Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146044
Title: EEG-based recognition of driver state related to situation awareness using graph convolutional networks
Authors: Li, Ruilin
Lan, Zirui
Cui, Jian
Sourina, Olga
Wang, Lipo
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Li, R., Lan, Z., Cui, J., Sourina, O., & Wang, L. (2020). EEG-based recognition of driver state related to situation awareness using graph convolutional networks. Proceedings of the International Conference on Cyberworlds. doi:10.1109/CW49994.2020.00037
Abstract: Extracting intra- and inter-subject parameters from Electroencephalogram (EEG) representing different Situation Awareness (SA) status is a critical challenge for objective SA recognition. Most of the existing work focuses on the subject-dependent classification that applies power spectrum density (PSD) features. In this paper, we propose a novel spectral-spatial (S-S) model for cross-subject fatigue-related SA recognition. The S-S model not only considers the biological topology across different brain regions to capture both local and global relations among different EEG channels, but also extracts spectral features for each EEG channel. Specifically, we firstly model the topological structure of EEG channels via an adjacency matrix which is built based on the Euclidean distance between EEG channels. Then, the graph convolution operation is employed to perform the neighbourhood aggregation for extracting spatial features. We test our model on a public dataset collected during driver's task performance. The subject-independent performance of the model is explored. Results demonstrate (1) the superior performance of our model compared with the state-of-the-art models on SA recognition from EEG signals. Specifically, our S-S model achieves 70.6% accuracy which is higher than traditional machine learning methods by 2.7%-6.8% and deep learning methods by 10.3%-11.6%; (2) EEG signal at the occipital region can better reflect the change of SA.
URI: https://hdl.handle.net/10356/146044
DOI: 10.1109/CW49994.2020.00037
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/CW49994.2020.00037
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:Fraunhofer Singapore Conference Papers

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