Ordinal Distance-based Domain Adaptation Framework for Motion Sickness Classification

So Hyun Han, Dong Kyun Han, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many people experience motion sickness. In order to analyze a driver's motion sickness state and prevent accidents, a method of estimating the degree of motion sickness based on bio-signals is emerging. The brain-computer interface (BCI) systems using electroencephalogram (EEG) are used as the most direct method of estimating motion sickness conditions. However, EEG-based systems suffer from variability between subjects and over time, so a calibration process is required for every use. To address this problem, we mitigate the need for calibration through cross-subject transfer learning between the target data and the multi-subjects source data. All experiments were conducted in a domain adaptation setting. Meanwhile, we assume that there is an ordinal relationship between motion sickness scores. Thus, we performed an ordinal classification task so that the feature vectors were mapped by reflecting the ordinal characteristics according to the motion sickness state. In this paper, we propose a motion sickness classification BCI framework in combination with ordinal classification, resting-state prototype-based ordinal distance learning, and a subject-specific embedding module. Taking into account constraints of ordinal rank, the feature extractor is trained with prototype-based ordinal distance learning to measure the relative distance between the resting-state and motion sickness state. We further utilize an embedding module that encodes subject-specific information combined with task discriminative features to be effective for domain adaptation tasks. The proposed framework achieved the highest performance (accuracy 60.21 %) through comparative experiments with other models.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2275-2280
Number of pages6
ISBN (Electronic)9781665452588
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Prague, Czech Republic
Duration: 2022 Oct 92022 Oct 12

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume2022-October
ISSN (Print)1062-922X

Conference

Conference2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022
Country/TerritoryCzech Republic
CityPrague
Period22/10/922/10/12

Bibliographical note

Funding Information:
This work was partly supported by Institute of Information communications Technology Planning Evaluation (IITP) grants funded by the Korea government (MSIT) (No. 2017-0-00451: Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning, No. 2019-0-00079: Artificial Intelligence Graduate School Program, Korea University, No. 2021-0-02068: Artificial Intelligence Innovation Hub, and No. 2021-0-00866: Development of BMI application technology based on multiple bio-signals for autonomous vehicle drivers).

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Brain-computer interface (BCI)
  • Domain adaptation
  • Motion sickness
  • Ordinal classification
  • Resting-state

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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