Automatic noise-level detection for extra-cellular micro-electrode recordings

Kevin Dolan*, H.C.F. Martens, P.R. Schuurman, L.J. Bour

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

26 Citations (Scopus)

Abstract

Extra-cellular neuro-recording signals used for functional mapping in deep brain stimulation (DBS) surgery and invasive brain computer interfaces, may suffer from poor signal to noise ratio. Therefore, a reliable automatic noise estimate is essential to extract spikes from recordings. We show that current methods are biased toward overestimation of noise-levels with increasing neuronal activity or artifacts. An improved and novel method is proposed that is based on an estimate of the mode of the distribution of the signal envelope. Our method makes use of the inherent characteristics of the noise distribution. For band-limited Gaussian noise the envelope of the signal is known to follow the Rayleigh distribution. The location of the peak of this distribution provides a reliable noise-level estimate. It is demonstrated that this new 'envelop' method gives superior performance both on simulated data, and on actual micro-electrode recordings made during the implantation surgery of DBS electrodes for the treatment of Parkinson's disease.

Original languageEnglish
Pages (from-to)791-800
Number of pages10
JournalMedical and Biological Engineering and Computing
Volume47
Issue number7
DOIs
Publication statusPublished - 3 Jun 2009
Externally publishedYes

Keywords

  • Deep brain stimulation
  • Electrophysiology
  • Micro-electrode recordings
  • Noise estimation
  • Spike detection

Fingerprint Dive into the research topics of 'Automatic noise-level detection for extra-cellular micro-electrode recordings'. Together they form a unique fingerprint.

Cite this