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Open AccessArticle

Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal

1
Istituto Di Ricovero e Cura a Carattere Scientifico (IRCCS), Centro Neurolesi Bonino-Pulejo, Via Palermo c/da Casazza, SS. 113, 98124 Messina, Italy
2
Dipartimento di Ingegneria Civile, dell'Energia, dell'Ambiente e dei Materiali (DICEAM), Mediterranean University of Reggio Calabria, Via Graziella Feo di Vito, 89060 Reggio Calabria, Italy
*
Author to whom correspondence should be addressed.
Entropy 2014, 16(12), 6553-6572; https://doi.org/10.3390/e16126553
Received: 31 July 2014 / Revised: 4 December 2014 / Accepted: 5 December 2014 / Published: 17 December 2014
(This article belongs to the Special Issue Entropy and Electroencephalography)
Electroencephalography (EEG) is a fundamental diagnostic instrument for many neurological disorders, and it is the main tool for the investigation of the cognitive or pathological activity of the brain through the bioelectromagnetic fields that it generates. The correct interpretation of the EEG is misleading, both for clinicians’ visual evaluation and for automated procedures, because of artifacts. As a consequence, artifact rejection in EEG is a key preprocessing step, and the quest for reliable automatic processors has been quickly growing in the last few years. Recently, a promising automatic methodology, known as automatic wavelet-independent component analysis (AWICA), has been proposed. In this paper, a more efficient and sensitive version, called enhanced-AWICA (EAWICA), is proposed, and an extensive performance comparison is carried out by a step of tuning the different parameters that are involved in artifact detection. EAWICA is shown to minimize information loss and to outperform AWICA in artifact removal, both on simulated and real experimental EEG recordings. View Full-Text
Keywords: automatic artifact rejection; electroencephalography; wavelet transform; independent component analysis; entropy automatic artifact rejection; electroencephalography; wavelet transform; independent component analysis; entropy
MDPI and ACS Style

Mammone, N.; Morabito, F.C. Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal. Entropy 2014, 16, 6553-6572.

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