Power spectral analysis is the most common method of quantitative electroencephalogram (qEEG) techniques and enables investigation of the microstructure of insomnia. Previous spectral analysis studies on insomnia have shown inconsistent results due to their heterogeneity and small sample sizes.

We compared the difference of electroencephalogram (EEG) spectral power during sleep among participants without insomnia, insomniacs with no hypnotic use, hypnotic users with no insomnia complaints, and hypnotic users with insomnia complaints.

We used the Sleep Heart Health Study data, which is large sample size and has good quality control. The fast Fourier transformation was used to calculate the EEG power spectrum for total sleep duration within contiguous 30-second epochs of sleep. For 1,985 participants, EEG spectral power was compared among the groups while adjusting for potential confounding factors that could affect sleep EEG.

The power spectra during total sleep differed significantly among the groups in all frequency bands (p corr < 0.001). We found that quantitative EEG spectral power in the beta and sigma bands of total sleep differed (p corr < 0.001) between participants without insomnia and hypnotic users with insomnia complaints after controlling for potential confounders. The higher beta and sigma power were found in the hypnotic users with insomnia complaints than in the non-insomnia participants.

This study suggests differences in the microstructures of polysomnography-derived sleep EEG between the insomnia groups.

No significant relationships.

Memory deficits are dominant in dementia and are positively correlated with electroencephalographic (EEG) beta power. EEG beta power can predict the progression of Alzheimer´s (AD) as early as at the stage of mild cognitive impairment (MCI) and could possibly be used as surrogate marker for memory impairment. The objective of this study is to analyze the relationship between frontal and parietal EEG beta power and memory-test outcome. Frontal and parietal beta power is analyzed for a resting state and an eyes-closed backward counting condition and related to memory impairment parameters.

A total of 28 right-handed female geriatric patients (mean age = 80.6) participated voluntarily in this study. Beta 1 (12.9–19.2 Hz) and beta 2 (19.2–32.4 Hz) EEG power at F3, F4, Fz, P3, P4, and Pz are correlated with immediate wordlist recall, delayed wordlist recall, recognition of learned words, and delayed figure recall. For classification between impaired and intact memory, we calculated a binary logistic regression model with memory impairment as a dependent variable and beta 2 power as an independent variable.

We found significant positive correlations between frontal and parietal beta power and delayed memory recall. A significant correlation (Bonferroni correction, p < 0.05) was found at F4 beta 2 during backward counting. The binary logistic regression model with F4 beta 2 power during the counting condition as a predictor yielded a sensitivity of 76.9% (95% CI) and a specificity of 73.3% (95% CI) for classifying patients into “verbal-memory impaired” and “intact.”

EEG beta 2 power recorded during a backward counting condition with eyes closed can be used as surrogate marker for verbal memory impairment in geriatric patients. Antidepressant treatment was correlated with EEG data in resting state but not in counting condition. Further studies are necessary to verify the results of this pilot study.