Artifact Removal from EEG using Spatially Constrained Independent Component Analysis and Wavelet Denoising with Otsu's Thresholding Technique

Geetha, G.; Geethalakshmi, S. N.

doi:10.1016/j.proeng.2012.01.964

Cited by 32 publications

(11 citation statements)

References 12 publications

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“…As the next step, Wavelet De -noising (WD) is applied to ext ract the brain act ivity fro m purged artifacts, and finally the artifacts are projected back and subtracted from EEG signals to get clean EEG data. Here, thresholding plays an important role in delineating the artifacts and hence a better thresholding technique called Otsu', thresholding is applied [50]. In this paper authors presented a new algorith m using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial and Transform Domain Filtering Method for Image De-noising: A Review

Roy¹

2013

IJMECS

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Present investigation reveals the quantum of work carried in the filtering methods for image denoising. An image is often gets corrupted by various noises that are visible or invisible while being gathered, coded, acquired and transmitted. Noise influences various process parameters that may cause a quality problem for further image processing. De-noising of natural images is appears to be very simp le however when considered under practical situations becomes complex. It has been cited by various author that parameter such as type and quantum of noise, image etc. through single algorith m or approach becomes cumbersome when results are optimized. In order to improve the quality of an image noise must be removed when the image is pre-p rocessed and the important signal features like edge details should be retained as much as possible. The search on efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. This paper reviews significant de-noising methods (spatial and transform domain method) and their salient features and applications. One filter in each category has been taken in consideration to understand the characteristics of both spatial and transform do main filters.

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Section: Literature Reviewmentioning

confidence: 99%

Spatial and Transform Domain Filtering Method for Image De-noising: A Review

Roy¹

2013

IJMECS

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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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“…There are different kinds of artifacts such as power line noise, muscle contraction or electromyogram (EMG), heart activity or electrocardiogram (ECG), and eye movement or electrooculogram (EOG) [1,6]. Therefore, the removal artifacts process is required to obtain the original brain signals.…”

Section: Introductionmentioning

confidence: 99%

“…Not only signal recording methods is diverse, signal processing method are also diverse. Among of them is independent component analysis (ICA) [6], principal component analysis (PCA) [7], adaptive filters, neural networks [8], non-liniear PCA [9], wavelet denoising [1,6], autoregressive (AR) [10], etc. This paper will discuss how to removing artifacts from eye movement (EOG).…”

Section: Introductionmentioning

confidence: 99%

Removal artifacts from EEG signal using independent component analysis and principal component analysis

Turnip

Junaidi

2014

2014 2nd International Conference on Technology, Informatics, Management, Engineering &Amp; Environment

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In recording the EEG signals are often contamination signal called artifacts. There are different kinds of artifacts such as power line noise, electromyogram (EMG), electrocardiogram (ECG) and electrooculogram (EOG). This research will compare two methods for removing artifacts, i.e. ICA and PCA methods. EEG signals are recorded on three conditions, which is normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of the EEG signal is obtained in the range of 12-14 Hz (alpha-beta) either on normal conditions, closed eyes, and blinked eyes. From processing with ICA and PCA methods found that ICA method are better than PCA in terms of the separation of the EEG signals from mixed signals.

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Artifact Removal from EEG using Spatially Constrained Independent Component Analysis and Wavelet Denoising with Otsu's Thresholding Technique

Cited by 32 publications

References 12 publications

Spatial and Transform Domain Filtering Method for Image De-noising: A Review

Spatial and Transform Domain Filtering Method for Image De-noising: A Review

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

Removal artifacts from EEG signal using independent component analysis and principal component analysis

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