Subject matching for cross-subject EEG-based recognition of driver states related to situation awareness

• Published in: Methods (San Diego, Calif.) Vol. 202; pp. 136 - 143
• Main Authors: Li, Ruilin, Wang, Lipo, Sourina, Olga
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2022
• Subjects: Classification; data collection; electroencephalography; Electroencephalography (EEG); Machine learning; Situation awareness; statistics; Taiwan; Transfer learning; Taiwan; Situation awareness; Electroencephalography (EEG); Transfer learning; Machine learning; Classification
• ISSN: 1046-2023; 1095-9130; 1095-9130
• DOI: 10.1016/j.ymeth.2021.04.009

Situation awareness (SA) has received much attention in recent years because of its importance for operators of dynamic systems. Electroencephalography (EEG) can be used to measure mental states of operators related to SA. However, cross-subject EEG-based SA recognition is a critical challenge, as data distributions of different subjects vary significantly. Subject variability is considered as a domain shift problem. Several attempts have been made to find domain-invariant features among subjects, where subject-specific information is neglected. In this work, we propose a simple but efficient subject matching framework by finding a connection between a target (test) subject and source (training) subjects. Specifically, the framework includes two stages: (1) we train the model with multi-source domain alignment layers to collect source domain statistics. (2) During testing, a distance is computed to perform subject matching in the latent representation space. We use a reciprocal exponential function as a similarity measure to dynamically select similar source subjects. Experiment results show that our framework achieves a state-of-the-art accuracy 74.32% for the Taiwan driving dataset.