Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Current Directions in Biomedical Engineering

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Buzug, Thorsten M. / Haueisen, Jens / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Kraft, Marc / Lenarz, Thomas / Leonhardt, Steffen / Malberg, Hagen / Penzel, Thomas / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Urban, Gerald A.


CiteScore 2018: 0.47

Source Normalized Impact per Paper (SNIP) 2018: 0.377

Open Access
Online
ISSN
2364-5504
See all formats and pricing
More options …

Improving electrocorticograms of awake and anaesthetized mice using wavelet denoising

Michael Schweigmann
  • Corresponding author
  • Department of Electrical Engineering, Trier University of Applied Sciences, Trier, Germany
  • Department of Molecular Physiology, CIPMM, University of Saarland, Homburg, Germany
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Klaus Peter Koch / Fabian Auler / Frank Kirchhoff
Published Online: 2018-09-22 | DOI: https://doi.org/10.1515/cdbme-2018-0112

Abstract

The quality of bioelectrical signals is essential for functional evaluation of cellular circuits. The electrical activity recorded from the cortical brain surface represents the average of many individual synaptic processes. By downsizing micro-electrode arrays, the spatial resolution of electrocortico-grams (ECoGs) can be increased. But, upon increasing electrode impedance, recorded noise from the electrode-tissue interface and the surroundings will become more prominent. Frequently, signal interpretation is improved by post-processing using filtering or pattern recognition. For a variety of applications, wavelet denoising has become an accepted tool. Here, we present how wavelet denoising affects the signal-to-noise ratio of ECoGs. The recording qualities from awake and anesthetized mice was artificially reduced by adding two noise models prior to filtering. Raw and filtered signals were compared by calculating the linear correlation coefficient.

Keywords: Electrocorticograms; noise models; denoising; correlation coefficient; mouse model

About the article

Published Online: 2018-09-22

Published in Print: 2018-09-01


Citation Information: Current Directions in Biomedical Engineering, Volume 4, Issue 1, Pages 469–472, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2018-0112.

Export Citation

© 2018 by Walter de Gruyter Berlin/Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Qing Lu, Lixin Pang, Haoqian Huang, Chong Shen, Huiliang Cao, Yunbo Shi, and Jun Liu
Micromachines, 2019, Volume 10, Number 2, Page 134

Comments (0)

Please log in or register to comment.
Log in