Investigating Window Segmentation on Mental Fatigue Detection Using Single-Channel EEG

Muhammad Afif Hendrawan, Evi Septiana Pane, Adhi Dharma Wibawa, Mauridhi Hery Purnormo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Mental fatigue condition can be a serious problem if it is not handled properly. It also has a correlation with acute or chronic illness. Many research has been done to detect mental fatigue condition using several methods. The Physiological method is proved as a robust indicator, one of which is electroencephalogram (EEG). EEG is the most widely used as a physiological indicator in the few decades. However, most of the research in mental fatigue detection based on EEG used long time segment and complex computation method. In this paper, a window segmentation was employed to investigate mental fatigue information that might contain in a specific segment. Power percentage feature was extracted from each segment. The detection of mental fatigue employs three classifiers, LDA, QDA, and SVM. According to our experiment, LDA yields the highest performance with 92.82 % of accuracy. This result obtained from 30s length window segment which contains only the first and the last segment of the EEG signal data points. This result showed that information of mental fatigue in EEG signal may be better detected in short time segment and can be found in specific window segment.

Original languageEnglish
Title of host publicationProceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages173-178
Number of pages6
ISBN (Electronic)9781538634554
DOIs
Publication statusPublished - 15 Nov 2018
Event5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017 - Bandung, Indonesia
Duration: 6 Nov 20177 Nov 2017

Publication series

NameProceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017

Conference

Conference5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017
Country/TerritoryIndonesia
CityBandung
Period6/11/177/11/17

Keywords

  • EEG signal
  • brain computer interface
  • mental fatigue

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