Ordinal Distance-based Domain Adaptation Framework for Motion Sickness Classification

• Published in: 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) pp. 2275 - 2280
• Main Authors: Han, So-Hyun, Han, Dong-Kyun, Lee, Seong-Whan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.10.2022
• Subjects: Brain-computer interface (BCI); Brain-computer interfaces; Calibration; Computer aided instruction; Domain adaptation; Electroencephalography; Feature extraction; Motion sickness; Ordinal classification; Resting-state; Transfer learning
• ISSN: 2577-1655
• DOI: 10.1109/SMC53654.2022.9945427

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.