以腦波動態變化預估暈車程度

Abstract

在日常交通運輸中，最常伴隨的不適症狀即為人們熟知的暈車現象，是一種當大腦接收到衝突的動作感知資訊時所引發的常見症狀。在許多已確認的暈車相關的生理指標中，我們於先前的研究觀察發現以腦電波動態變化預估暈車程度的可行性。相較於傳統的線性迴歸，我們採用輻射基底函數類神經網路(Radial basis function neural network)與支持向量迴歸(Support vector regression)以得到更佳的暈車程度估計。本研究並使用主成分分析和基因特徵選取以提升估計表現至優於過去所用的相關性分析之成效。研究結果顯出使用特徵選取和迴歸方法可有效優化暈車程度之估計，並可引領後續非侵入式暈車監測系統在實際生活之應用發展。

Motion sickness is a common symptom that occurs when the brain receives conflicting information about the sensation of movement. Many motion sickness biomarkers have been identified, and electroencephalogram (EEG)-based motion sickness level estimation was found feasible in our previous study. Radial basis function neural network (RBFNN) and support vector regression (SVR) were adopted in this work to approach better motion sickness estimation comparing to the traditional linear regression. This study employs principal component analysis (PCA) and genetic feature selection (GFS) to find usage of EEG features that can further improve estimation performance over the correlation-based method reported in the previous studies. Results of this study demonstrate that these feature selection techniques and regression methods are effective to optimize the estimation of motion-sickness level. This work could lead to a practical system for noninvasive monitoring of the motion sickness of individuals in real-world environments.