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$\beta$-wave-based exploration of sensitive EEG features and classification of situation awareness

Published online by Cambridge University Press:  09 May 2024

C. Feng Open the ORCID record for C. Feng [Opens in a new window] ,
S. Liu ,
X. Wanyan Open the ORCID record for X. Wanyan [Opens in a new window] ,
Y. Dang ,
Z. Wang  and
C. Qian
C. Feng
Affiliation:
School of Aeronautic Science and Engineering, Beihang University, Beijing, China Tianmushan Laboratory, Hangzhou, China
S. Liu
Affiliation:
School of Aeronautic Science and Engineering, Beihang University, Beijing, China
X. Wanyan*
Affiliation:
School of Aeronautic Science and Engineering, Beihang University, Beijing, China
Y. Dang
Affiliation:
School of Aeronautic Science and Engineering, Beihang University, Beijing, China
Z. Wang
Affiliation:
School of Aeronautic Science and Engineering, Beihang University, Beijing, China
C. Qian
Affiliation:
School of Aeronautic Science and Engineering, Beihang University, Beijing, China
*
Corresponding author: X. Wanyan; Email: wanyanxiaoru@buaa.edu.cn
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Abstract

The purpose of this study was to explore the electroencephalogram (EEG) features sensitive to situation awareness (SA) and then classify SA levels. Forty-eight participants were recruited to complete an SA standard test based on the multi-attribute task battery (MATB) II, and the corresponding EEG data and situation awareness global assessment technology (SAGAT) scores were recorded. The population with the top 25% of SAGAT scores was selected as the high-SA level (HSL) group, and the bottom 25% was the low-SA level (LSL) group. The results showed that (1) for the relative power of $\beta$1 (16–20Hz), $\beta$2 (20–24Hz) and $\beta$3 (24–30Hz), repeated measures analysis of variance (ANOVA) in three brain regions (Central Central-Parietal, and Parietal) × three brain lateralities (left, midline, and right) × two SA groups (HSL and LSL) showed a significant main effect for SA groups; post hoc comparisons revealed that compared with LSL, the above features of HSL were higher. (2) for most ratio features associated with $\beta$1 ∼ $\beta$3, ANOVA also revealed a main effect for SA groups. (3) EEG features sensitive to SA were selected to classify SA levels with small-sample data based on the general supervised machine learning classifiers. Five-fold cross-validation results showed that among the models with easy interpretability, logistic regression (LR) and decision tree (DT) presented the highest accuracy (both 92%), while among the models with hard interpretability, the accuracy of random forest (RF) was 88.8%, followed by an artificial neural network (ANN) of 84%. The above results suggested that (1) the relative power of $\beta$1 ∼ $\beta$3 and their associated ratios were sensitive to changes in SA levels; (2) the general supervised machine learning models all exhibited good accuracy (greater than 75%); and (3) furthermore, LR and DT are recommended by combining the interpretability and accuracy of the models.

Keywords

situation awarenessmeasurement and classificationEEG featuresinterpretabilityflight safety
Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 16
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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