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Understanding the effects of stress on the P300 response during naturalistic simulation of heights exposure

Cite this dataset

Zhu, Howe; Chen, Hsiang-Ting; Lin, Chin-Teng (2023). Understanding the effects of stress on the P300 response during naturalistic simulation of heights exposure [Dataset]. Dryad. https://doi.org/10.5061/dryad.v9s4mw70k

Abstract

Stress is a prevalent bodily response universally experienced and significantly affects a person's mental and cognitive state. The P300 response is a commonly observed brain behaviour that provides insight into a person's cognitive state. Previous works have documented the effects of stress on the P300 behaviour; however, only a few have explored the performance in a mobile and naturalistic experimental setup. Our study examined the effects of stress on the human brain's P300 behaviour through a height exposure experiment that incorporates complex visual, vestibular, and proprioceptive stimuli. A more complex sensory environment could produce translatable findings toward real-world behaviour and benefit emerging technologies such as brain-computer interfaces. 

Seventeen participants experienced our experiment that elicited the stress response through physical and virtual height exposure. We found two unique groups within our participants that exhibited contrasting behavioural performance and P300 target reaction response when in a stressed state (when walking at heights). One group performed worse when stressed and exhibited a significant decrease in parietal P300 peak amplitude and increased beta and gamma power. Contrarily, the group less affected by stress exhibited a change in their N170 peak amplitude and alpha/mu rhythms desynchronisation. The findings of our study suggest that a more individualised approach to assessing a person's behaviour performance under stress can aid in understanding P300 performance when experiencing stress.

Methods

The data was collected at UTS Techlab. All data was processed through Matlab and will require the EEGLab toolbox.

Usage notes

Matlab and EEGLab toolbox.

Funding

Australian Research Council, Award: DP210101093