Driving fatigue detection based on brain source activity and ARMA model

Nadalizadeh, Fahimeh; Rajabioun, Mehdi; Feyzi, Amirreza

doi:10.1007/s11517-023-02983-z

Cited by 2 publications

(2 citation statements)

References 63 publications

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“…Increased beta activity is linked to heightened alertness and attention [ 30 ], while studies suggest a decline in beta wave activity when drivers feel drowsy [ 31 , 32 , 33 ]. Belyavin and Wright [ 14 ] assert that a sharp drop in beta wave activity is a key indicator of reduced alertness, consistent with the findings of this study. Additionally, Han et al reported a significant decrease in beta and gamma rhythm during states of fatigue [ 34 ].…”

Section: Discussionsupporting

confidence: 91%

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Scented Solutions: Examining the Efficacy of Scent Interventions in Mitigating Driving Fatigue

Jiang,

Muthusamy,

Chen

et al. 2024

Sensors

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Fatigued driving threatens the safety of people’s lives and property. Scent countermeasures offer minimal disruption and high efficacy, making them a promising approach. The aim of this study was to explore the application of scent countermeasures in alleviating fatigued driving. This study explored changes in EEG frequency bands (alpha, beta, theta, and gamma) and the activity of EEG metrics (R(α/β), Rθ/(α+β) and R(α+θ)/(α+β)) in the temporal lobe during driving tasks, selected fatigued driving identifiers, and aided validation by investigating subjective fatigue with the Karolinska Sleepiness Scale (KSS). The EEG indicators all increased, with a significant increase in R(α/β). R(α/β) was combined with the KSS to explore the effects of three scents, peppermint, grapefruit, and lavender, on driving fatigue. The subjective questionnaire results indicated that all three scents significantly improved driving fatigue, with significantly lower levels of driving fatigue compared to the control group. The analysis of EEG signals revealed a significant decrease in R(α/β) after the implementation of scent countermeasures. Moreover, R(α/β) was found to be lower in all three odor intervention groups compared to the control group. All three scents were found to significantly alleviate driving fatigue. The grapefruit scent had a better timely effect in relieving driving fatigue and the lavender scent had a longer effectiveness. This study provides further exploration for the application of odor interventions to alleviate driving fatigue. This study provides a practical reference for drivers to use odors to avoid fatigue in order to improve road safety.

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Section: Discussionsupporting

confidence: 91%

“…The EEG is the golden indicator for accurately identifying fatigue driving [ 13 , 14 ] and serves as the basis for this study. In investigating the fatigue-alleviating effects of scent on driving, the meticulous selection of indicators characterizing fatigued driving becomes paramount.…”

Section: Study On Fatigued Driving Recognitionmentioning

confidence: 99%

Scented Solutions: Examining the Efficacy of Scent Interventions in Mitigating Driving Fatigue

Jiang,

Muthusamy,

Chen

et al. 2024

Sensors

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Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies

Vakitbilir,

Islam,

Gomez

et al. 2024

Sensors

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Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data streams, including intracranial pressure (ICP) and cerebral perfusion pressure (CPP), providing real-time insights into cerebral function. Analyzing these signals is crucial for understanding complex brain processes, identifying subtle patterns, and detecting anomalies. Computational models play an essential role in linking sensor-derived signals to the underlying physiological state of the brain. Multivariate machine learning models have proven particularly effective in this domain, capturing intricate relationships among multiple variables simultaneously and enabling the accurate modeling of cerebral physiologic signals. These models facilitate the development of advanced diagnostic and prognostic tools, promote patient-specific interventions, and improve therapeutic outcomes. Additionally, machine learning models offer great flexibility, allowing different models to be combined synergistically to address complex challenges in sensor-based data analysis. Ensemble learning techniques, which aggregate predictions from diverse models, further enhance predictive accuracy and robustness. This review explores the use of multivariate machine learning models in cerebral physiology as a whole, with an emphasis on sensor-derived signals related to hemodynamics, cerebral oxygenation, metabolism, and other modalities such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) where applicable. It will detail the operational principles, mathematical foundations, and clinical implications of these models, providing a deeper understanding of their significance in monitoring cerebral function.

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Driving fatigue detection based on brain source activity and ARMA model

Cited by 2 publications

References 63 publications

Scented Solutions: Examining the Efficacy of Scent Interventions in Mitigating Driving Fatigue

Scented Solutions: Examining the Efficacy of Scent Interventions in Mitigating Driving Fatigue

Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies

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