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Article Dans Une Revue Biomedical Signal Processing and Control Année : 2012

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

Résumé

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 re- sults 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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Dates et versions

hal-00600103 , version 1 (13-06-2011)

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Rebeca Romo-Vázquez, Hugo Velez-Perez, Radu Ranta, Valérie Louis-Dorr, Didier Maquin, et al.. Blind source separation, wavelet denoising and discriminant analysis for EEG artefacts and noise cancelling. Biomedical Signal Processing and Control, 2012, 7 (4), pp.389-400. ⟨10.1016/j.bspc.2011.06.005⟩. ⟨hal-00600103⟩
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