Alleviating Seasickness through Brain-Computer Interface-based Attention Shift
Xiaoyu Bao, Kailin Xu, Jiawei Zhu, Haiyun Huang, Kangning Li, Qiyun Huang, Yuanqing Li

TL;DR
This study develops a brain-computer interface that uses attention shift techniques to effectively reduce seasickness symptoms in maritime environments, validated through real-world experiments with positive participant feedback.
Contribution
It introduces a novel BCI-based method incorporating attention shift tasks like breath counting to alleviate seasickness, validated in real-world maritime settings.
Findings
81.39% participants found BCI intervention effective
EEG signatures of motion sickness were effectively regulated
Theta/beta ratio decreased during attention shift tasks
Abstract
Seasickness poses a widespread problem that adversely impacts both passenger comfort and the operational efficiency of maritime crews. Although attention shift has been proposed as a potential method to alleviate symptoms of motion sickness, its efficacy remains to be rigorously validated, especially in maritime environments. In this study, we develop an AI-driven brain-computer interface (BCI) to realize sustained and practical attention shift by incorporating tasks such as breath counting. Forty-three participants completed a real-world nautical experiment consisting of a real-feedback session, a resting session, and a pseudo-feedback session. Notably, 81.39\% of the participants reported that the BCI intervention was effective. EEG analysis revealed that the proposed system can effectively regulate motion sickness EEG signatures, such as an decrease in total band power, along with an…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEEG and Brain-Computer Interfaces
MethodsSoftmax · Attention Is All You Need · Focus
