Objective: The Electroencephalogram (EEG) is gaining popularity as a
physiological measure for neuroergonomics in human factor studies because it is
objective, less prone to bias, and capable of assessing the dynamics of
cognitive states. This study investigated the associations between memory
workload and EEG during participants' typical office tasks on a single-monitor
and dual-monitor arrangement. We expect a higher memory workload for the
single-monitor arrangement. Approach: We designed an experiment that mimics the
scenario of a subject performing some office work and examined whether the
subjects experienced various levels of memory workload in two different office
setups: 1) a single-monitor setup and 2) a dual-monitor setup. We used EEG band
power, mutual information, and coherence as features to train machine learning
models to classify high versus low memory workload states. Main results: The
study results showed that these characteristics exhibited significant
differences that were consistent across all participants. We also verified the
robustness and consistency of these EEG signatures in a different data set
collected during a Sternberg task in a prior study. Significance: The study
found the EEG correlates of memory workload across individuals, demonstrating
the effectiveness of using EEG analysis in conducting real-world neuroergonomic
studies.

本文研究了单屏幕和双屏幕情况下，记忆工作量与脑电波之间的关系，并使用机器学习模型对高记忆工作量状态进行分类，研究结果表明 EEG 可以作为进行神经人类学研究的有效工具。