Drowsiness Detection from EEG Spectrum Analysis

Yin, Yabo; Zhu, Yun; Xiong, Shi; Zhang, Jiacai

doi:10.1007/978-3-642-25992-0_101

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…According to the authors, the "depth of drowsiness" method described in this study was the first implementation of a non-binary drowsiness detector using a low-cost EEG system. They reported an accuracy of 82%, over a prior reported accuracy of 70% (Yin et al, 2011). Mohamed et al (2018a,b) used the Mark IV headset to analyze EEG output during driver behavior, based on spectral features.…”

Section: Openbci Ultracortexmentioning

confidence: 99%

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

LaRocco

Paeng

2020

Front. Neuroinform.

114

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Drowsiness is a leading cause of traffic and industrial accidents, costing lives and productivity. Electroencephalography (EEG) signals can reflect awareness and attentiveness, and low-cost consumer EEG headsets are available on the market. The use of these devices as drowsiness detectors could increase the accessibility of safety and productivity-enhancing devices for small businesses and developing countries. We conducted a systemic review of currently available, low-cost, consumer EEG-based drowsiness detection systems. We sought to determine whether consumer EEG headsets could be reliably utilized as rudimentary drowsiness detection systems. We included documented cases describing successful drowsiness detection using consumer EEG-based devices, including the Neurosky MindWave, InteraXon Muse, Emotiv Epoc, Emotiv Insight, and OpenBCI. Of 46 relevant studies, ∼27 reported an accuracy score. The lowest of these was the Neurosky Mindwave, with a minimum of 31%. The second lowest accuracy reported was 79.4% with an OpenBCI study. In many cases, algorithmic optimization remains necessary. Different methods for accuracy calculation, system calibration, and different definitions of drowsiness made direct comparisons problematic. However, even basic features, such as the power spectra of EEG bands, were able to consistently detect drowsiness. Each specific device has its own capabilities, tradeoffs, and limitations. Widely used spectral features can achieve successful drowsiness detection, even with low-cost consumer devices; however, reliability issues must still be addressed in an occupational context.

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Section: Openbci Ultracortexmentioning

confidence: 99%

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

LaRocco

Paeng

2020

Front. Neuroinform.

114

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“…The ratio between the alpha (α) and beta (β) brain waves (i.e., α/(α + β)) was used the analysis indicator since these waves were highly correlated with the drowsy state. As the drowsiness increased, the activation degree of the α wave increased, whereas that of the β wave decreased [29,30]. Therefore, in these experiments, an α/(α + β) ratio higher than that obtained in the normal state was assumed to reflect a drowsy state.…”

Section: Eeg-based Drowsiness Verification Resultsmentioning

confidence: 76%

Advanced Alarm Method Based on Driver’s State in Autonomous Vehicles

Han

2021

Electronics

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In autonomous driving vehicles, the driver can engage in non-driving-related tasks and does not have to pay attention to the driving conditions or engage in manual driving. If an unexpected situation arises that the autonomous vehicle cannot manage, then the vehicle should notify and help the driver to prepare themselves for retaking manual control of the vehicle. Several effective notification methods based on multimodal warning systems have been reported. In this paper, we propose an advanced method that employs alarms for specific conditions by analyzing the differences in the driver’s responses, based on their specific situation, to trigger visual and auditory alarms in autonomous vehicles. Using a driving simulation, we carried out human-in-the-loop experiments that included a total of 38 drivers and 2 scenarios (namely drowsiness and distraction scenarios), each of which included a control-switching stage for implementing an alarm during autonomous driving. Reaction time, gaze indicator, and questionnaire data were collected, and electroencephalography measurements were performed to verify the drowsiness. Based on the experimental results, the drivers exhibited a high alertness to the auditory alarms in both the drowsy and distracted conditions, and the change in the gaze indicator was higher in the distraction condition. The results of this study show that there was a distinct difference between the driver’s response to the alarms signaled in the drowsy and distracted conditions. Accordingly, we propose an advanced notification method and future goals for further investigation on vehicle alarms.

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“…Recently, some studies [10,11] concentrated on detecting the information on drowsiness available from a full EEG spectrum.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Classification Methods of Sleep Stages by Using EEG

Kim

2014

Journal of Korea Multimedia Society

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Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in recognition of alertness level. In this paper, EEG signals have been analyzed using wavelet transform as well as discrete wavelet transform and classification using statistical classifiers such as euclidean and mahalanobis distance classifiers and a promising method SVM (Support Vector Machine). As a result of simulation, the average values of accuracies for the Linear Discriminant Analysis (LDA)-Quadratic, k-Nearest Neighbors (k-NN)-Euclidean, and Linear SVM were 48%, 34.2%, and 86%, respectively. The experimental results show that SVM classification method offer the better performance for reliable classification of the EEG signal in comparison with the other classification methods.

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Drowsiness Detection from EEG Spectrum Analysis

Cited by 5 publications

References 5 publications

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection

Advanced Alarm Method Based on Driver’s State in Autonomous Vehicles

A Comparative Study on Classification Methods of Sleep Stages by Using EEG

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