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。

使用脑电信号评估真实场景下记忆检索的负荷

Using EEG Signals to Assess Workload during Memory Retrieval in a  Real-world Scenario

How can we accurately identify new memory workloads while classifying known
memory workloads? Verifying DRAM (Dynamic Random Access Memory) using various
workloads is an important task to guarantee the quality of DRAM. A crucial
component in the process is open-set recognition which aims to detect new
workloads not seen in the training phase. Despite its importance, however,
existing open-set recognition methods are unsatisfactory in terms of accuracy
since they fail to exploit the characteristics of workload sequences. In this
paper, we propose Acorn, an accurate open-set recognition method capturing the
characteristics of workload sequences. Acorn extracts two types of feature
vectors to capture sequential patterns and spatial locality patterns in memory
access. Acorn then uses the feature vectors to accurately classify a
subsequence into one of the known classes or identify it as the unknown class.
Experiments show that Acorn achieves state-of-the-art accuracy, giving up to
37% points higher unknown class detection accuracy while achieving comparable
known class classification accuracy than existing methods.

本文介绍 Acorn，一种准确的开放集识别方法，它提取两种类型的特征向量以捕获内存访问的顺序和空间局部性模式，并用于精确地分类子序列，以达到高于现有方法的未知类检测准确度高达 37％的效果。