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Ocular artifact elimination from electroencephalography signals: A systematic review

Ranjan Rakesh, Chandra Sahana Bikash, Kumar Bhandari Ashish

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

960--996

EN

Electroencephalography (EEG) is the signal of intrigue that has immense application in the clinical diagnosis of various neurological, psychiatric, psychological, psychophysiological, and neurocognitive disorders. It is significantly crucial in neural communication, brain-computer interface, and other practical tasks. EEG signal is exceptionally susceptible to artifacts, which are external noise signals originated from non-cerebral regions. The interference of artifacts in EEG signals can potentially affect the original recorded EEG signal quality and pattern. Therefore, artifact removal from EEG signal is critically important before applying it to a specific task for accurate outcomes. Researchers have proposed numerous techniques to remove various artifacts present in the contaminated EEG signal. However, neither optimum method nor criterion stands standard for endorsement of clinically recorded EEG signals. Therefore, the research related to artifact elimination from EEG signal is challenging and perplexing task. This paper attempts to give an extensive outline of the advancement in methodologies to eliminate one of the most common artifacts, i.e., ocular artifact. It is anticipated that the study will enlighten the researchers on all the existing ocular artifact elimination techniques with a validated simulation model on the recorded EEG signal. In future advancements, Standard norms in artifact elimination techniques are expected to diminish the neurologist’s load by substantiating the clinical diagnosis after gaining correct information from artifact-free EEG signals.

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Modified Atom Search Optimization-based Deep Recurrent Neural Network for epileptic seizure prediction using electroencephalogram signals

Borhade Ratnaprabha Ravindra, Nagmode Manoj S.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1638--1653

EN

Context and background: Epilepsy is considered as the common neurological disease in the world. Early prediction of epileptic seizure gained great influence on the epileptic patient's life. Epileptic patients suffer from unpredictable conditions that may occur at any moment. Motivation: Various epileptic seizure prediction methods are introduced for accurately predicting the pre-ictal state of human brain, but to determine the discriminative features poses a major challenge in the medical sector. Hypothesis: Develop a technique for epileptic seizure prediction using electroencephalogram signals that detects the epileptic seizure automatically. Method: In this research, an effective optimization algorithm, named Modified Atom Search Optimization-based Deep Recurrent Neural Network is proposed to perform accurate seizure prediction with less computation time. Here, the Deep Recurrent Neural Network classifier per-forms the seizure prediction using various hidden layers associated in the hierarchy layer based on the optimally selected features. The proposed Modified Atom Search Optimization algorithm is designed using the Squirrel Search Algorithm and Atom Search Optimization. It is worth interesting to note that the proposed Modified Atom Search Optimization-based Deep Recurrent Neural Network performed early and accurate seizure prediction using electroencephalogram signals. Result: The analysis of the proposed SASO-based Deep RNN is carried out using CHB-MIT Scalp EEG dataset using the metrics, namely accuracy, sensitivity, and specificity. The proposed algorithm obtained better performance in terms of specificity, accuracy, and sensitivity with the values of 97.536%, 96.545%, and 96.520% by varying training percentage, and 93.736%, 94.128%, and 96.520% by varying K-fold value. Conclusion: The proposed method has significant benefits like, faster convergence rate, easy to implement, low complexity, high speed, and robustness. The weights of the classifier are optimally trained using the proposed algorithm in order to reveal the effectiveness of prediction performance.

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Zastosowanie sieci neuronowej opartej na parametrach charakterystyki widma sygnału EEG w celu rozróżnienia ruchu i zamiaru ruchu oraz przynależności personalnej sygnału

Broniec A.

Acta Bio-Optica et Informatica Medica. Inżynieria Biomedyczna

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2009

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Vol. 15, nr 3

244-247

PL

Badanie zmian zachodzących w sygnale elektroencefalograficznym (EEG) pod wpływem zadanego bodźca wymaga założenia, że ta reakcja ma własną i powtarzalną charakterystykę, w przeciwieństwie do ciągłej, spontanicznej aktywności mózgu, która w tym kontekście może być traktowana jako addytywny szum. Na podstawie powyższego założenia podzielono otrzymane w eksperymencie próbki sygnału EEG na grupy danych, związanych z odpowiedzią mózgu na zadane bodźce. Artykuł przedstawia wyniki otrzymane po zastosowaniu prostej sieci neuronowej LVQ (learning vector quantization) do rozróżnienia otrzymanych w eksperymencie danych. Porównano dane związane z ruchem palca i z wyobrażeniem ruchu palcem oraz przeprowadzono rozróżnienie danych otrzymanych od różnych osób. Zastosowanie sieci neuronowej samoorganizującej LVQ, opartej na parametrach charakterystyki widma EEG, pozwoliło na rozróżnienie przynależności personalnej sygnału EEG pomiędzy dwiema osobami ze średnią skutecznością 87,31% oraz pomiędzy czterema osobami ze średnią skutecznością 77,39%.

EN

The evaluation of changes within electroencephalography signal (EEG) occurred in response to stimuli, requires the assumption that this reaction has its own, repeatable characteristics compare to the continuous spontaneous brain activity, which can be treated in this context as the additive noise. Therefore, the recorded EEG signal samples, were divided into data groups connected with brain response to the stimuli. The simple neural network LVQ (learning vector quantization) was applied to evaluate recorded data. Movement of finger and voluntary intention of movement were examined. The application of simple neural network LVQ based on parameters of EEG-signal spectrum characteristics allowed for differentiation of EEG signal between two persons with the average efficiency of 87.31% and between four persons with the 77.39% accuracy.