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Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling

Authors
Journal
Biomedical Signal Processing and Control
1746-8094
Publisher
Elsevier
Volume
7
Issue
4
Identifiers
DOI: 10.1016/j.bspc.2011.06.005
Keywords
  • Eeg
  • Artefact Elimination
  • Bss
  • Supervised Classification
  • Wavelet Denoising
  • Medical Validation
Disciplines
  • Medicine

Abstract

Abstract This paper proposes an automatic method for artefact removal and noise elimination from scalp electroencephalogram recordings (EEG). The method is based on blind source separation (BSS) and supervised classification and proposes a combination of classical and news features and classes to improve artefact elimination (ocular, high frequency muscle and ECG artefacts). The role of a supplementary step of wavelet denoising (WD) is explored and the interactions between BSS, denoising and classification are analyzed. The results are validated on simulated signals by quantitative evaluation criteria and on real EEG by medical expertise. The proposed methodology successfully rejected a good percentage of artefacts and noise, while preserving almost all the cerebral activity. The “denoised artefact-free” EEG presents a very good improvement compared with recorded raw EEG: 96% of the EEGs are easier to interpret.

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