On-line automatic detection of driver drowsiness using a single electroencephalographic channel

Picot, Antoine; Charbonnier, Sylvie; Caplier, Alice

doi:10.1109/iembs.2008.4650053

Cited by 50 publications

(30 citation statements)

References 7 publications

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“…It is thus important that the database contain all PSG signals of interest, along with the images of the face, and perfectly time-synchronized with them. The literature provides articles showing how to obtain an LoD from PSG signals [1,7,13].…”

Section: Motivation and Goalsmentioning

confidence: 99%

The ULg multimodality drowsiness database (called DROZY) and examples of use

Massoz

Langohr

François

et al. 2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Drowsiness is a major cause of accidents, in particular in road transportation. It is thus crucial to develop robust drowsiness monitoring systems. There is a widespread agreement that the best way to monitor drowsiness is by closely monitoring symptoms of drowsiness that are directly linked to the physiology of an operator such as a driver. The best systems are completely transparent to the operator until the moment he/she must react. In transportation, cameras placed in the passenger compartment and looking at least at the face of the driver are most likely the best way to sense physiology related symptoms such as facial expressions and the fine behavior of the eyeballs and eyelids. We present here the new database 1 called DROZY that provides multiple modalities of data to tackle the design of drowsiness monitoring systems and related experiments. We also present two novel systems developed using this database that can make predictions about the speed of reaction of an operator by using near-infrared intensity and range images of his/her face.

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Section: Motivation and Goalsmentioning

confidence: 99%

The ULg multimodality drowsiness database (called DROZY) and examples of use

Massoz

Langohr

François

et al. 2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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“…The MEG signals were mainly acquired from 4 electrodes placed over occipital lobe [7] from 3 subjects (1 male and 2 female) of the age group 20-30 years. Three trials of duration 30 minutes each were taken for each subject.…”

Section: A Data Acquisitionmentioning

confidence: 99%

“…The method of MCT (inspired by Antoine Picot, Sylvie Charbonnier and Alice Caplier) [7] is applied on the relative powers in the alpha band and beta band.…”

Section: A Mean Comparison Test (Mct)mentioning

confidence: 99%

Performance Evaluation Of EEG And MEG Based Sleep Onset Detection Using Mean Comparison Test

Sivaramakrishnan¹,

P.K²,

Vidhya³

et al. 2011

Int. Jour. Appl. Bio-Eng.

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The objective of this project is to detect the onset of drowsiness/sleep by an analysis of the characteristic features using EEG and MEG waveforms corresponding to the "Awake" and "Drowsy" states using MCT (Mean Comparison Test) approach. Drowsiness refers to an abnormally sleepy feeling, especially at a time when the subject is required to be awake and alert. Such momentary feelings of drowsiness may lead to catastrophic errors of judgment. The proposed system detects the condition of drowsiness by acquiring the physiological signals such as EEG and MEG; the time and frequency domain characteristics of those signals change depending upon the level of alertness. The acquired EEG and MEG signals are generally distorted due to the addition of noise from various sources as well as by parasitic biological artifacts. So, filtering plays a vital role in the preprocessing of the data. The useful features giving information on awake, drowsy and sleep states of the subject can be extracted by implementing wavelet decomposition and windowing technique. These extracted features are then supplied as inputs to the classifier, which classifies them into awake and drowsy state by following a set of rules and procedures assigned initially.

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“…1 illustrates EEG signals of alert and drowsy stages. Several approaches to the analysis of EEG signals were proposed in the last decade [6]- [9]. Basically these approaches can fall into one of the following schemes: 1) time analysis, 2) frequency analysis and 3) time-frequency analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Despite its efficient computation, these statistical features are not effective enough to capture underlying properties of EEG signals. More common approaches are based on frequency analysis [7]- [9]. , Fourier analysis is a dominant technique that extracts a frequency spectrum of EEG signals.…”

Section: Introductionmentioning

confidence: 99%