Determination of optimal electroencephalography recording locations for detecting drowsy driving

Zhang, Chaofei; Wang, Wenjun; Chen, Chaoyang; Zeng, Chao; Anderson, Dennis E.; Cheng, Bo

doi:10.1049/iet-its.2017.0083

Cited by 8 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notably, the theta–alpha ratio highlighted superior performance among all features analyzed, displaying an increase during drowsiness and a decrease during wakefulness episodes (878 out of 927 events). These findings, consistent with prior studies [ 36 , 66 , 67 ], underscore a strong alignment between these EEG features and visual-based scoring across all drivers. Additionally, introducing new parameters enriched our analysis: the delta–alpha ratio mirrored a similar trend to delta–theta, increasing during drowsiness and decreasing during wakefulness episodes (808 and 804 out of 927 events, respectively).…”

Section: Discussionsupporting

confidence: 90%

“…A couple of studies observed heightened theta [ 27 ] and delta [ 27 , 67 ] brain activities when drivers transition from wakefulness to drowsiness. Other research indicated that drowsiness is often linked to decreased EEG activity and marked by increased theta frequency band dominance [ 9 , 68 ] or a decline in alpha activity, especially evident when eyes are closed [ 69 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

Minhas,

Peker,

Hakkoz

et al. 2024

Sensors

View full text Add to dashboard Cite

Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta–alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta–alpha-ratio (87.2%) and delta–theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta–alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

Minhas,

Peker,

Hakkoz

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The motor and somatosensory cortexes are located in these areas. These cortexes are significantly more involved in the cognitive information processing and functional control than other regions of the brain (Zhang et al, 2018). The results of other studies have shown that in the drowsiness phase, the activity of neurons in both motor and somatosensory cortex decreases and leads to significant changes in EEG signals (Zhang et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…The delta and theta bands are regarded as slow wave activities, while the alpha and beta bands are considered the fast wave activities (Jap et al, 2009;Zhang et al, 2018). Results showed that there is a high correlation between the fractal dimension of fast wave activities and PA.…”

Section: Discussionmentioning

confidence: 99%

Effect of psychoacoustic annoyance on EEG signals of tractor drivers

2023

Archives of Acoustics

View full text Add to dashboard Cite

The purpose of this study was to evaluate the psychoacoustic annoyance (PA) that the tractor drivers are exposed to, and investigate its effects on their brain signals during their work activities. To this aim, the sound of a garden tractor was recorded. Each driver's electroencephalogram (EEG) was then recorded at five different engine speeds. The Higuchi method was used to calculate the fractal dimension of the brain signals. To evaluate the amount of acoustic annoyance that the tractor drivers were exposed to, a psychoacoustic annoyance (PA) model was used. The results showed that as the engine speed increased, the values of PA increased as well. The results also indicated that an increase in the Higuchi's fractal dimension (HFD) of alpha and beta bands was due to the increase of the engine speed. The regression results also revealed that there was a high correlation between the HFD of fast wave activities and PA, in that, the coefficients of determination were 0.92 and 0.91 for alpha and beta bands, respectively. Hence, a good correlation between the EEG signals and PA can be used to develop a mathematical model which quantifies the human brain response to the external stimuli.

show abstract

Driver Monitoring System Based on CNN Models: An Approach for Attention Level Detection

Recalde

Pérez

Echanobe

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Determination of optimal electroencephalography recording locations for detecting drowsy driving

Cited by 8 publications

References 30 publications

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea

Effect of psychoacoustic annoyance on EEG signals of tractor drivers

Driver Monitoring System Based on CNN Models: An Approach for Attention Level Detection

Contact Info

Product

Resources

About