEE Computer Society, 2015. s. 964-967 7146786.

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceeding

Harvard

Belić, JJ, Halje, P, Richter, U, Petersson, P & Kotaleski, JH 2015, Behavior discrimination using a discrete wavelet based approach for feature extraction on local field potentials in the cortex and striatum. i 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015., 7146786, IEEE Computer Society, s. 964-967, Montpellier, Frankrike, 2015/04/22. https://doi.org/10.1109/NER.2015.7146786

APA

Belić, J. J., Halje, P., Richter, U., Petersson, P., & Kotaleski, J. H. (2015). Behavior discrimination using a discrete wavelet based approach for feature extraction on local field potentials in the cortex and striatum. I 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 (s. 964-967). [7146786] IEEE Computer Society. https://doi.org/10.1109/NER.2015.7146786

CBE

Belić JJ, Halje P, Richter U, Petersson P, Kotaleski JH. 2015. Behavior discrimination using a discrete wavelet based approach for feature extraction on local field potentials in the cortex and striatum. I 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015. IEEE Computer Society. s. 964-967. https://doi.org/10.1109/NER.2015.7146786

MLA

Belić, Jovana J et al. "Behavior discrimination using a discrete wavelet based approach for feature extraction on local field potentials in the cortex and striatum". 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015. IEEE Computer Society. 2015, 964-967. https://doi.org/10.1109/NER.2015.7146786

Vancouver

Belić JJ, Halje P, Richter U, Petersson P, Kotaleski JH. Behavior discrimination using a discrete wavelet based approach for feature extraction on local field potentials in the cortex and striatum. I 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015. IEEE Computer Society. 2015. s. 964-967. 7146786 https://doi.org/10.1109/NER.2015.7146786

Author

Belić, Jovana J ; Halje, Pär ; Richter, Ulrike ; Petersson, Per ; Kotaleski, Jeanette Hellgren. / Behavior discrimination using a discrete wavelet based approach for feature extraction on local field potentials in the cortex and striatum. 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015. IEEE Computer Society, 2015. s. 964-967

RIS

TY - GEN

T1 - Behavior discrimination using a discrete wavelet based approach for feature extraction on local field potentials in the cortex and striatum

AU - Belić, Jovana J

AU - Halje, Pär

AU - Richter, Ulrike

AU - Petersson, Per

AU - Kotaleski, Jeanette Hellgren

PY - 2015/7/1

Y1 - 2015/7/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84940386288&partnerID=8YFLogxK

U2 - 10.1109/NER.2015.7146786

DO - 10.1109/NER.2015.7146786

M3 - Paper in conference proceeding

SP - 964

EP - 967

BT - 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015

PB - IEEE Computer Society

ER -