Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data

Akhtar, Muhammad Tahir; Mitsuhashi, Wataru; James, Christopher J.

doi:10.1016/j.sigpro.2011.08.005

Cited by 174 publications

(108 citation statements)

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“…The main sources of artefacts are the EOG artefacts, ocular artefacts (eye movement and eye blink), noise muscles (EMG), heart signal, and various kinds of noise which is mixed with brain signals and often be the artefacts in EEG recordings [6][7][8][9][10]. Removal of artefacts is the real solution for quantitative analysis in the EEG recordings.…”

Section: Introductionmentioning

confidence: 99%

Artefacts Removal of EEG Signals with Wavelet Denoising

Turnip

Pardede

2017

MATEC Web Conf.

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Abstract. The recording of EEG signals often still contains many contaminants electrical signals that originate from non-cerebral origin such as ocular muscle activity called artefacts. The amplitude of artefacts can be quite large relative to the size of amplitude of the cortical signals of interest. In this paper, an application of wavelet denoising method for artefacts removal of EEG signals is proposed. The experiment result shows that contaminant artifact of EEG signals can significantly removed.

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Section: Introductionmentioning

confidence: 99%

Artefacts Removal of EEG Signals with Wavelet Denoising

Turnip

Pardede

2017

MATEC Web Conf.

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“…In the spatial domain techniques, the data from multiple spatially-distinct channels are utilized to identify the true signal projected onto all channels from the noise that is generally assumed to be independent among such channels. Such methods range from simple local spatial averaging to sophisticated variants of blind source separation methods such as independent component analysis, (Ramirez, Kopell, Butson, Hiner, & Baillet, 2011), (de Cheveigne & Simon, 2008), (Pires, Nunes, & Castelo-Branco, 2011), (Vorobyov & Cichocki, 2002), (Akhtar, Mitsuhashi, & James, 2012), (Geetha & Geethalakshmi, 2012). On the other hand, temporal domain techniques attempt to find similarities within the time domain of a single channel signal that can be used to identify and suppress the noise components in that signal.…”

Section: Introductionmentioning

confidence: 99%

A Survey on the Project in title "Unsupervised Adaptive P300 BCI in the Framework of Chaotic Theory and Stochastic Theory"

Alhaddad¹

2016

IJAERS

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Abstract-In this paper we present a survey of work that has been done in the project "Unsupervised Adaptive P300 BCI in the framework of chaotic theory and stochastic theory"we summarised the following papers, (Mohammed J Alhaddad & 2011), (Mohammed J. Alhaddad & Kamel M, 2012), (Mohammed J Alhaddad, Kamel, & Al-Otaibi, 2013), (Mohammed J Alhaddad, Kamel, & Bakheet, 2013), (Mohammed J Alhaddad, Kamel, & Al-Otaibi, 2014), (Mohammed J Alhaddad, Kamel, & Bakheet, 2014), (Mohammed J Alhaddad, Kamel, & Kadah, 2014), (Mohammed J Alhaddad, Kamel, Makary, Hargas, & Kadah, 2014), (Mohammed J Alhaddad, Mohammed, Kamel, & Hagras, 2015

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“…Grunwald et al [17] presented a study about spontaneous facial self-touch gestures (sFSTG); the aim of the study was to investigate whether sFSTG are associated with specific changes in the electrical brain activity that might indicate an involvement of regulatory emotional processes and working memory. Akhtar et al [18] proposed a framework, based on ICA and wavelet denoising (WD), to improve the pre-processing of EEG signals. Zeng et al [19] proposed an approach to remove ocular artifacts from the raw EEG recording.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal

Mammone

Morabito

2014

Entropy

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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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Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data

Cited by 174 publications

References 40 publications

Artefacts Removal of EEG Signals with Wavelet Denoising

Artefacts Removal of EEG Signals with Wavelet Denoising

A Survey on the Project in title "Unsupervised Adaptive P300 BCI in the Framework of Chaotic Theory and Stochastic Theory"

Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal

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