Comparison of Machine Learning Algorithms for Recognizing Drowsiness in Drivers using Electroencephalogram (EEG) Signals

Autor:	Ruya Akinci, Erhan Akdogan, Mehmet Emin Aktan
Rok vydání:	2022
Předmět:	Electroencephalogram ComputingMethodologies_PATTERNRECOGNITION Alpha band machine learning Control and Systems Engineering Artificial Intelligence drowsiness biological signal processing Computer Graphics and Computer-Aided Design Information Systems
Zdroj:	International Journal of Intelligent Systems and Applications in Engineering International Journal of Intelligent Systems and Applications in Engineering; Vol. 10 No. 1 (2022); 44-51
ISSN:	2147-6799
Popis:	Drowsiness is one of the major reasons that causes traffic accidents. Thus, its early detection can help preventing accidents by warning the drivers before the unfortunate events. This study focuses on the detection of drowsiness using classification of alpha waves from EEG signals with 25 different machine learning algorithms. The results were evaluated in terms of classification accuracy and classification time. Accordingly, the Bagged Trees and Subspace KNN models gave better results in terms of classification accuracy compared to the Tree algorithm methodology, although the classification times are relatively high. Tree Algorithms approach displays optimal features as it serves as both a considerably satisfactory classification accuracy in much shorter times. The requirements in terms of accuracy and time for the recognition of drowsiness should determine the method to be applied.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0c39196f46fd2d14189f74040196220 http://hdl.handle.net/11772/13955 Zobrazit plný text záznamu