Subtractive fuzzy classifier based driver drowsiness levels classification using EEG

Murugappan, M.; Wali, Mousa K.; Ahmmad, R. Badlishah; Murugappan, S.

doi:10.1109/iccsp.2013.6577036

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…Murugappan et al decomposed the EEG signals into four bands (δ to β), using WPT, after which they used FFT to extract the band power and spectral centroid (SC) from the above frequency bands. In this work, four wavelet functions ("db4", "db8", "sym8", and "coif5") were used, of which "db4"was found to be the best one, in terms of the band power feature [73].…”

Section: Wavelet+mentioning

confidence: 99%

Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review

Chung

2022

Sensors

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Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early-stage symptoms of self-driving carsickness). In view of the importance of detecting drivers’ drowsiness, this paper reviews the algorithms of electroencephalogram (EEG)-based drivers’ drowsiness detection (DDD). To facilitate the review, the EEG-based DDD approaches are organized into a tree structure taxonomy, having two main categories, namely “detection only (open-loop)” and “management (closed-loop)”, both aimed at designing better DDD systems that ensure early detection, reliability and practical utility. To achieve this goal, we addressed seven questions, the answers of which helped in developing an EEG-based DDD system that is superior to the existing ones. A basic assumption in this review article is that although driver drowsiness and carsickness-induced drowsiness are caused by different factors, the brain network that regulates drowsiness is the same.

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Section: Wavelet+mentioning

confidence: 99%

Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review

Chung

2022

Sensors

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“…Meanwhile, Ref. [16] findings have demonstrated that by using fuzzy classifier, the driver's drowsiness level can be differentiated into four levels specifically awake, drowsy, high drowsy and sleep.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Mean Fuzzy Approach for Attention Detection

et al. 2021

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Statistics around the world showed that attention deficit significantly leads to road accidents. Hence, the growth of studies on attention deficit detection becoming more important. The studies obtained the waveform from electroencephalography (EEG) to identify the characteristic of attention. However, each individual has own unique characteristics to significantly shown the attention deficit. Thus, this research aim is to use the fuzzy approach to minimize the variability gap of the EEG signal between each individual. The research conducted the prior experiment to develop control parameter for training set of fuzzy by using two distinct stimulations to create two groups of attention sample i.e., attentive and inattentive. An approach of novel Hybrid Mean Fuzzy (HMF) was proposed in this research to detect attention deficit in EEG signal. It is the combination of simple averaging (Mean) and Fuzzy approaches for EEG analysis and classification. The results of using this method shows a significantly change in EEG signal which correlates to the attention detection. An Attention Degradation Scale (ADS) is successfully developed as the threshold value of EEG for attention detection. Therefore, the findings in this research can be a promising foundation on attention deficit detection in large application not only for reducing the road accidents.

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“…Haddad et al [ 15 ] concluded that most physiological signals are non-stationary signals. In the field of driver drowsiness detection, several studies have successfully extracted wavelet-based features from EEG signal [ 16 ], eyelid signals [ 17 ] and even steering wheel movements [ 18 ]. In a more recent work, a hybrid algorithm using EEG, electrooculogram (EOG), ECG and wavelet-packet-based feature is addressed for driver drowsiness detection [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier

Chung

2013

Sensors

187

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Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV) analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV signals of drivers were always regarded as stationary signals. The wavelet transform method is a method for analyzing non-stationary signals. The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT)-based features. Based on the standard shortest duration for FFT-based short-term HRV evaluation, the wavelet decomposition is performed on 2-min HRV samples, as well as 1-min and 3-min samples for reference purposes. A receiver operation curve (ROC) analysis and a support vector machine (SVM) classifier are used for feature selection and classification, respectively. The ROC analysis results show that the wavelet-based method performs better than the FFT-based method regardless of the duration of the HRV sample that is used. Finally, based on the real-time requirements for driver drowsiness detection, the SVM classifier is trained using eighty FFT and wavelet-based features that are extracted from 1-min HRV signals from four subjects. The averaged leave-one-out (LOO) classification performance using wavelet-based feature is 95% accuracy, 95% sensitivity, and 95% specificity. This is better than the FFT-based results that have 68.8% accuracy, 62.5% sensitivity, and 75% specificity. In addition, the proposed hardware platform is inexpensive and easy-to-use.

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Subtractive fuzzy classifier based driver drowsiness levels classification using EEG

Cited by 12 publications

References 9 publications

Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review

Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review

Hybrid Mean Fuzzy Approach for Attention Detection

Detection of Driver Drowsiness Using Wavelet Analysis of Heart Rate Variability and a Support Vector Machine Classifier

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