Threshold adaptation in automatic wavelet-ICA for electroencephalographic artifact removal

Nadia Mammone, Aime Lay-Ekuakille, Patrizia Vergallo, Francesco C. Morabito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Electroencephalography (EEG) is a well established methodology to record the electrical activity of the brain. We can be interested in monitoring the cerebral electrical activity for different purposes: studying the cognitive activity, interfacing the brain with the machine, extracting diagnostic information, etc. Artifacts are unwelcome signals, generated by electromagnetic sources not related to cerebral activity, that may overlap to the EEG signals and affect their processing. Whatever the goal of EEG processing, a preprocessing step consisting in artifact removal is normally required. Unfortunately, artifact removal is unavoidably a lossy procedure, therefore, the goal must be removing artifacts losing the minimum amount of useful information embedded in the EEG. To this purpose, Automatic Wavelet-ICA was recently proposed by the authors. The technique is multistep and parameter dependent, thus its performance may vary significantly with the parameter setting. The present paper shows the results of the optimization with respect to the threshold used for artifact detection.

Original languageEnglish
Title of host publication3rd IMEKO TC13 Symposium on Measurements in Biology and Medicine 2014: New Frontiers in Biomedical Measurements
PublisherIMEKO-International Measurement Federation Secretariat
Pages99-105
Number of pages7
ISBN (Print)9781634398206
Publication statusPublished - 2014
Event3rd IMEKO TC13 Symposium on Measurements in Biology and Medicine 2014: New Frontiers in Biomedical Measurements - Lecce, Italy
Duration: Apr 17 2014Apr 18 2014

Other

Other3rd IMEKO TC13 Symposium on Measurements in Biology and Medicine 2014: New Frontiers in Biomedical Measurements
CountryItaly
CityLecce
Period4/17/144/18/14

Keywords

  • Automatic artifact rejection
  • Electroencephalography
  • Entropy
  • Independent component analysis
  • Wavelet transform

ASJC Scopus subject areas

  • Health Informatics
  • Biomedical Engineering

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  • Cite this

    Mammone, N., Lay-Ekuakille, A., Vergallo, P., & Morabito, F. C. (2014). Threshold adaptation in automatic wavelet-ICA for electroencephalographic artifact removal. In 3rd IMEKO TC13 Symposium on Measurements in Biology and Medicine 2014: New Frontiers in Biomedical Measurements (pp. 99-105). IMEKO-International Measurement Federation Secretariat.