The effects of motivation and noise on situation awareness: A study based on SAGAT and EEG

Liu, Yang; Chen, Na; Rau, Pei-Luen Patrick

doi:10.1016/j.ergon.2023.103491

Cited by 9 publications

(2 citation statements)

References 32 publications

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“… Kaur et al (2020) used the Stroop test to assess the effect of EEG pre-task rest information on SA and showed that Pre-task Resting Absolute Alpha (PRAA) and Pre-task Resting Alpha Frontal Asymmetry (PRAFA) were associated with performance on subsequent SA tasks when the task was subjected to the Stroop effect. Liu et al (2023) found that SA level 1 and 3 were significantly correlated with some EEG signals, which were mainly δ and θ band power, but there was no correlation between SA and the α and β band power. And there was no significant correlation between SA level 2 and all EEG signals.…”

Section: Introductionmentioning

confidence: 86%

“…Kaur et al (2020) with performance on subsequent SA tasks when the task was subjected to the Stroop effect. Liu et al (2023) found that SA level 1 and 3 were significantly correlated with some EEG signals, which were mainly δ and θ band power, but there was no correlation between SA and the α and β band power. And there was no significant correlation between SA level 2 and all EEG signals.…”

Section: Introductionmentioning

confidence: 86%

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

Li,

Kang,

Chen

et al. 2024

Front. Neurosci.

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Lack of situation awareness (SA) is the primary cause of human errors when operating forklifts, so determining the SA level of the forklift operator is crucial to the safety of forklift operations. An EEG recognition approach of forklift operator SA in actual settings was presented in order to address the issues with invasiveness, subjectivity, and intermittency of existing measuring methods. In this paper, we conducted a field experiment that mimicked a typical forklift operation scenario to verify the differences in EEG states of forklift operators with different SA levels and investigate the correlation of multi-band combination features of each brain region of forklift operators with SA. Based on the sensitive EEG combination indexes, Support Vector Mechanism was used to construct a forklift operator SA recognition model. The results revealed that there were differences between forklift operators with high and low SA in the θ, α, and β frequency bands in zones F, C, P, and O; combined EEG indicators θ/β, (α + θ)/(α + β), and θ/(α + β) in zones F, P, and C were significantly correlated with SA; the recognition accuracy of the model reached 88.64% in the case of combined EEG indicators of zones C & F & P as input. It could provide a reference for SA measurement, contributing to the improvement of SA.

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Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 86%