A resting-state EEG dataset for sleep deprivation

Xiang, Chuqin; Fan, Xinrui; Bai, Duo; Lv, Ke; Lei, Xu · 2024 · Crossref

DOI: 10.1038/s41597-024-03268-2

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Summary

This paper introduces a comprehensive resting-state electroencephalogram (EEG) dataset designed to investigate the physiological and cognitive impacts of sleep deprivation (SD). Motivated by the widespread prevalence of insufficient sleep and its detrimental effects on cardiovascular health, cognitive function, and emotional regulation, the authors aim to provide open-access data to facilitate research into how SD alters brain activity. The study specifically focuses on capturing individual variability in responses to SD, linking neural changes measured via EEG with behavioral indicators of alertness, mood, and sleepiness. The study employed a within-subject experimental design involving 71 healthy participants (mean age 20 years) who underwent two testing sessions: one after normal sleep (NS) and one following 24–30 hours of total sleep deprivation. The sessions were counterbalanced to eliminate sequence effects, with an interval of 7 days to one month between conditions. During each session, participants completed the Psychomotor Vigilance Task (PVT) to assess alertness, followed by state scales measuring mood (PANAS, ATQ, SAI) and sleepiness (SSS, KSS). Resting-state EEG data were recorded using 61 electrodes, capturing five minutes of eyes-open activity and, for a subset of 38 participants, five minutes of eyes-closed activity. Trait scales, including the Pittsburgh Sleep Quality Index (PSQI) and Buss-Perry Aggression Questionnaire (BPAQ), were also administered to account for individual differences in sleep quality and personality. Technical validation confirmed that the SD protocol successfully induced significant physiological and behavioral changes. PVT results showed a significant increase in the standard deviation of reaction times and the number of lapses under SD, though median reaction time remained unchanged. Subjective measures indicated significant increases in negative affect, anxiety, and sleepiness (both SSS and KSS scores), while positive affect and depressive mood scores did not change significantly. EEG analysis revealed that absolute power during SD was generally higher than during NS, particularly in the low-frequency range (0.2–13 Hz), with a peak around 10 Hz. Topographical maps indicated that theta and alpha power were predominantly distributed in the frontal and occipital lobes, while beta power remained low in the parietal lobe across both conditions. The significance of this work lies in the release of a standardized, preprocessed EEG dataset in Brain Imaging Data Structure (BIDS) format, accompanied by detailed behavioral and trait data. By providing raw and processed data from a controlled SD experiment, the authors enable the broader scientific community to explore the neural correlates of sleep loss, individual susceptibility to SD, and the relationship between brain activity and cognitive or emotional deficits. This resource supports open science initiatives and facilitates further investigation into the mechanisms underlying sleep deprivation’s impact on human health.

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Topics

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• drowsiness detection algorithms
• drowsiness
• neuro workload indices

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• Empirical Findings: physiological data