EEG Feature Analysis Related to Situation Awareness Assessment and Discrimination

Abstract

In order to discriminate situation awareness (SA) levels on the basis of SA-sensitive electroencephalography (EEG) features, the high-SA (HSA) group and low-SA (LSA) groups, which are representative of two SA levels, were classified according to the situation awareness global assessment technology (SAGAT) scores measured in the multi-attribute task battery (MATB) II tasks. Furthermore, three types of EEG features, namely, absolute power, relative power, and slow-wave/fast-wave (SW/FW), were explored using spectral analysis. In addition, repeated analysis of variance (ANOVA) was conducted in three brain regions (frontal, central, and parietal) × three brain lateralities (left, middle, and right) × two SA groups (LSA and HSA) to explore SA-sensitive EEG features. The statistical results indicate a significant difference between the two SA groups according to SAGAT scores; moreover, no significant difference was found for the absolute power of four waves (delta (δ), theta (θ), alpha (α), and beta (β)). In addition, the LSA group had a significantly lower β relative power than the HSA group in central and partial regions. Lastly, compared with the HSA group, the LSA group had higher θ/β and (θ + α)/(α + β) in all analyzed brain regions, higher α/β in the parietal region, and higher (θ + α)/β in all analyzed regions except for the left and right laterality in the frontal region. The above SA-sensitive EEG features were fed into principal component analysis (PCA) and the Bayes method to discriminate different SA groups, and the accuracies were 83.3% for the original validation and 70.8% for the cross-validation. The results provide a basis for real-time assessment and discrimination of SA by investigating EEG features, thus contributing to monitoring SA decrement that might lead to threats to flight safety.

Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment

“…The classification model has outperformed other baseline algorithms with an accuracy of 66.5%. Feng et al (2022) used SA-sensitive EEG features fed into principal component analysis (PCA) and the Bayes method to discriminate different SA groups, and the accuracies were 83.3% for the original validation and 70.8% for the cross-validation. Jiang et al (2023) developed an RF algorithm by PCA on EEG features with significant correlation with SA for further feature combination, which was then fed into CNN classification algorithm to obtain a classification recognition accuracy of 84.8%.…”

Blink-Related Oscillations Provide Naturalistic Assessments of Brain Function and Cognitive Workload within Complex Real-World Multitasking Environments

“…MATB is well-established as a computer-based task battery that effectively simulates multi-tasking through its constituent tasks consisting of system monitoring, tracking, communications, resource management, and scheduling [ 52 ]. While several studies have utilized traditional EEG-based measurements (e.g., power in canonical frequency bands ) to assess cognitive constructs such as workload and situational awareness using MATB tasks [ 53 , 54 , 55 ], no study has evaluated the potential of BRO responses to assess cognition while participants complete MATB tasks. This study evaluated BRO responses while adult human participants completed the MATB under various task difficulty levels.…”