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EEG Sparse Representation Based Alertness States Identification Using Gini Index

Published on Dec 13, 2018 in ICONIP (International Conference on Neural Information Processing)
· DOI :10.1007/978-3-030-04239-4_43
Muna Tageldin (Sultan Qaboos University), Talal Al-Mashaikki (Khalifa University)+ 1 AuthorsMostefa Mesbah2
Estimated H-index: 2
(Sultan Qaboos University)
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Abstract
Poor alertness experienced by individuals may lead to serious accidents that impact on people’s health and safety. To prevent such accidents, an efficient automatic alertness states identification is required. Sparse representation-based classification has recently gained a lot of popularity. A classifier from this class typically comprises three stages: dictionary learning, sparse coding and class assignment. Gini index, a recently proposed method, was shown to possess a number of properties that make it a better sparsity measure than the widely used \( l_{0} \)- and \( l_{1} \)-norms. This paper investigates whether these properties also lead to a better classifier. The proposed classifier, unlike the existing sparsity-based ones, embeds the Gini index in all stages of the classification process. To assess its performance, the new classifier was used to automatically identify three alertness levels, namely awake, drowsy, and sleep using EEG signal. The obtained results show that the new classifier outperforms those based on \( l_{0} \)- and \( l_{1} \)-norms.
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References38
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Published on Oct 1, 2018in Neural Computing and Applications4.66
Ahmed Al-Ani18
Estimated H-index: 18
(UTS: University of Technology, Sydney),
Mostefa Mesbah2
Estimated H-index: 2
(Sultan Qaboos University)
The aim of the paper is to automatically select the optimal EEG rhythm/channel combinations capable of classifying human alertness states. Four alertness states were considered, namely ‘engaged’, ‘calm’, ‘drowsy’ and ‘asleep’. The features used in the automatic selection are the energies associated with the conventional rhythms, \(\delta , \theta , \alpha , \beta\) and \(\gamma\), extracted from overlapping windows of the different EEG channels. The selection process consists of two stages. In t...
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Muhammad Awais3
Estimated H-index: 3
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Nasreen Badruddin12
Estimated H-index: 12
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Micheal Drieberg8
Estimated H-index: 8
Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-bas...
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Thiago Lopes Trugillo da Silveira3
Estimated H-index: 3
(UFSM: Universidade Federal de Santa Maria),
Alice de Jesus Kozakevicius6
Estimated H-index: 6
(UFSM: Universidade Federal de Santa Maria),
Cesar Ramos Rodrigues7
Estimated H-index: 7
(UFSM: Universidade Federal de Santa Maria)
Ratio indices computed from a single EEG channel used as drowsiness indicators.Delta and gamma brain rhythms successfully used for drowsiness detection.Wavelet packet transform as the main tool to localize specific brain frequency ranges.Transition from alert to drowsy state is taken as main event of interest.Wilcoxon signed rank test analysis pointed out the contribution of proposed indices. Advances in materials engineering, electronic circuits, sensors, signal processing and classification te...
Published on Jul 8, 2016in Frontiers in Aging Neuroscience3.63
Dong Wen4
Estimated H-index: 4
(Yanshan University),
Peilei Jia1
Estimated H-index: 1
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+ 2 AuthorsChengbiao Lu1
Estimated H-index: 1
(Xinxiang Medical University)
At present, the sparse representation-based classification (SRC) methods of electroencephalograph (EEG) signal analysis have become an important approach for studying brain science. SRC methods mean that the target data is sparsely represented on the basis of a fixed dictionary or learned dictionary, and classified based on the reconstruction criteria or the corresponding features extracted. SRC methods have been used to analyze the EEG signals of epilepsy, mild cognitive impairment (MCI) and Al...
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Jun Juh Yan10
Estimated H-index: 10
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Hang Hong Kuo3
Estimated H-index: 3
+ 1 AuthorsTeh-Lu Liao23
Estimated H-index: 23
(NCKU: National Cheng Kung University)
This study develops a real-time drowsiness detection system based on grayscale image processing and PERCLOS to determine if the driver is fatigued. The proposed system comprises three parts: first, it calculates the approximate position of the driver's face in grayscale images, and then uses a small template to analyze the eye positions, second, it uses the data from the previous step and PERCLOS to establish a fatigue model, and finally, based on the driver's personal fatigue model, the system ...
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Zutao Zhang8
Estimated H-index: 8
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Dianyuan Luo2
Estimated H-index: 2
+ 4 AuthorsChunbai Wang6
Estimated H-index: 6
In this paper, we present a vehicle active safety model for vehicle speed control based on driver vigilance detection using low-cost, comfortable, wearable electroencephalographic (EEG) sensors and sparse representation. The proposed system consists of three main steps, namely wireless wearable EEG collection, driver vigilance detection, and vehicle speed control strategy. First of all, a homemade low-cost comfortable wearable brain-computer interface (BCI) system with eight channels is designed...
Published on Aug 1, 2015 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
Syed Anas Imtiaz5
Estimated H-index: 5
(Imperial College London),
Esther Rodriguez-Villegas22
Estimated H-index: 22
(Imperial College London)
PhysioNet Sleep EDF database has been the most popular source of data used for developing and testing many automatic sleep staging algorithms. However, the recordings from this database has been used in an inconsistent fashion. For example, arbitrary selection of start and end times from long term recordings, data-hypnogram mismatches, different performance metrics and hypnogram conversion from R&K to AASM. All these differences result in different data sections and performance metrics being use...
Published on Aug 1, 2015 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
Hamza Baali3
Estimated H-index: 3
(IIUM: International Islamic University Malaysia),
Mostefa Mesbah2
Estimated H-index: 2
(Sultan Qaboos University)
Mehrdad J. Gangeh9
Estimated H-index: 9
(U of T: University of Toronto),
Ahmed K. Farahat9
Estimated H-index: 9
(UW: University of Waterloo)
+ 1 AuthorsMohamed S. Kamel43
Estimated H-index: 43
(UW: University of Waterloo)
Dictionary learning and sparse representation (DLSR) is a recent and successful mathematical model for data representation that achieves state-ofthe-art performance in various elds such as pattern recognition, machine learning, computer vision, and medical imaging. The original formulation for DLSR is based on the minimization of the reconstruction error between the original signal and its sparse representation in the space of the learned dictionary. Although this formulation is optimal for solv...
Published on Jan 1, 2015in IEEE Access4.10
Zheng Zhang10
Estimated H-index: 10
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Yong Xu39
Estimated H-index: 39
(HIT: Harbin Institute of Technology)
+ 2 AuthorsDavidZhang89
Estimated H-index: 89
(PolyU: Hong Kong Polytechnic University)
Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision, and pattern recognition. Sparse representation also has a good reputation in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. The main purpose of this paper is to provide a comprehensive study and an updated review on sparse representation and to supply guidance for researchers. The taxo...
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