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Methods for automatic detection of artifacts in microelectrode recordings.

Eduard Bakštein1, Tomáš Sieger2, Jiří Wild3

  • 1Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic; National Institute of Mental Health, Klecany, Czech Republic.

Journal of Neuroscience Methods
|July 25, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for automatically detecting noise in microelectrode recordings (MER). These advanced techniques effectively identify and remove artifacts, improving data quality for neuronal activity studies.

Keywords:
Artifact detectionExternal noiseMicroelectrode recordingsSupervised classification

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Biomedical Engineering

Background:

  • Microelectrode recording (MER) is crucial for studying neuronal activity.
  • High-quality MER data is essential for accurate analysis, but often corrupted by noise and artifacts.
  • Noise, including electromagnetic interference and motion artifacts, can impact over 25% of MER data.

Purpose of the Study:

  • To develop and evaluate automatic methods for detecting noise and artifacts in MER signals.
  • To improve the reliability and efficiency of MER data analysis pipelines.

Main Methods:

  • Proposed methods include unsupervised stationary segment detection, power spectral density analysis, and a classifier using time- and frequency-domain features.
  • Methods were evaluated on a large database of 5735 MER signals from Parkinson's disease patients.
  • Compared performance against existing unsupervised change-point detection techniques.

Main Results:

  • The best classifiers (bagging, decision tree) achieved up to 89% accuracy in artifact classification on unseen data.
  • Outperformed unsupervised methods by 5-10%, approaching human inter-rater agreement (93.5%).

Conclusions:

  • The developed methods are effective for automatic MER denoising.
  • These techniques can significantly aid in the efficient removal of signal artifacts, enhancing data quality for neuroscience research.