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1.	Belic, Jovana, 1987-, et al. (author) Behavior Discrimination Using a Discrete Wavelet Based Approach for Feature Extraction on Local Field Potentials in the Cortex and Striatum 2015 In: 7th International IEEE/EMBS Conference on Neural Engineering (NER). - : IEEE conference proceedings. - 9781467363891 ; , s. 964-967 Conference paper (peer-reviewed)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 (SVM) classifier and utilized features obtained by applying discrete wavelet transform (DWT) on LFPs, which arose as a solution with high accuracy.

Belic, Jovana, 1987-, et al. (author)
Behavior Discrimination Using a Discrete Wavelet Based Approach for Feature Extraction on Local Field Potentials in the Cortex and Striatum
2015
In: 7th International IEEE/EMBS Conference on Neural Engineering (NER). - : IEEE conference proceedings. - 9781467363891 ; , s. 964-967
Conference paper (peer-reviewed)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 (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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