A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness

Li, Gang; Chung, Wan-Young

doi:10.3390/s150820873

Cited by 78 publications

(50 citation statements)

References 36 publications

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“…Intrusive detection analyzes the psychological state of the driver through electroencephalographic (EEG) and electrooculographic (EOG) information features [3,4,5,6,7,8]. Generally, the fatigue detection systems based on EEG and EOG signals provide high accuracy; however, they rely on physiological information measured by sensors located on or around the driver, and the driver’s movement therefore affects the reliability of data collection.…”

Section: Introductionmentioning

confidence: 99%

Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

et al. 2017

Sensors

154

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This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn) features from fixed sliding windows on real-time steering wheel angles time series. After that, this system linearizes the ApEn features series through an adaptive piecewise linear fitting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classifier. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: “wake” and “drowsy”. The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the “awake” state, and 15.15% false detections of the “drowsy” state. The results also confirm that the proposed method based on SWA signal is valuable for applications in preventing traffic accidents caused by driver fatigue.

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Section: Introductionmentioning

confidence: 99%

Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

et al. 2017

Sensors

154

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“…For Mental Disorder, Guo et al [77] adopted fractional amplitude of low-frequency fluctuation to examine regional alterations of the default mode network in unaffected siblings of schizophrenia patients during resting. For Fatigue Driving, Li and Chung [78] proposed an innovative context-aware brain machine interface system for detecting driver drowsiness at early stage. For Near-Infrared Spectroscopy, Hernandez-Meza et al [79] examined the potential of functional near infrared spectroscopy to monitor anesthetic effects on prefrontal cortex.…”

Section: Plos Onementioning

confidence: 99%

Topics and trends in artificial intelligence assisted human brain research

Chen

Cheng

et al. 2020

PLoS ONE

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Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AIassisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AIassisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AIassisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.

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“…Similarly in [25], the authors utilized non-visual features such as physiological EEG and EOG signals. They used EOG signals from the forehead region of the face.…”

Section: Related Workmentioning

confidence: 99%

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

Abbas¹

2020

IJACSA

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Road accidents mainly caused by the state of driver drowsiness. Detection of driver drowsiness (DDD) or fatigue is an important and challenging task to save roadside accidents. To help reduce the mortality rate, the "HybridFatigue" DDD system was proposed. This HybridFatigue system is based on integrating visual features through PERCLOS measure and non-visual features by heartbeat (ECG) sensors. A hybrid system was implemented to combine both visual and non-visual features. Those hybrid features have been extracted and classified as driver fatigue by advanced deep-learning-based architectures in real-time. A multi-layer based transfer learning approach by using a convolutional neural network (CNN) and deep-belief network (DBN) was used to detect driver fatigue from hybrid features. To solve night-time driving and to get accurate results, the ECG sensors were utilized on steering by analyzing heartbeat signals in case if the camera is not enough to get facial features. Also to solve the accurate detection of center head-position of drivers, two-cameras were mounted instead of a single camera. As a result, a new HybridFatigue system was proposed to get high accuracy of driver's fatigue. To train and test this HybridFatigue system, three online datasets were used. Compare to state-of-the-art DDD system, the HybridFatigue system is outperformed. On average, the HybridFatigue system achieved 94.5% detection accuracy on 4250 images when tested on different subjects in the variable environment. The experimental results indicate that the HybridFatigue system can be utilized to decrease accidents.

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A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness

Cited by 78 publications

References 36 publications

Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

Topics and trends in artificial intelligence assisted human brain research

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

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