Driver Drowsiness Detection Using EEG Features

Hwang, Se-Hyeon; Park, Myoungouk; Kim, Jong-Hwa; Yun, Yong-Won; Son, Joonwoo

doi:10.1007/978-3-319-92285-0_49

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…The attenuation of the alpha rhythm serves as a reliable EEG marker of sleep onset [20][29][30] [31] , it is considered the most valuable marker of sleep on-set during sleep [21] . Furthermore, objective assessment of sleepiness in permanently awake individuals could be facilitated by probing for alpha attenuation immediately after closing the eyes [32] . In this way, EEG tests can be performed in closed eyes for one minute.…”

Section: Phenomenon Of Attenuation-disappearance Of Alpha Wavesmentioning

confidence: 99%

Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems

Almiron,

Castillo,

Montoya Angulo

et al. 2024

Rev. Mex. Ing. Biomed.

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Currently, the percentage of traffic accidents has increased, and according to statistics, this percentage will continue to increase every year, so it is necessary to develop new technologies to prevent this kind of accidents. This paper presents a drowsiness detection system based on electroencephalogram (EEG) signals using a pair of channels (Fp1 and Fp2) applied to drivers before entering their vehicles. First, this model detects the relationship between the area under the curve (AUC) of alpha brain waves, an effective parameter for detecting drowsiness. Then, the extracted information is passed to a fuzzy expert system (FES) that classifies the subject's state as "alert" or "sleepy"; the criterion used was a threshold and training with subjective levels. The proposed system was compared with neural network models, such as support vector machine (SVM), K nearest neighbors (KNN), and random forest (RF). Measurements of one hundred and twenty minutes were performed on each of the ten drivers for two days to test the system. The tests confirm that this system is suitable for preventive measures and that the fuzzy system is superior to traditional neural network methods.

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Section: Phenomenon Of Attenuation-disappearance Of Alpha Wavesmentioning

confidence: 99%

Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems

Almiron,

Castillo,

Montoya Angulo

et al. 2024

Rev. Mex. Ing. Biomed.

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show abstract

“…-Mu wave (8-12 Hz), related to motor activity and can be used to recognize the purpose of one's movements. -Beta waves (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), related to the level of alertness and concentration of a person.…”

Section: Electroencephalogram (Eeg)mentioning

confidence: 99%

EEG signal classification for drowsiness detection using wavelet transform and support vector machine

Pasaribu

Halim

Ratnadewi

et al. 2021

IJ-AI

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<span id="docs-internal-guid-ed628156-7fff-8934-2369-94f011b043ca"><span>There are several categories to detect and measure driver drowsiness such as physiological methods, subjective methods and behavioral methods. The most objective method for drowsiness detection is the physiological method. One of the physiological methods used is an electroencephalogram (EEG). In this research wavelet transform is used as a feature extraction and using support vector machine (SVM) as a classifier. We proposed an experiment of retrieval data which is designed by using modified-EAR and EEG signal. From the SVM training process, with the 5-fold cross validation, Quadratic kernel has the highest accuracy 84.5% then others. In testing Driving-2 process 7 respondents were detected as drowsiness class, and 3 respondents were detected as awake class. In the testing of Driving-3 process, 6 respondents were detected as drowsiness class, and 4 respondents were detected as awake class. </span></span>

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“…Brain waves have been studied for measurement of concentration. Measurement of concentration using brain waves is applied to a wide range of fields including fatigue of driver and effects of yoga [10]- [12]. Especially for education, it is used for evaluation of active learning which has attracted attention in recent years.…”

Section: Measurement Of Concentrationmentioning

confidence: 99%

Investigation of Dynamic Transition of Learning Contents Based on Brain Waves

Tanabe

Touyama

Nakayama

et al. 2019

SICE Journal of Control, Measurement, and System Integration

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With the spread of the Internet, e-learning which is a way of learning using information technology has become popular. Although lecture videos in e-learning are designed so that many learners can understand, they do not take into consideration individual differences of learners. To address this problem, we designed a system that can grasp the concentration state of learners using electroencephalogram and dynamically provide contents according to the state. Moreover, we defined a metric using alpha waves in brain waves acquired from the occipital region as an index of changing learning materials used in the system. It enables providing suitable contents to each learner. In this research, we investigated the usefulness of the index and compared it with other brain waves such as theta waves and beta waves. As a result, it was confirmed that the proposed index was useful to extract learners who are relatively concentrating and not relatively concentrating. Moreover, it was suggested that alpha waves were more useful as the index for concentrating state than other waves.

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Driver Drowsiness Detection Using EEG Features

Cited by 6 publications

References 12 publications

Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems

Preventive Detection of Driver Drowsiness from EEG Signals using Fuzzy Expert Systems

EEG signal classification for drowsiness detection using wavelet transform and support vector machine

Investigation of Dynamic Transition of Learning Contents Based on Brain Waves

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