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P‐34: Compare and Model Multi‐level Stereoscopic 3D Visual Fatigue Based on EEG
Author(s) -
Yue Kang,
Wang Danli,
Hu Haichen,
Yang Xinpan,
Chiu Steve C.
Publication year - 2017
Publication title -
sid symposium digest of technical papers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.351
H-Index - 44
eISSN - 2168-0159
pISSN - 0097-966X
DOI - 10.1002/sdtp.11899
Subject(s) - electroencephalography , audiology , logistic regression , task (project management) , psychology , computer science , physical medicine and rehabilitation , simulation , medicine , engineering , machine learning , neuroscience , systems engineering
In this paper, we designed an experiment to observe changes for different fatigue level in human brain when watching random dot stereogram (RDS) using the method of EEG. The subjects were asked to watch 3D content for 30 minutes and the disparity of RDS changed every 10 seconds. During this watching task, subjects should rate their extent of fatigue during the experiment on the scale of one to five only when they felt worse or better. 30 channels electroencephalogram (EEG) signals were recorded through the experiment. Finally, the relationship between various fatigue levels and disparities which revealed by EEG signals were investigated. Our results show that the power of waveband, namely alpha increased with the fatigue level increasing from one to four during watching RDS, but decreased in level five. We have employed logistic regression to model fatigue level and achieved classification accuracy of 94.18%. Our research has important implications for the continuous monitoring of user's fatigue in the future.