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Journal articles on the topic 'Stationarity of human sleep EEG'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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1

Massimini, Marcello, Mario Rosanova, and Maurizio Mariotti. "EEG Slow (∼1 Hz) Waves Are Associated With Nonstationarity of Thalamo-Cortical Sensory Processing in the Sleeping Human." Journal of Neurophysiology 89, no. 3 (March 1, 2003): 1205–13. http://dx.doi.org/10.1152/jn.00373.2002.

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Intracellular studies reveal that, during slow wave sleep (SWS), the entire cortical network can swing rhythmically between extremely different microstates, ranging from wakefulness-like network activation to functional disconnection in the space of a few hundred milliseconds. This alternation of states also involves the thalamic neurons and is reflected in the EEG by a slow (<1 Hz) oscillation. These rhythmic changes, occurring in the thalamo-cortical circuits during SWS, may have relevant, phasic effects on the transmission and processing of sensory information. However, brain reactivity to sensory stimuli, during SWS, has traditionally been studied by means of sequential averaging, a procedure that necessarily masks any short-term fluctuation of responsiveness. The aim of this study was to provide a dynamic evaluation of brain reactivity to sensory stimuli in naturally sleeping humans. To this aim, single-trial somatosensory evoked potentials (SEPs) were grouped and averaged as a function of the phase of the ongoing sleep slow (<1 Hz) oscillation. This procedure revealed a dynamic profile of responsiveness, which was conditioned by the phase of the spontaneous sleep EEG. Overall, the amplitude of the evoked potential changed sistematically, increasing and approaching wakefulness levels along the negative slope of the EEG oscillation and decaying below SWS average levels along the positive drift. These marked and fast changes of stimulus-correlated electrical activity involved both short (N20) and long latency (P60 and P100) components of SEPs. In addition, the observed short-term response variability appeared to be centrally generated and specifically related to the evolution of the spontaneous oscillatory pattern. The present findings demonstrate that thalamo-cortical processing of sensory information is not stationary in the very short period (approximately 500 ms) during natural SWS.

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Olbrich, E., P. Achermann, and P. F. Meier. "Dynamics of human sleep EEG." Neurocomputing 52-54 (June 2003): 857–62. http://dx.doi.org/10.1016/s0925-2312(02)00816-0.

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3

Purnima, B. R., N. Sriraam, U. Krishnaswamy, and K. Radhika. "A Measure to Detect Sleep Onset Using Statistical Analysis of Spike Rhythmicity." International Journal of Biomedical and Clinical Engineering 3, no. 1 (January 2014): 27–41. http://dx.doi.org/10.4018/ijbce.2014010103.

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Electroencephalogram (EEG) signals derived from polysomnography recordings play an important role in assessing the physiological and behavioral changes during onset of sleep. This paper suggests a spike rhythmicity based feature for discriminating the wake and sleep state. The polysomnography recordings are segmented into 1 second EEG patterns to ensure stationarity of the signal and four windowing scheme overlaps (0%, 50%, 60% and 75%)of EEG pattern are introduced to study the influence of the pre-processing procedure. The application of spike rhythmicity feature helps to estimate the number of spikes from the given pattern with a threshold of 25%.Then non parametric statistical analysis using Wilcoxon signed rank test is introduced to evaluate the impact of statistical measures such as mean, standard deviation, p-value and box-plot analysis under various conditions .The statistical test shows significant difference between wake and sleep with p<0.005 for the applied feature, thus demonstrating the efficiency of simple thresholding in distinguishing sleep and wake stage .

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Hiremath, Basavaraj, Natarajan Sriraam, B. R. Purnima, Nithin N. S., Suresh Babu Venkatasamy, and Megha Narayanan. "EEG-Based Demarcation of Yogic and Non-Yogic Sleep Patterns Using Power Spectral Analysis." International Journal of E-Health and Medical Communications 12, no. 6 (November 2021): 1–18. http://dx.doi.org/10.4018/ijehmc.20211101.oa2.

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Electroencephalogram (EEG) signals resulting from recordings of polysomnography play a significant role in determining the changes in physiology and behavior during sleep. This study aims at demarcating the sleep patterns of yogic and non-yogic subjects. Frequency domain features based on power spectral density methods were explored in this study. The EEG recordings were segmented into 1s and 0.5s. EEG patterns with four windowing scheme overlaps (0%, 50%, 60% and 75%) to ensure stationarity of the signal in order to investigate the effect of the pre-processing stage. In order to recognize the yoga and non-yoga group through N3 sleep stage, non-linear KNN classifier was introduced and performance was evaluated in terms of sensitivity and specificity. The experimental results show that modified covariance PSD estimate is the best method in classifying the sleep stage N3 of yogic and non-yogic subjects with 95% confidence interval, sensitivity, specificity and accuracy of 97.3%, 98% and 97%, respectively.

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5

Finelli, L. "Individual 'Fingerprints' in Human Sleep EEG Topography." Neuropsychopharmacology 25, no. 5 (November 2001): S57—S62. http://dx.doi.org/10.1016/s0893-133x(01)00320-7.

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6

Kim, Hyungrae, Christian Guilleminault, Seungchul Hong, Daijin Kim, Sooyong Kim, Hyojin Go, and Sungpil Lee. "Pattern analysis of sleep-deprived human EEG." Journal of Sleep Research 10, no. 3 (September 26, 2001): 193–201. http://dx.doi.org/10.1046/j.1365-2869.2001.00258.x.

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7

Werth, Esther, Peter Achermann, and Alexander A. Borbély. "Brain topography of the human sleep EEG." NeuroReport 8, no. 1 (December 1996): 123–27. http://dx.doi.org/10.1097/00001756-199612200-00025.

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8

SHENG, HU, YANGQUAN CHEN, and TIANSHUANG QIU. "MULTIFRACTIONAL PROPERTY ANALYSIS OF HUMAN SLEEP EEG SIGNALS." International Journal of Bifurcation and Chaos 22, no. 04 (April 2012): 1250080. http://dx.doi.org/10.1142/s0218127412500800.

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Electroencephalogram (EEG), the measures and records of the electrical activity of the brain, exhibits evidently nonlinear, nonstationary, chaotic and complex dynamic properties. Based on these properties, many nonlinear dynamical analysis techniques have emerged, and much valuable information has been extracted from complex EEG signals using these nonlinear analysis techniques. Among these techniques, the Hurst exponent estimation was widely used to characterize the fractional or scaling property of the EEG signals. However, the constant Hurst exponent H cannot capture the detailed information of dynamic EEG signals. In this research, the multifractional property of the normal human sleep EEG signals is investigated and characterized using local Hölder exponent H(t). The comparison of the analysis results for human sleep EEG signals in different stages using constant Hurst exponent H and the local Hölder exponent H(t) are summarized with tables and figures in the paper. The results of the analysis show that local Hölder exponent provides a novel and valid tool for dynamic assessment of brain activities in different sleep stages.

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9

Kobayashi, Toshio, Shigeki Madokoro, Yuji Wada, Kiwamu Misaki, and Hiroki Nakagawa. "Human Sleep EEG Analysis Using the Correlation Dimension." Clinical Electroencephalography 32, no. 3 (July 2001): 112–18. http://dx.doi.org/10.1177/155005940103200305.

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Sleep electroencephalograms (EEG) were analyzed by non-linear analysis. Polysomnography (PSG) of nine healthy male subjects was analyzed and the correlation dimension (D2) was calculated. The D2 characterizes the dynamics of the sleep EEG, estimates the degrees of freedom, and describes the complexity of the signal. The mean D2 decreased from the awake stage to stages 1,2,3 and 4 and increased during rapid eye movement (REM) sleep. The D2 during each REM sleep stage were high and those during each slow wave sleep stage were low, respectively, for each sleep cycle. The mean D2 of the sleep EEG in the second half of the night was significantly higher than those in the first half of the night. Significant changes were also observed during sleep stage 2, but were not seen during REM sleep and sleep stages 3 and 4. The D2 may be a useful method in the analysis of the entire sleep EEG.

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10

Fell, J., and J. Röschke. "Nonlinear Dynamical Aspects of the Human Sleep EEG." International Journal of Neuroscience 76, no. 1-2 (January 1994): 109–29. http://dx.doi.org/10.3109/00207459408985997.

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11

WERTH, ESTHER, PETER ACHERMANN, and ALEXANDER BORBÉLY. "Fronto‐occipital EEG power gradients in human sleep." Journal of Sleep Research 6, no. 2 (June 1997): 102–12. http://dx.doi.org/10.1046/j.1365-2869.1997.d01-36.x.

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12

AESCHBACH, DANIEL, and ALEXANDER A. BORBÉLY. "All-night dynamics of the human sleep EEG." Journal of Sleep Research 2, no. 2 (June 1993): 70–81. http://dx.doi.org/10.1111/j.1365-2869.1993.tb00065.x.

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13

Rial, Rubén, Julián González, Lluis Gené, Mourad Akaârir, Susana Esteban, Antoni Gamundí, Pere Barceló, and Cristina Nicolau. "Asymmetric sleep in apneic human patients." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 304, no. 3 (February 1, 2013): R232—R237. http://dx.doi.org/10.1152/ajpregu.00302.2011.

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Unilateral sleep in marine mammals has been considered to be a defense against airway obstruction, as a sentinel for pod maintenance, and as a thermoregulatory mechanism. Birds also show asymmetric sleep, probably to avoid predation. The variable function of asymmetric sleep suggests a general capability for independence between brain hemispheres. Patients with obstructive sleep apnea share similar problems with diving mammals, but their eventual sleep asymmetry has received little attention. The present report shows that human sleep apnea patients also present temporary interhemispheric variations in dominance during sleep, with significant differences when comparing periods of open and closed airways. The magnitude of squared coherence, an index of interhemispheric EEG interdependence in phase and amplitude, rises in the delta EEG range during apneic episodes, while the phase lag index, a measure of linear and nonlinear interhemispheric phase synchrony, drops to zero. The L index, which measures generalized nonlinear EEG interhemispheric synchronization, increases during apneic events. Thus, the three indexes show significant and congruent changes in interhemispheric symmetry depending on the state of the airways. In conclusion, when confronted with a respiratory challenge, sleeping humans undergo small, but significant, breathing-related oscillations in interhemispheric dominance, similar to those observed in marine mammals. The evidence points to a relationship between cetacean unihemispheric sleep and their respiratory challenges.

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14

Dehghani, Nima, Sydney S. Cash, Andrea O. Rossetti, Chih Chuan Chen, and Eric Halgren. "Magnetoencephalography Demonstrates Multiple Asynchronous Generators During Human Sleep Spindles." Journal of Neurophysiology 104, no. 1 (July 2010): 179–88. http://dx.doi.org/10.1152/jn.00198.2010.

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Sleep spindles are ∼1 s bursts of 10–16 Hz activity that occur during stage 2 sleep. Spindles are highly synchronous across the cortex and thalamus in animals, and across the scalp in humans, implying correspondingly widespread and synchronized cortical generators. However, prior studies have noted occasional dissociations of the magnetoencephalogram (MEG) from the EEG during spindles, although detailed studies of this phenomenon have been lacking. We systematically compared high-density MEG and EEG recordings during naturally occurring spindles in healthy humans. As expected, EEG was highly coherent across the scalp, with consistent topography across spindles. In contrast, the simultaneously recorded MEG was not synchronous, but varied strongly in amplitude and phase across locations and spindles. Overall, average coherence between pairs of EEG sensors was ∼0.7, whereas MEG coherence was ∼0.3 during spindles. Whereas 2 principle components explained ∼50% of EEG spindle variance, >15 were required for MEG. Each PCA component for MEG typically involved several widely distributed locations, which were relatively coherent with each other. These results show that, in contrast to current models based on animal experiments, multiple asynchronous neural generators are active during normal human sleep spindles and are visible to MEG. It is possible that these multiple sources may overlap sufficiently in different EEG sensors to appear synchronous. Alternatively, EEG recordings may reflect diffusely distributed synchronous generators that are less visible to MEG. An intriguing possibility is that MEG preferentially records from the focal core thalamocortical system during spindles, and EEG from the distributed matrix system.

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15

Fell, Juergen, Mario Staedtgen, Wieland Burr, Edgar Kockelmann, Christoph Helmstaedter, Carlo Schaller, Christian E. Elger, and Guillen Fernandez. "Rhinal-hippocampal EEG coherence is reduced during human sleep." European Journal of Neuroscience 18, no. 6 (September 2003): 1711–16. http://dx.doi.org/10.1046/j.1460-9568.2003.02934.x.

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16

Moroni, Fabio, Lino Nobili, Giuseppe Curcio, Fabrizio De Carli, Fabiana Fratello, Cristina Marzano, Luigi De Gennaro, et al. "Sleep in the Human Hippocampus: A Stereo-EEG Study." PLoS ONE 2, no. 9 (September 12, 2007): e867. http://dx.doi.org/10.1371/journal.pone.0000867.

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17

Jenni, Oskar G., Alexander A. Borbély, and Peter Achermann. "Development of the nocturnal sleep electroencephalogram in human infants." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 286, no. 3 (March 2004): R528—R538. http://dx.doi.org/10.1152/ajpregu.00503.2003.

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The development of nocturnal sleep and the sleep electroencephalogram (EEG) was investigated in a longitudinal study during infancy. All-night polysomnographic recordings were obtained at home at 2 wk and at 2, 4, 6, and 9 mo after birth (analysis of 7 infants). Total sleep time and the percentage of quiet sleep or non-rapid eye movement sleep (QS/NREMS) increased with age, whereas the percentage of active sleep or rapid eye movement sleep (AS/REMS) decreased. Spectral power of the sleep EEG was higher in QS/NREMS than in AS/REMS over a large part of the 0.75- to 25-Hz frequency range. In both QS/NREMS and AS/REMS, EEG power increased with age in the frequency range <10 Hz and >17 Hz. The largest rise occurred between 2 and 6 mo. A salient feature of the QS/NREMS spectrum was the emergence of a peak in the sigma band (12-14 Hz) at 2 mo that corresponded to the appearance of sleep spindles. Between 2 and 9 mo, low-frequency delta activity (0.75-1.75 Hz) showed an alternating pattern with a high level occurring in every other QS/NREMS episode. At 6 mo, sigma activity showed a similar pattern. In contrast, theta activity (6.5-9 Hz) exhibited a monotonic decline over consecutive QS/NREMS episodes, a trend that at 9 mo could be closely approximated by an exponential function. The results suggest that 1) EEG markers of sleep homeostasis appear in the first postnatal months, and 2) sleep homeostasis goes through a period of maturation. Theta activity and not delta activity seems to reflect the dissipation of sleep propensity during infancy.

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18

Vincze, Janos. "The Sleep Modeling in the Human Organism." Clinical Research and Clinical Trials 3, no. 4 (May 28, 2021): 01–04. http://dx.doi.org/10.31579/2693-4779/039.

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There are three alternating states of vigilance throughout our lives: wakefulness, NREM, and REM sleep. We usually yawn before falling asleep. Yawning is an ancient reaction, an instinctive action, manifested in a person by drowsiness or boredom. Yawning is often associated with the need for stretching. Yawning is a less strong territorial reflex. During deep sleep muscular tone is sharply reduced. Relaxation of the muscles and the lowering of their tone, howeever, are not constant and necessary components of sleep. Analysis of EEG recordings soon revealed that sleep is by no means a uniform process, but can be divided into at least two sharply separated states: one is characterized by slow waves in the EEG that are completely separate from the activity of wakefulness: this so-called slow wave sleep; the other is the so-called paradoxical sleep. Hypnopedia, as a discipline, deals with the input of fixed information introduced during the period of natural sleep, also known as sleep learning. Our hypnopedia researches was a pleasant surprise, because they were able to reproduce texts they did not know with an efficiency of approx. 25%.

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19

Schönwald, Suzana V., Günther J. L. Gerhardt, Emerson L. de Santa-Helena, and Márcia L. F. Chaves. "Characteristics of human EEG sleep spindles assessed by Gabor transform." Physica A: Statistical Mechanics and its Applications 327, no. 1-2 (September 2003): 180–84. http://dx.doi.org/10.1016/s0378-4371(03)00473-4.

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20

Shen, Y., E. Olbrich, P. Achermann, and P. F. Meier. "Dimensional complexity and spectral properties of the human sleep EEG." Clinical Neurophysiology 114, no. 2 (February 2003): 199–209. http://dx.doi.org/10.1016/s1388-2457(02)00338-3.

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21

Cox, Roy, and Juergen Fell. "Analyzing human sleep EEG: A methodological primer with code implementation." Sleep Medicine Reviews 54 (December 2020): 101353. http://dx.doi.org/10.1016/j.smrv.2020.101353.

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22

Linkowski, P. "Genetic Influences on EEG Sleep and the Human Circadian Clock." Pharmacopsychiatry 27, no. 01 (January 1994): 7–10. http://dx.doi.org/10.1055/s-2007-1014266.

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23

Lee, Jong-Min, Dae-Jin Kim, In-Young Kim, Kwang Suk Park, and Sun I. Kim. "Nonlinear-analysis of human sleep EEG using detrended fluctuation analysis." Medical Engineering & Physics 26, no. 9 (November 2004): 773–76. http://dx.doi.org/10.1016/j.medengphy.2004.07.002.

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24

Olbrich, E., and P. Achermann. "Oscillatory events in the human sleep EEG—detection and properties." Neurocomputing 58-60 (June 2004): 129–35. http://dx.doi.org/10.1016/j.neucom.2004.01.033.

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25

Tarokh, Leila, and Mary A. Carskadon. "Developmental Changes in the Human Sleep EEG During Early Adolescence." Sleep 33, no. 6 (June 2010): 801–9. http://dx.doi.org/10.1093/sleep/33.6.801.

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26

Trachsel, L., W. Schreiber, F. Holsboer, and T. Pollmächer. "Endotoxin Enhances EEG Alpha and Beta Power in Human Sleep." Sleep 17, no. 2 (March 1994): 132–39. http://dx.doi.org/10.1093/sleep/17.2.132.

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27

Ferrillo, F., G. Rodrigues, G. Rosadini, and W. G. Sannita. "Quantitative EEG during human sleep: Within-and between-hemisphere differences." Biological Psychology 20, no. 3 (May 1985): 197–98. http://dx.doi.org/10.1016/0301-0511(85)90074-2.

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28

Ferrara, Michele, Luigi De Gennaro, Giuseppe Curcio, Riccardo Cristiani, and Mario Bertini. "Interhemispheric asymmetry of human sleep EEG in response to selective slow-wave sleep deprivation." Behavioral Neuroscience 116, no. 6 (2002): 976–81. http://dx.doi.org/10.1037/0735-7044.116.6.976.

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29

Tosun, Pinar Deniz, Derk-Jan Dijk, Raphaelle Winsky-Sommerer, and Daniel Abasolo. "Effects of Ageing and Sex on Complexity in the Human Sleep EEG: A Comparison of Three Symbolic Dynamic Analysis Methods." Complexity 2019 (January 2, 2019): 1–12. http://dx.doi.org/10.1155/2019/9254309.

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Symbolic dynamic analysis (SDA) methods have been applied to biomedical signals and have been proven efficient in characterising differences in the electroencephalogram (EEG) in various conditions (e.g., epilepsy, Alzheimer’s, and Parkinson’s diseases). In this study, we investigated the use of SDA on EEGs recorded during sleep. Lempel-Ziv complexity (LZC), permutation entropy (PE), and permutation Lempel-Ziv complexity (PLZC), as well as power spectral analysis based on the fast Fourier transform (FFT), were applied to 8-h sleep EEG recordings in healthy men (n=31) and women (n=29), aged 20-74 years. The results of the SDA methods and FFT analysis were compared and the effects of age and sex were investigated. Surrogate data were used to determine whether the findings with SDA methods truly reflected changes in nonlinear dynamics of the EEG and not merely changes in the power spectrum. The surrogate data analysis showed that LZC merely reflected spectral changes in EEG activity, whereas PE and PLZC reflected genuine changes in the nonlinear dynamics of the EEG. All three SDA techniques distinguished the vigilance states (i.e., wakefulness, REM sleep, NREM sleep, and its sub-stages: stage 1, stage 2, and slow wave sleep). Complexity of the sleep EEG increased with ageing. Sex on the other hand did not affect the complexity values assessed with any of these three SDA methods, even though FFT detected sex differences. This study shows that SDA provides additional insights into the dynamics of sleep EEG and how it is affected by ageing.

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30

Ferrara, Michele, and Luigi De Gennaro. "Going Local: Insights from EEG and Stereo-EEG Studies of the Human Sleep-Wake Cycle." Current Topics in Medicinal Chemistry 11, no. 19 (September 1, 2011): 2423–37. http://dx.doi.org/10.2174/156802611797470268.

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31

Roth, Corinne, Peter Achermann, and Alexander A. Borbély. "Alpha activity in the human REM sleep EEG: topography and effect of REM sleep deprivation." Clinical Neurophysiology 110, no. 4 (April 1999): 632–35. http://dx.doi.org/10.1016/s1388-2457(98)00060-1.

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32

Uchida, Sunao, Nobuyuki Okudaira, Kyoko Nishihara, Yoshinobu Iguchi, and Xin Tan. "Flunitrazepam effects on human sleep eeg spectra II: Sigma and beta alterations during NREM sleep." Life Sciences 59, no. 9 (July 1996): 117–20. http://dx.doi.org/10.1016/0024-3205(96)00367-0.

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33

Hasan, Md Junayed, Dongkoo Shon, Kichang Im, Hyun-Kyun Choi, Dae-Seung Yoo, and Jong-Myon Kim. "Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals." Applied Sciences 10, no. 21 (October 29, 2020): 7639. http://dx.doi.org/10.3390/app10217639.

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This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively.

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Dijk, D. J., D. G. Beersma, S. Daan, and A. J. Lewy. "Bright morning light advances the human circadian system without affecting NREM sleep homeostasis." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 256, no. 1 (January 1, 1989): R106—R111. http://dx.doi.org/10.1152/ajpregu.1989.256.1.r106.

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Eight male subjects were exposed to either bright light or dim light between 0600 and 0900 h for 3 consecutive days each. Relative to the dim light condition, the bright light treatment advanced the evening rise in plasma melatonin and the time of sleep termination (sleep onset was held constant) for an average approximately 1 h. The magnitude of the advance of the plasma melatonin rise was dependent on its phase in dim light. The reduction in sleep duration was at the expense of rapid-eye-movement (REM) sleep. Spectral analysis of the sleep electroencephalogram (EEG) revealed that the advance of the circadian pacemaker did not affect EEG power densities between 0.25 and 15.0 Hz during either non-REM or REM sleep. The data show that shifting the human circadian pacemaker by 1 h does not affect non-REM sleep homeostasis. These findings are in accordance with the predictions of the two-process model of sleep regulation.

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35

Roth, Corinne, Peter Achermann, and Alexander A. Borbély. "Frequency and state specific hemispheric asymmetries in the human sleep EEG." Neuroscience Letters 271, no. 3 (August 1999): 139–42. http://dx.doi.org/10.1016/s0304-3940(99)00048-8.

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36

Kobayashi, Toshio, Shigeki Madokoro, Yuji Wada, Kiwamu Misaki, and Hiroki Nakagawa. "Effect of Ethanol on Human Sleep EEG Using Correlation Dimension Analysis." Neuropsychobiology 46, no. 2 (2002): 104–10. http://dx.doi.org/10.1159/000065420.

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37

Li, Ning, Yan Wang, Mingshi Wang, and Haiying Liu. "Effects of sleep deprivation on gamma oscillation of waking human EEG." Progress in Natural Science 18, no. 12 (December 2008): 1533–37. http://dx.doi.org/10.1016/j.pnsc.2008.05.021.

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38

Inoue, Katsuhiro, Tomohiro Tsujihata, Yuma Aso, Kousuke Kumamaru, and Shigeki Matsuoka. "Feature Extraction of Human EEG Sleep Stages by Using Wavelet Analysis." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2004 (May 5, 2004): 140–45. http://dx.doi.org/10.5687/sss.2004.140.

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39

Landolt, H. "Caffeine Reduces Low-Frequency Delta Activity in the Human Sleep EEG." Neuropsychopharmacology 12, no. 3 (May 1995): 229–38. http://dx.doi.org/10.1016/0893-133x(94)00079-f.

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40

Motamedi-Fakhr, Shayan, Mohamed Moshrefi-Torbati, Martyn Hill, Catherine M. Hill, and Paul R. White. "Signal processing techniques applied to human sleep EEG signals—A review." Biomedical Signal Processing and Control 10 (March 2014): 21–33. http://dx.doi.org/10.1016/j.bspc.2013.12.003.

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41

Cajochen, Christian, Sat Bir S. Khalsa, James K. Wyatt, Charles A. Czeisler, and Derk-Jan Dijk. "EEG and ocular correlates of circadian melatonin phase and human performance decrements during sleep loss." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 277, no. 3 (September 1, 1999): R640—R649. http://dx.doi.org/10.1152/ajpregu.1999.277.3.r640.

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The aim of this study was to quantify the associations between slow eye movements (SEMs), eye blink rate, waking electroencephalogram (EEG) power density, neurobehavioral performance, and the circadian rhythm of plasma melatonin in a cohort of 10 healthy men during up to 32 h of sustained wakefulness. The time course of neurobehavioral performance was characterized by fairly stable levels throughout the first 16 h of wakefulness followed by deterioration during the phase of melatonin secretion. This deterioration was closely associated with an increase in SEMs. Frontal low-frequency EEG activity (1–7 Hz) exhibited a prominent increase with time awake and little circadian modulation. EEG alpha activity exhibited circadian modulation. The dynamics of SEMs and EEG activity were phase locked to changes in neurobehavioral performance and lagged the plasma melatonin rhythm. The data indicate that frontal areas of the brain are more susceptible to sleep loss than occipital areas. Frontal EEG activity and ocular parameters may be used to monitor and predict changes in neurobehavioral performance associated with sleep loss and circadian misalignment.

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Tsuji, Yoichi, Takefumi Usui, Yasuhisa Sato, and Kazuyuki Nagasawa. "Development of Automatic Scoring System for Sleep EEG Using Fuzzy Logic." Journal of Robotics and Mechatronics 5, no. 3 (June 20, 1993): 204–8. http://dx.doi.org/10.20965/jrm.1993.p0204.

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Determination of the EEG sleep stages by clinicians is a time consuming task, because of a great amount of record obtained during one night. Therefore, an effective computer technique for an automatic processing EEG is strongly desired. We tried to make an automatic scoring system for human sleep EEG stages by a computer. The specific EEG wave form (e.g. delta, theta, alpha or spindle wave) was detected by means of a specific algorithm using a Fuzzy logic and an ""if...then..."" procedure. The scoring results by this system and clinicians agreed with each epoch more than 72%. This system is an available system for the clinical sleep EEG.

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Perumalsamy, Venkatakrishnan, Sangeetha Sankaranarayanan, and Sukanesh Rajamony. "Sleep spindles detection from human sleep EEG signals using autoregressive (AR) model: a surrogate data approach." Journal of Biomedical Science and Engineering 02, no. 05 (2009): 294–303. http://dx.doi.org/10.4236/jbise.2009.25044.

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Ishii, T., T. Koike, E. Nakagawa, M. Sumiya, and N. Sadato. "0147 Dynamic Alterations in Functional Connectivity Between Sleep- and Wake-Promoting Regions of the Human Brain at the Sleep Onset Period." Sleep 43, Supplement_1 (April 2020): A58. http://dx.doi.org/10.1093/sleep/zsaa056.145.

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Abstract Introduction The sleep onset period, involving so-called stage N1 sleep largely, is characterized by a reduction in the amount of alpha activity compared to wakefulness. Various kinds of physiological and psychological changes are also apparent, such as slow eye movements, changes in muscle tonus, and the hypnagogic dream-like mentation. These phenomena are thought to be the reflection of dynamic alterations in the brain during the transition period, however, details of these changes have still been uncovered. Methods We aimed to investigate a dynamic shift in the brain connectivity at sleep onset using the method of EEG-fMRI simultaneous recording. Twenty-three healthy subjects participated. EEG/fMRI were recorded simultaneously during an hour’s nap in a 3T-MRI scanner and real-time monitoring of EEG was performed. To record the transition period between multiple times, an experimenter inside a scanner room touched a subject’s foot for inducing arousal when a shift to NREM sleep stage 1 was observed. EEG data were scored according to the AASM criteria. Based on sleep stages defined by polysomnographic findings, we investigated alterations in functional connectivity of sleep- and wake- promoting regions within the hypothalamus and other areas including the thalamus. Results Posterior alpha power showed significant positive correlation with BOLD signals in the anterior and medial dorsal thalamus. Connectivity between the thalamus and cortical regions reduced sharply in the descent to sleep stage. Meanwhile, BOLD signals of the sleep- and wake- promoting regions within the hypothalamus fluctuated with certain temporal lags from fluctuations of alpha rhythm at sleep onset. Conclusion Present findings provide preliminary evidence of dynamics of wake- and sleep- promoting regions in the human brain in vivo. Our data also support the hypothesis that reduced thalamocortical connectivity which limits the capacity to integrate information is associated with the transition of consciousness at sleep onset. Support None

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Cantero, Jose L., Mercedes Atienza, Joseph R. Madsen, and Robert Stickgold. "Gamma EEG dynamics in neocortex and hippocampus during human wakefulness and sleep." NeuroImage 22, no. 3 (July 2004): 1271–80. http://dx.doi.org/10.1016/j.neuroimage.2004.03.014.

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Wagner, Peter, Joachim Röschke, Klaus Mann, Jürgen Fell, Wolfgang Hiller, Clarissa Frank, and Michael Grözinger. "Human Sleep EEG under the Influence of Pulsed Radio Frequency Electromagnetic Fields." Neuropsychobiology 42, no. 4 (2000): 207–12. http://dx.doi.org/10.1159/000026695.

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Tan, Xin, Sunao Uchida, Masato Matsuura, Kyoko Nishihara, and Takuya Kojima. "Long-, intermediate- and short-acting benzodiazepine effects on human sleep EEG spectra." Psychiatry and Clinical Neurosciences 57, no. 1 (February 2003): 97–104. http://dx.doi.org/10.1046/j.1440-1819.2003.01085.x.

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Inoue, Katsuhiro, Tomohiro Tsujihata, Kousuke Kumamaru, and Shigeaki Matsuoka. "FEATURE EXTRACTION OF HUMAN SLEEP EEG BASED ON A PEAK FREQUENCY ANALYSIS." IFAC Proceedings Volumes 38, no. 1 (2005): 1059–64. http://dx.doi.org/10.3182/20050703-6-cz-1902.00177.

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Jin, Seung-Hyun, Sun Hee Na, Soo Yong Kim, and Dai-Jin Kim. "Effects of total sleep-deprivation on waking human EEG: functional cluster analysis." Clinical Neurophysiology 115, no. 12 (December 2004): 2825–33. http://dx.doi.org/10.1016/j.clinph.2004.07.001.

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Crespo-Garcia, Maite, Mercedes Atienza, and Jose L. Cantero. "Muscle Artifact Removal from Human Sleep EEG by Using Independent Component Analysis." Annals of Biomedical Engineering 36, no. 3 (January 29, 2008): 467–75. http://dx.doi.org/10.1007/s10439-008-9442-y.

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