@article{ref1,
title="Automatic classification methods for detecting drowsiness using wavelet packet transform extracted time-domain features from single-channel EEG signal",
journal="Journal of neuroscience methods",
year="2020",
author="B, Venkata Phanikrishna and Chinara, Suchismitha",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="BACKGROUND: Detecting human drowsiness during some critical works like vehicle driving, crane operating, mining blasting, etc. is one of the safeguards to prevent accidents. Among several drowsiness detection (DD) methods, a combination of neuroscience and computer science knowledge has a better ability to differentiate awake and sleep states. Most of the current models are implemented using multi-sensors electroencephalogram (EEG) signals, multi-domain features, predefined features selection algorithms. Therefore, there is great interest in the method of detecting drowsiness on embedded platforms with improved accuracy using generalized best features. New-Method: Single-channel EEG based drowsiness detection (DD) model is proposed in this by utilizing wavelet packet transform (WPT) to extract the time-domain features from considered channel EEG. The dimension of the feature vector is reduced by the proposed novel feature selection method.  RESULTS: The proposed model on freely available real-time sleep analysis EEG and Simulated Virtual Driving Driver (SVDD) EEG achieves 94.45% and 85.3% accuracy, respectively. Comparison-with-Existing-Method: The results show that the proposed DD method produces better accuracy compared to the state-of-the-art using the physiological dataset with the proposed time-domain sub-band-based features and feature selection method. This task of detecting drowsiness by analyzing the 5-seconds EEG signal with four features is an improvement to my previous work on detecting drowsiness using a 30-seconds EEG signal with 66 features.  CONCLUSIONS: Time-domain features obtained from EEG time-domain sub-bands collected using WPT achieving excellent accuracy rate by selecting unique optimization features for all subjects by the proposed feature selection algorithm.<p /> <p>Language: en</p>",
language="en",
issn="0165-0270",
doi="10.1016/j.jneumeth.2020.108927",
url="http://dx.doi.org/10.1016/j.jneumeth.2020.108927"
}