Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings

Giuseppina Inuso; Fabio La Foresta; Nadia Mammone; Francesco Carlo Morabito

doi:10.1109/IJCNN.2007.4371184

Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings

Giuseppina Inuso, Fabio La Foresta, Nadia Mammone, Francesco Carlo Morabito

IRCCS Centro Neurolesi "Bonino Pulejo"

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Electroencephalographic (EEG) recordings are often contaminated by the artifacts, signals that have non-cerebral origin and that might mimic cognitive or pathologic activity and therefore distort the analysis of EEG. In this paper the issue of artifact extraction from Electroencephalographic data is addressed and a new technique for EEG artifact removal, based on the joint use of Wavelet transform and Independent Component Analysis (WICA), is presented and compared to two other techniques based on ICA and wavelet denoising. An artificial artifact-laden EEG dataset was created mixing a real EEG with a set of synthesized artifacts. This dataset was processed by WICA and the two other methods. The proposed technique had the best artifact separation performance for every kind of artifact also allowing for the minimum information loss.

Original language	English
Title of host publication	IEEE International Conference on Neural Networks - Conference Proceedings
Pages	1524-1529
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2007.4371184
Publication status	Published - 2007
Event	2007 International Joint Conference on Neural Networks, IJCNN 2007 - Orlando, FL, United States Duration: Aug 12 2007 → Aug 17 2007

Other

Other	2007 International Joint Conference on Neural Networks, IJCNN 2007
Country/Territory	United States
City	Orlando, FL
Period	8/12/07 → 8/17/07

ASJC Scopus subject areas

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10.1109/IJCNN.2007.4371184

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Inuso, G, La Foresta, F, Mammone, N & Morabito, FC 2007, Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings. in IEEE International Conference on Neural Networks - Conference Proceedings., 4371184, pp. 1524-1529, 2007 International Joint Conference on Neural Networks, IJCNN 2007, Orlando, FL, United States, 8/12/07. https://doi.org/10.1109/IJCNN.2007.4371184

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title = "Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings",

abstract = "Electroencephalographic (EEG) recordings are often contaminated by the artifacts, signals that have non-cerebral origin and that might mimic cognitive or pathologic activity and therefore distort the analysis of EEG. In this paper the issue of artifact extraction from Electroencephalographic data is addressed and a new technique for EEG artifact removal, based on the joint use of Wavelet transform and Independent Component Analysis (WICA), is presented and compared to two other techniques based on ICA and wavelet denoising. An artificial artifact-laden EEG dataset was created mixing a real EEG with a set of synthesized artifacts. This dataset was processed by WICA and the two other methods. The proposed technique had the best artifact separation performance for every kind of artifact also allowing for the minimum information loss.",

author = "Giuseppina Inuso and {La Foresta}, Fabio and Nadia Mammone and Morabito, {Francesco Carlo}",

year = "2007",

doi = "10.1109/IJCNN.2007.4371184",

language = "English",

isbn = "142441380X",

pages = "1524--1529",

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note = "2007 International Joint Conference on Neural Networks, IJCNN 2007 ; Conference date: 12-08-2007 Through 17-08-2007",

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AU - Inuso, Giuseppina

AU - La Foresta, Fabio

AU - Mammone, Nadia

AU - Morabito, Francesco Carlo

PY - 2007

Y1 - 2007

N2 - Electroencephalographic (EEG) recordings are often contaminated by the artifacts, signals that have non-cerebral origin and that might mimic cognitive or pathologic activity and therefore distort the analysis of EEG. In this paper the issue of artifact extraction from Electroencephalographic data is addressed and a new technique for EEG artifact removal, based on the joint use of Wavelet transform and Independent Component Analysis (WICA), is presented and compared to two other techniques based on ICA and wavelet denoising. An artificial artifact-laden EEG dataset was created mixing a real EEG with a set of synthesized artifacts. This dataset was processed by WICA and the two other methods. The proposed technique had the best artifact separation performance for every kind of artifact also allowing for the minimum information loss.

AB - Electroencephalographic (EEG) recordings are often contaminated by the artifacts, signals that have non-cerebral origin and that might mimic cognitive or pathologic activity and therefore distort the analysis of EEG. In this paper the issue of artifact extraction from Electroencephalographic data is addressed and a new technique for EEG artifact removal, based on the joint use of Wavelet transform and Independent Component Analysis (WICA), is presented and compared to two other techniques based on ICA and wavelet denoising. An artificial artifact-laden EEG dataset was created mixing a real EEG with a set of synthesized artifacts. This dataset was processed by WICA and the two other methods. The proposed technique had the best artifact separation performance for every kind of artifact also allowing for the minimum information loss.

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BT - IEEE International Conference on Neural Networks - Conference Proceedings

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Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings

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