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Title: Subject matching for cross-subject EEG-based recognition of driver states related to situation awareness
Authors: Li, Ruilin
Wang, Lipo
Sourina, Olga
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Li, R., Wang, L. & Sourina, O. (2022). Subject matching for cross-subject EEG-based recognition of driver states related to situation awareness. Methods, 202, 136-143.
Journal: Methods
Abstract: 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.
ISSN: 1046-2023
DOI: 10.1016/j.ymeth.2021.04.009
Schools: School of Electrical and Electronic Engineering 
Research Centres: Fraunhofer Singapore 
Rights: © 2021 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles
Fraunhofer Singapore Journal Articles

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