eprintid: 16605
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/66/05
datestamp: 2023-12-19 03:23:07
lastmod: 2023-12-19 03:23:07
status_changed: 2023-12-19 03:06:33
type: article
metadata_visibility: show
creators_name: Tarafder, S.
creators_name: Badruddin, N.
creators_name: Yahya, N.
creators_name: Nasution, A.H.
title: Drowsiness Detection Using Ocular Indices from EEG Signal
ispublished: pub
keywords: Biomedical signal processing; Classification (of information); Decision trees; Electrophysiology; Learning algorithms; Learning systems; Nearest neighbor search; Roads and streets; Signal detection; Support vector machines, Driver drowsiness; Drowsiness detection; Ensemble learning; Extracting features; Eye-blink artifacts; Machine learning classification; Machine-learning; Ocular artifacts; Public dataset; Road users, Electroencephalography, algorithm; electroencephalography; human; machine learning; procedures; signal processing; support vector machine, Algorithms; Electroencephalography; Humans; Machine Learning; Signal Processing, Computer-Assisted; Support Vector Machine
note: cited By 1
abstract: Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroen-cephalography (EEG) signals to detect driver drowsiness. However, in most of the work performed in this area, the eyeblink or ocular artifacts present in EEG signals are considered noise and are removed during the preprocessing stage. In this study, we examined the possibility of extracting features from the EEG ocular artifacts themselves to perform classification between alert and drowsy states. In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. We were able to show that features from EEG ocular artifacts are able to classify drowsy and alert states, with the optimized ensemble-boosted trees yielding the highest accuracy of 91.10 among all classic machine learning models. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132778213&doi=10.3390%2fs22134764&partnerID=40&md5=8bcfd59e27120fea3b395c0b535868db
id_number: 10.3390/s22134764
full_text_status: none
publication: Sensors
volume: 22
number: 13
refereed: TRUE
issn: 14248220
citation:   Tarafder, S. and Badruddin, N. and Yahya, N. and Nasution, A.H.  (2022) Drowsiness Detection Using Ocular Indices from EEG Signal.  Sensors, 22 (13).   ISSN 14248220