Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings

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

doi:10.1109/ijcnn.2007.4371184

Cited by 85 publications

(58 citation statements)

References 6 publications

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“…Also, FastICA has many significant advantages, such as faster convergence speed of iteration and better performance of noise immunity. At the same time, despite ICA has been vastly employed in many methods to reject artifacts from EEG, its capabilities to detect some artifact types are limited, especially when these artifacts overlap with the true EEG [16,17]. Also, its performance depends on the size of the data set and cannot filter the artifacts without discarding the true signals as well, resulting in some effective data loss [18].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Hybrid Approach for Artifacts Removal from EEG Recordings

Helal¹,

Seddik²,

Hussein³

2017

IJCA

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The electroencephalogram (EEG) is a widely used traditional procedure for diagnosing, monitoring and managing neurological disorders. Many artifact types that often contaminate EEG remain a key challenge for precise diagnosis of brain dysfunctions and neurological disorders. Hence, artifact removal is intuitively required for accurate EEG analysis and treatment. This paper presents a new extensive method that can remove a wide variety of EEG artifacts based mainly on Template Matching approach including multiple signal-processing tools. The method was evaluated and validated on real EEG data, giving promising results that offer better capabilities to neurophysiologists in routine EEG examinations and diagnosis.

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Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Hybrid Approach for Artifacts Removal from EEG Recordings

Helal¹,

Seddik²,

Hussein³

2017

IJCA

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“…The sampling rate is 100 Hz, and the time duration is 7.5 s. The electrode montage and the EEG recording are shown in [24].…”

Section: Semi-simulated Eegmentioning

confidence: 99%

“…In order to remove artifacts from the EEG automatically, a technique, automatic wavelet-independent component analysis (AWICA), was recently proposed by the authors ( [22][23][24][25][26][27]), which performs automatic artifact rejection, while not discarding any epoch of the EEG recording.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal

Mammone

Morabito

2014

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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.

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“…This procedure fails when the artifacts do not have these properties, for example DWT approach cannot remove Electro Cardio Graphic (ECG) artifact present in some myoelectric recordings (Inuso et al, 2007b;La Foresta et al, 2005) because the spectral components of artifact overlap with the myoelectric signal spectrum. On the other hand, the ICA method, its implementations and its applications are well described in literature and a review on artifact identification and removal, with special emphasis on the ocular ones, can be also found.…”

Section: Ajasmentioning

confidence: 99%

Independent Component Analysis and Discrete Wavelet Transform for Artifact Removal in Biomedical Signal Processing

Calcagno

Foresta

Versaci

2014

American Journal of Applied Sciences

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Recent works have shown that artifact removal in biomedical signals can be performed by using Discrete Wavelet Transform (DWT) or Independent Component Analysis (ICA). It results often very difficult to remove some artifacts because they could be superimposed on the recordings and they could corrupt the signals in the frequency domain. The two conditions could compromise the performance of both DWT and ICA methods. In this study we show that if the two methods are jointly implemented, it is possible to improve the performances for the artifact rejection procedure. We discuss in detail the new method and we also show how this method provides advantages with respect to DWT of ICA procedure. Finally, we tested the new approach on real data.

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Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings

Cited by 85 publications

References 6 publications

A Hybrid Approach for Artifacts Removal from EEG Recordings

A Hybrid Approach for Artifacts Removal from EEG Recordings

Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal

Independent Component Analysis and Discrete Wavelet Transform for Artifact Removal in Biomedical Signal Processing

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