Spatio-temporal EEG power spectral patterns during a short daytime nap

Assessment of sleep quality in powernapping

The purpose of this study is to assess the Sleep Quality (SQ) in powernapping. The contributed factors for SQ assessment are time of Sleep Onset (SO), Sleep Length (SL), Sleep Depth (SD), and detection of sleep events (K-complex (KC) and Sleep Spindle (SS)). Data from daytime nap for 10 subjects, 2 days each, including EEG and ECG were recorded. The SD and sleep events were analyzed by applying spectral analysis. The SO time was detected by a combination of signal spectral analysis, Slow Rolling Eye Movement (SREM) detection, Heart Rate Variability (HRV) analysis and EEG segmentation using both Autocorrelation Function (ACF), and Crosscorrelation Function (CCF) methods. The EEG derivation FP1-FP2 filtered in a narrow band and used as an alternative to EOG for SREM detection. The ACF and CCF segmentation methods were also applied for detection of sleep events. The ACF method detects segment boundaries based on single channel analysis, while the CCF includes spatial variation from multiple EEG derivation. The results indicate that SREM detection using EEG is possible and can be used as input together with power spectral analysis to enhance SO detection. Both segmentation methods could detect SO as a segment boundary. Additionally they were able to contribute to detection of KC and SS events. The CCF method was more sensitive to spatial EEG changes and the exact segment boundaries varied slightly between the two methods. The HRV analysis revealed, that low and very low frequency variations in the heart rate was highly correlated with the EEG changes during both SO and variations in SD. Analyzing the relationship between the sleep events and SD showed a negative correlation between the Delta and Sigma activity. Analyzing the subjective measurement (SM) showed that there were a positive correlation between the SL and rated SQ. This preliminary study showed that the factors contributing to the overall SQ during powernapping can be assessed markedly better using a fusion of multiple methods. Future studies will include measures of individual performance before and after powernapping and investigate its relation to the assessed SQ.

Low-resolution brain electromagnetic tomography (LORETA) identifies brain regions linked to psychometric performance under modafinil in narcolepsy

Low-resolution brain electromagnetic tomography (LORETA) showed a functional deterioration of the fronto-temporo-parietal network of the right hemispheric vigilance system in narcolepsy and a therapeutic effect of modafinil. The aim of this study was to determine the effects of modafinil on cognitive and thymopsychic variables in patients with narcolepsy and investigate whether neurophysiological vigilance changes correlate with cognitive and subjective vigilance alterations at the behavioral level. In a double-blind, placebo-controlled crossover design, EEG-LORETA and psychometric data were obtained during midmorning hours in 15 narcoleptics before and after 3 weeks of placebo or 400 mg modafinil. Cognitive investigations included the Pauli Test and complex reaction time. Thymopsychic/psychophysiological evaluation comprised drive, mood, affectivity, wakefulness, depression, anxiety, the Symptom Checklist 90 and critical flicker frequency. The Multiple Sleep Latency Test (MSLT) and the Epworth Sleepiness Scale (ESS) were performed too. Cognitive performance (Pauli Test) was significantly better after modafinil than after placebo. Concerning reaction time and thymopsychic variables, no significant differences were observed. Correlation analyses revealed that a decrease in prefrontal delta, theta and alpha-1 power correlated with an improvement in cognitive performance. Moreover, drowsiness was positively correlated with theta power in parietal and medial prefrontal regions and beta-1 and beta-2 power in occipital regions. A less significant correlation was observed between midmorning EEG LORETA and the MSLT; between EEG LORETA and the ESS, the correlation was even weaker. In conclusion, modafinil did not influence thymopsychic variables in narcolepsy, but it significantly improved cognitive performance, which may be related to medial prefrontal activity processes identified by LORETA.

Regional differences of the temporal EEG dynamics during the first 30 min of human sleep

The analysis of the time-course of different EEG bands during the first sleep cycle has up to now taken into account mostly central derivations. In the present study, we assessed the topographical differences of the time-course of different EEG bands during the first 30 min of sleep, by analysing the EEG power from four different scalp locations along the antero-posterior axis. Correlation of EEG bands time courses within and between each derivation has also been evaluated. Delta-theta (1-6.75 Hz) and alpha (7-10.75 Hz) activities exhibited an antero-posterior gradient with maximal power on the frontal lead and increased at all derivations in the first 15 min and 5 min periods, respectively. Then alpha power decreased at all derivations, but the frontal. Sigma EEG power (11-15.75 Hz) showed a coherent behavior over the four derivations, characterized by a steep increase in the first 3-5 min of sleep, followed by a stable decreasing trend. Beta power (16-25.75 Hz) linearly decreased only on the more posterior derivations, abruptly increasing on the frontal lead after a 15 min interval. Correlations between delta-theta and alpha band were higher on the frontal derivation. Moreover, frontal alpha was strongly related to delta-theta activity on all the four derivations, while occipital alpha was not. The negative correlations between delta-theta and beta time courses were very high on all derivations but the frontal one. This study shows the existence of topographical differences in the time-course of different EEG bands during the first 30 min of sleep. The peculiar behavior of the alpha and beta EEG bands over the frontal derivation indicates the need to re-consider the functional role of traditional EEG bands during sleep.