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

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


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.


External organisations
  • KTH Royal Institute of Technology
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Neurosciences
  • Other Medical Engineering
Original languageEnglish
Title of host publication7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
PublisherIEEE Computer Society
ISBN (Electronic)9781467363891
Publication statusPublished - 2015 Jul 1
Publication categoryResearch
Event7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 - Montpellier, France
Duration: 2015 Apr 222015 Apr 24


Conference7th International IEEE/EMBS Conference on Neural Engineering, NER 2015