Back

The effect of task demand on EEG responses to irrelevant sound and speech in simulated surgical environments.

Rosenkranz, M.; Uslar, V. N.; Weyhe, D.; Bleichner, M. G.

2025-02-17 neuroscience
10.1101/2025.02.13.638036 bioRxiv
Show abstract

1Complex soundscapes in high-stakes environments, such as the operating room (OR), are characterized by a variety of overlapping auditory stimuli and present significant challenges for personnel, particularly during periods of high demand. This study investigates how task demand and an OR soundscape including irrelevant speech, influence perceived work-load, surgical performance, and auditory processing in a simulated surgical environment, using mobile electroencephalography (EEG). Participants performed two simulated surgical tasks, namely peg transfer and suturing, representing a low-demand and high-demand task, respectively. The tasks were performed under two sound conditions: An OR soundscape was presented with irrelevant speech or alone. Neural responses to transient and continuous auditory stimuli were analyzed using event-related potentials (ERPs) and temporal response functions (TRFs), respectively. Results showed that irrelevant speech increased self-reported workload and distraction. EEG analyses revealed reduced neural responses to transient sounds and irrelevant speech under high task demand, reflecting early-stage sensory filtering of auditory distractions. Notably, an inverse relationship was observed between neural responses to speech and self-reported workload, indicating that the speech responses may serve as a marker for perceived workload. Overall, this study demonstrates the potential of EEG to assess irrelevant sound processing in realistic work-like settings and highlights the critical role of task demand in modulating neural responses and self-reported workload to soundscapes.

Published in Psychophysiology (predicted rank #11) · training set

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.