Artifact Processing of Epileptic EEG Signals: An Overview of Different Types of Artifacts

Hamal, Abdul Qayoom; Rehman, Abdul

doi:10.1109/acsat.2013.77

Cited by 14 publications

(8 citation statements)

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“…Such ocular signals can be recorded using electrooculogram (EOG). The amplitude of EOG is generally many times greater than EEG [ 25 , 26 , 27 ] and its frequency are similar with the frequency of EEG signals. Worthy to note that not only EEG data can be contaminated by the EOG, but in turn, EOG can also be contaminated by EEG.…”

Section: Introductionmentioning

confidence: 99%

“…Contamination of EEG data by muscle activity is a well-recognized tough problem as it arises from different type of muscle groups [ 27 , 29 ]. These artifacts can be caused by any muscle proximity to signal recording sites contraction and stretch, the subject talks, sniffs, swallows [ 21 ], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Cardiac artifacts can be introduced when the electrodes is placed on or near a blood vessel [ 29 ], in which the movement of expansion and contraction due to the heart. Such artifacts called pulse artifacts, whose frequency is around 1.2 Hz, can occur within EEG as a similar waveform, hence it is hard to remove [ 27 ]. Another cardiac activity known as ECG measure the electrical signal outputted from the heart [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

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Removal of Artifacts from EEG Signals: A Review

Jiang

Bian

Tian

2019

Sensors

563

329

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Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Removal of Artifacts from EEG Signals: A Review

Jiang

Bian

Tian

2019

Sensors

563

329

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“…All details about the MKNLMS-CS algorithm and its parameters' setting can be found in [41]. The MKNLMS-CS technique managed not only to work efficiently on non-stationary EEG signals, but also to provide accurate artifacts removal using a small dictionary size, demonstrating its low computational requirements compared to other adaptive filtering methods [42,43]. Moreover, the proposed adaptive filter design succeeded to eliminate efficiently EMG and EOG interferences, while maintaining the essential characteristics of EEG signal that distinguish healthy, ictal, and inter-ictal cases.…”

Section: Eeg Acquisition and Preprocessingmentioning

confidence: 99%

“…Such unwanted artifacts can significantly deteriorate the EEG's signal-to-noise ratio and lead to inaccurate clinical interpretations of epileptic EEG signals [41,42]. This reveals that any epileptic seizure diagnosis system based on raw EEG data without removing the artifact sources may lead to spurious classification results [43].…”

Section: Introductionmentioning

confidence: 99%

Identification and classification of epileptic EEG signals using invertible constant-Q transform-based deep convolutional neural network

Eltrass¹,

Tayel²,

Elqady³

2022

J. Neural Eng.

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Context. Epilepsy is the most widespread disorder of the nervous system, affecting humans of all ages and races. The most common diagnostic test in epilepsy is the Electroencephalogram (EEG). Objective. In this paper, a novel automated Deep Learning (DL) approach based on integrating a pre-trained Convolutional Neural Network (CNN) structure, called AlexNet, with the Constant-Q Non-Stationary Gabor Transform (CQ-NSGT) algorithm is proposed for classifying seizure versus seizure-free EEG records. Approach. The CQ-NSGT method is introduced to transform the input 1-D EEG signal into 2-D spectrogram which is sent to the AlexNet CNN model. The AlexNet architecture is utilized to capture the discriminating features of the 2-D image corresponding to each EEG signal in order to distinguish seizure and non-seizure subjects using Multi-Layer Perceptron (MLP) algorithm. Main Results. The robustness of the introduced CQ-NSGT technique in transforming the 1-D EEG signals into 2-D spectrograms is assessed by comparing its classification results with the Continuous Wavelet Transform (CWT) method, and the results elucidate the high performance of the CQ-NSGT technique. The suggested epileptic seizure classification framework is investigated with clinical EEG data acquired from the Bonn University database, and the experimental results reveal the superior performance of the proposed framework over other state-of-the-art approaches with an accuracy of 99.56 %, sensitivity of 99.12 %, specificity of 99.67 %, and precision of 98.69 %. Significance. This elucidates the importance of the proposed automated system in helping neurologists to accurately interpret and classify epileptic EEG records without necessitating tedious visual inspection or massive data analysis for long-term EEG signals.

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