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Methods for detecting and removing ocular artifacts from EEG signals in drowsy driving warning systems: A survey

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Abstract

In the last decade, drowsiness while driving has been identified as a major factor behind a large number of fatal traffic accidents around the world. This highly prevalent problem has caused significant loss of life, injuries, property damage, and economic losses in many parts of the world. For that, great efforts have been made to introduce driver’s drowsiness detection systems for reducing and preventing traffic accidents in many cities in the world. Among the existing driver assistance systems, the one based on the EEG signal measurement is the most popular and relevant system. Nevertheless, EEG signals can be easily altered by many kinds of artifacts arising from sources other than the brain, such as muscle (EMG), cardiac (ECG), and ocular (EOG) activities. In the midst of them, ocular artifacts are one of the most important noise sources among the others. In this paper, we give an in-depth review on techniques used to detect and eliminate ocular artifacts from EEG recordings for all potential EEG-based drowsiness warning applications. Initially, we present an overview of some significant artifact types that can be observed in EEG signals and we study their impact on drowsiness detection applications. Subsequently, we review many approaches to artifact rejection, categorize and compare them based on their ability to eliminate EOG artifacts. Finally, we provide an innovative idea based on the IoT Cloud, which might be the succeeding step for safe driving, for alerting the driver when is getting drowsy.

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Notes

  1. NHTSA is an agency of the United States federal government, which is part of the Department of Transportation. Its task is designated as “Save Lives, Prevent Injury, Reduce Vehicle Accidents,” related to transportation safety in the United States.

Abbreviations

AF ::

Adaptive Filtering

BSS ::

Blind Source Separation

CCA ::

Canonical Correlation Analysis

DOST ::

Discrete Orthonormal Stockwell Transform

DWT ::

Discrete Wavelet Transform

ECG ::

Electrocardiogram

EMD ::

Empirical Mode Decomposition

EEMD ::

Ensemble Empirical Mode Decomposition

EEG ::

Electroencephalography

EMG ::

Electromyogram

EOG ::

Electrooculography

GPS ::

Global Positioning System

GSM ::

Global System for Mobile

ICA ::

Independent Component Analysis

ICs ::

Independent Components

ICA-R ::

Independent Component Analysis with Reference

IMF ::

Multiple Intrinsic Mode Functions

IoT ::

Internet of Things

LWT ::

Lifting Wavelet Transform

MSE ::

Multiscale Sample Entropy

OA ::

Ocular Artifact

PCA ::

Principal Component Analysis

REOG ::

Radial EOG

RLS::

Recursive Least Squares

SNR::

Signal-to-Noise Ratio

SOS ::

Second Order Statistics

ST ::

Statistical Thresholding

SWT ::

Stationary Wavelet Transform

UT ::

Universal Thresholding

WT ::

Wavelet Transform

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Acknowledgment

This work was carried out in the framework of the research activities of the LIMED (laboratory of Medical Computing) laboratory, which is affiliated to the faculty of exact sciences of the university of Bejaia and the IRISA laboratory, university of South Brittany, France. It was done in collaboration with the Labex MS2T, which was funded by the French Government, through the program “Investments for the future” managed by the National Agency for Research (Reference ANR-11-IDEX-0004-02). Finally, this work has been sponsored by the General Directorate for Scientific Research and Technological Development, Ministry of Higher Education and Scientific Research (DGRSDT), Algeria.

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  1. Laboratoire d’Informatique Médicale, Faculté des Sciences Exactes, Université de Bejaia, 06000, Bejaia, Algérie

    Mohamed Mohammedi

  2. IRISA, Université Bretagne Sud, Vannes, France

    Mawloud Omar

  3. Sorbonne Universités, Université de Technologie de Compiegne, CNRS, Heudiasyc UMR 7253, CS 60 319, 60 203 Compiegne cedex, Compiegne, France

    Abdelmadjid Bouabdallah

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Mohammedi, M., Omar, M. & Bouabdallah, A. Methods for detecting and removing ocular artifacts from EEG signals in drowsy driving warning systems: A survey. Multimed Tools Appl 82, 17687–17714 (2023). https://doi.org/10.1007/s11042-022-13822-y

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