Using EEG Signals to Assess Workload during Memory Retrieval in a
  Real-world Scenario
Kuan-Jung Chiang Steven Dong Chung-Kuan Cheng Tzyy-Ping Jung
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.