Neurophysiological Dataset of Stress Resilience During Human-Computer Interaction

This dataset provides multimodal neurophysiological and physiological
recordings collected to investigate stress resilience. It comprises
electroencephalography, functional near-infrared spectroscopy, electrodermal
activity, blood volume pulse, inter-beat intervals, heart rate, and
accelerometer readings from 35 participants engaged in a computer-based task
under stress. The experimental design consisted of six sequential phases:
Resting Baseline, Working Baseline, Stress 1, Recovery 1, Stress 2, and
Recovery 2. Event markers were synchronized across all modalities to track
transitions between task conditions. Additionally, subjective resilience was
assessed using the 10-item Connor-Davidson Resilience Scale. The dataset also
includes demographic variables such as age, gender, and ethnic background.
This dataset is valuable for research on stress resilience, neuroergonomics,
and machine learning applications in human factors. Researchers can utilize
the data to analyze neural and physiological responses to stress, develop
predictive models, and evaluate adaptive training strategies. By making this
dataset publicly available, we aim to support reproducibility,
interdisciplinary collaboration, and advancements in neuroergonomics.