Automated Detection and Selection of Artifacts in Encephalography Signals

Al-Kasasbeh, Riad Taha; Shamaseen, Mahdi Salman; Skopin, Dmitriy E.

doi:10.1007/s10527-009-9069-9

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…A large number of methods for artifact detection have been developed in the case of adult EEG signals [15,16,17,18], but these methods cannot be applied to neonatal EEG signals as the latter have much more diversity in their patterns [19]. Previous studies used features based on spectral, temporal, statistical properties and wavelet decomposition to discriminate artifacts from other abnormalities having similar morphologies [19]…”

Section: Newborn Eeg Artifact Detectionmentioning

confidence: 99%

Principles of time–frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection

Boashash

Azemi

Khan

2015

Pattern Recognition

101

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This paper considers the general problem of detecting change in non-stationary signals using features observed in the time-frequency (t, f) domain, obtained using a class of quadratic timefrequency distributions (QTFDs). The focus of this study is to propose a methodology to define new (t, f) features by extending time-only and frequency-only features to the joint (t, f) domain for detecting changes in non-stationary signals. The (t, f) features are used as a representative subset characterizing the status of the observed non-stationary signal. Change in the signal is then reflected as a change in the (t, f) features. This (t, f) approach is applied to the problem of detecting abnormal brain activity in newborns (e.g. seizure) using measurements of the EEG for diagnosis and prognosis. In addition, a pre-processing stage for detecting artifacts in EEG signals for signal enhancement is studied and implemented separately. Overall results indicate that, in general, the (t, f) approach results in an improved performance in detecting artifacts and seizures in newborn EEG signals as compared to time-only or frequency-only features.

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Section: Newborn Eeg Artifact Detectionmentioning

confidence: 99%

Principles of time–frequency feature extraction for change detection in non-stationary signals: Applications to newborn EEG abnormality detection

Boashash

Azemi

Khan

2015

Pattern Recognition

101

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“…While recording a brain signal, numerous artifacts include the signal. Different sorts of artifacts affecting the signal are open and last of eyes all through the signal acquisition process, muscular sports, and sports occurring withinside the background [6]. Therefore, EEG alerts ought to be recorded in de-noised labs the use of machines which might be loose from interferences, artifacts, or every other style of noise [7].…”

mentioning

confidence: 99%

EEG Channel Selection Using A Modified Grey Wolf Optimizer

Hussien

El-kenawy

El-Desouky

2021

EJECE

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Consider an increasingly growing field of research, Brain-Computer Interface (BCI) is to form a direct channel of communication between a computer and the brain. However, extracting features of random time-varying EEG signals and their classification is a major challenge that faces current BCI. This paper proposes a modified grey wolf optimizer (MGWO) that can select optimal EEG channels to be used in (BCIs), the way that identifies main features and the immaterial ones from that dataset and the complexity to be removed. This allows (MGWO) to opt for optimal EEG channels as well as helping machine learning classification in its tasks when doing training to the classifier with the dataset. (MGWO), which imitates the grey wolves leadership and hunting manner nature and which consider metaheuristics swarm intelligence algorithms, is an integration with two modification to achieve the balance between exploration and exploitation the first modification applies exponential change for the number of iterations to increase search space accordingly exploitation, the second modification is the crossover operation that is used to increase the diversity of the population and enhance exploitation capability. Experimental results use four different EEG datasets BCI Competition IV- dataset 2a, BCI Competition IV- data set III, BCI Competition II data set III, and EEG Eye State from UCI Machine Learning Repository to evaluate the quality and effectiveness of the (MGWO). A cross-validation method is used to measure the stability of the (MGWO).

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