EEG-based evaluation system for motion sickness estimation

Wei, CS; Ko, LW; Chuang, SW; Jung, TP; Lin, CT

EEG-based evaluation system for motion sickness estimation

Wei, CS Ko, LW Chuang, SW Jung, TP Lin, CT

Permalink

Publication Type:: Conference Proceeding
Citation:: 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011, 2011, pp. 100 - 103
Issue Date:: 2011-07-20

Closed Access

	Filename	Description	Size
	05910498.pdf	Published version	399.62 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wei, CS	en_US
dc.contributor.author	Ko, LW	en_US
dc.contributor.author	Chuang, SW	en_US
dc.contributor.author	Jung, TP	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2011-07-20	en_US
dc.identifier.citation	2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011, 2011, pp. 100 - 103	en_US
dc.identifier.isbn	9781424441402	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120364
dc.description.abstract	Motion sickness is a common symptom which occurs when the brain receives conflicting sensory information. Although many motion sickness-related biomarkers have been identified, estimating humans' motion sickness level (MSL) remains a challenge in operational environments. Traditionally, questionnaire and physical check are the common ways to passively evaluate subject's sickness level. This study proposes a physiology-based estimation system that can automatically assess subject's motion-sickness level in operational environments. Our previous study showed that increases in self-reported MSL in a Virtual-reality based driving experiment on a motion platform were accompanied by elevated alpha (8-12Hz) power most prominently in the occipital midline electroencephalogram (EEG). This study explores the feasibility of an automatic MSL estimation based on spontaneous EEG spectrum. To this end, this study employed three different estimators: 1) Linear regression (LR), 2) Radial basis function neural network (RBFNN), and 3) Support vector regression (SVR). The results of this study showed that SVR outperformed LR and RBFNN in estimating MSL from EEG spectrum. The averaged accuracy of MSL estimation by SVR was 86.926.09% across 6 subjects. This demonstration could lead to a practical system for noninvasive monitoring of the motion sickness in real-world environments. © 2011 IEEE.	en_US
dc.relation.ispartof	2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011	en_US
dc.relation.isbasedon	10.1109/NER.2011.5910498	en_US
dc.title	EEG-based evaluation system for motion sickness estimation	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Motion sickness is a common symptom which occurs when the brain receives conflicting sensory information. Although many motion sickness-related biomarkers have been identified, estimating humans' motion sickness level (MSL) remains a challenge in operational environments. Traditionally, questionnaire and physical check are the common ways to passively evaluate subject's sickness level. This study proposes a physiology-based estimation system that can automatically assess subject's motion-sickness level in operational environments. Our previous study showed that increases in self-reported MSL in a Virtual-reality based driving experiment on a motion platform were accompanied by elevated alpha (8-12Hz) power most prominently in the occipital midline electroencephalogram (EEG). This study explores the feasibility of an automatic MSL estimation based on spontaneous EEG spectrum. To this end, this study employed three different estimators: 1) Linear regression (LR), 2) Radial basis function neural network (RBFNN), and 3) Support vector regression (SVR). The results of this study showed that SVR outperformed LR and RBFNN in estimating MSL from EEG spectrum. The averaged accuracy of MSL estimation by SVR was 86.926.09% across 6 subjects. This demonstration could lead to a practical system for noninvasive monitoring of the motion sickness in real-world environments. © 2011 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/120364