文章基本信息

标题：CLASSIFICATION OF DRIVER FATIGUE STATE BASED ON EEG USING EMOTIV EPOC+
本地全文：下载
作者：Brilian T. Nugraha ; Riyanarto Sarno ; Dimas Anton Asfani 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：86
期号：3
出版社：Journal of Theoretical and Applied
摘要：Driver fatigue is a major issue since many people have been aware about safety degree of driving. In this regard, this paper proposes methods and application to determine the driving fatigue state for every 3 minutes. The collected EEG data come from 30 participants that were taken their EEG data using Emotiv EPOC+ with the duration of 33 or 60 minutes during driving simulation and their answers about the driving fatigue states for every 3 minutes. The participant and channel outliers were determined based on the correlation coefficient channels results with 3 highest correlation coefficient results ≤ 0,45 and the frequency of channels shown in the 3 highest correlation with counts ≥ 7. The data that have been determined their participant outliers will be grouped into class 1 (fit/alert) or class 2 (fatigue/sleepy). The preprocessing and classification will use the grouped data with the selected channels. The proposed method gives accuracy results using the KNN classification method with the maximum mean accuracy 96%, minimum accuracy 90%, and maximum accuracy 100%; and using the SVM classification method with a maximum mean accuracy 81%, minimum accuracy 60%, and maximum accuracy 90%.
关键词：Electroencephalogram (EEG); Find Significant Channels; K-Nearest Neighbors (KNN); Driver Fatigue Prediction; Find The Best Features