Automatic drowsiness detection using electroencephalogram signal

Drowsiness refers to the state of being sleepy or the state of minimal concentration. It is characterised by a decrease in a person's memory capacity and brain information processing speed. These conditions cause hazards in the real-time working environment such as driving, monitoring power gen...

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Veröffentlicht in:Electronics letters Jg. 56; H. 25; S. 1383 - 1386
Hauptverfasser: Dutta, A, Kour, S, Taran, S
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 10.12.2020
Schlagworte:
automatic drowsiness detection system
band‐limited modes
clustering variational mode decomposition
CVMD optimum allocation sampling analyses nonhomogenous EEG signals
drowsiness detection performance
electroencephalogram signal
electroencephalogram‐based automatic drowsiness detection method
electroencephalography
hazards
least squares approximations
medical signal processing
minimal concentration
neural nets
neurophysiology
nonstationary behaviour
oscillatory mode characteristics
patient health
person
power generation
real‐time working environment
signal classification
Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications
support vector machines
electroencephalography
nonstationary behaviour
least squares approximations
support vector machines
electroencephalogram signal
drowsiness detection performance
patient health
real-time working environment
CVMD optimum allocation sampling analyses nonhomogenous EEG signals
signal classification
medical signal processing
hazards
person
minimal concentration
electroencephalogram-based automatic drowsiness detection method
power generation
clustering variational mode decomposition
neurophysiology
automatic drowsiness detection system
band-limited modes
neural nets
oscillatory mode characteristics
ISSN:0013-5194, 1350-911X, 1350-911X
Online-Zugang:Volltext
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Zusammenfassung:Drowsiness refers to the state of being sleepy or the state of minimal concentration. It is characterised by a decrease in a person's memory capacity and brain information processing speed. These conditions cause hazards in the real-time working environment such as driving, monitoring power generation and patient health etc. These hazards can be sidestepped by introducing the automatic drowsiness detection system. This Letter suggested the electroencephalogram (EEG)-based automatic drowsiness detection method. The clustering variational mode decomposition (CVMD) explores the non-stationary behaviour of EEG for drowsiness detection. In CVMD optimum allocation sampling analyses non-homogenous EEG signals and converts those into homogeneous EEG clusters. These clusters are then decomposed into band-limited modes. The oscillatory mode characteristics are extracted in terms of several features. These features are fed as input into the least-squares support vector machine classifier. The proposed method provides better drowsiness detection performance in comparison with the different methods using the same data set.
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2020.2697