Techniques for noise removal from EEG, EOG, and airflow signals in sleep patients

Berryman, Matthew J.; Messer, Sheila R.; Allison, Andrew; Abbott, Derek

doi:10.1117/12.546637

Cited by 3 publications

(1 citation statement)

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“…Our first aim is thus to study the interaction between BSS and WD. Rather few references combine them, either for ECG [28] or for EEG preprocessing [29,30,31,32]. However, the only detailed study on the interaction is presented in [33].…”

Section: Bss and Wdmentioning

confidence: 99%

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

Vázquez

Vélez-Pérez

Ranta

et al. 2012

Biomedical Signal Processing and Control

155

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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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