Alçin, Ömer Faruk2021-06-082021-06-082020Alçin, O. F. (December 01, 2020). Approach based on wavelet packet transform and 1D‐RMLBP for drowsiness detection using EEG. Electronics Letters, 56, 25, 1378-1381.https://doi.org/10.1049/el.2020.2668https://hdl.handle.net/20.500.12899/196Early drowsiness detection may be crucial for the vehicle alertness system. Towards this, wearable technology, camera-based biophysical signals like electroencephalogram (EEG) approaches are utilised. In this Letter, the EEG-based approach is proposed to detect drowsiness. The proposed method consists of random sampling-based artificial signal augmentation, wavelet packet transform decomposition, logarithmic energy entropy, and one-dimensional region mean local binary pattern (1d-RMLBP) based feature extraction and classifier. k-Nearest neighbour and support vector machine classifiers are employed to detect the drowsiness. The MIT/BIH polysomnographic dataset has been used to test the proposed model. The proposed method has superior performance than the other methods using the same data set. The experimental results demonstrate that the proposed model could efficiently detect drowsiness from polysomnographic EEG signals.eninfo:eu-repo/semantics/openAccessWavelet transformsElectroencephalographyFeature extractionSupport vector machinesEntropyMedical signal processingSignal classificationMedical signal detectionNearest neighbour methodsDrowsiness detectionVehicle alertness systemWearable technologyCamera-based biophysical signalsRandom sampling-based artificial signal augmentationLogarithmic energy entropySupport vector machine classifiersPolysomnographic EEG signals1D-RMLBPWavelet packet transform decompositionOne-dimensional region mean local binary pattern based feature extractionMIT-BIH polysomnographic datasetk-nearest neighbour classifierExtreme learning machineWPDArticle10.1049/el.2020.2668562513781381WOS:000604957700009Q4