Relevant bibliographies by topics / Stationarity of human sleep EEG

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Stationarity of human sleep EEG'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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

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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Stationarity of human sleep EEG"

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Sadovský, Petr. "Analýza spánkového EEG." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2007. http://www.nusl.cz/ntk/nusl-233411.

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This thesis deals with analysis and processing of the Sleep Electroencephalogram (EEG) signals. The scope of this thesis can be split into several areas. The first area is application of the Independent Component Analysis (ICA) method for EEG signal analysis. A model of EEG signal formation is proposed and conditions under which this model is valid are examined. It is shown that ICA can be used to remove non-deterministic artifacts contained in the EEG signals. The second area of interest is analysis of stationarity of the Sleep EEG signal. Methods to identify stationary signal segments and to analyze statistical properties of these stationary segments are presented. The third area of interest focuses on spectral analysis of the Sleep EEG signals. Analyses are performed that shows the processes that form particular parts of EEG signals spectrum. Also, random signals that are an integral part of the EEG signals analysis are performed. The last area of interest focuses on elimination of the transition processes that are caused by the filtering of the short EEG signal segments.
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Adamczyk, Marek [Verfasser], and Rainer [Akademischer Betreuer] Landgraf. "Genetics of human sleep EEG : analysis of EEG microstructure in twins / Marek Adamczyk. Betreuer: Rainer Landgraf." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2015. http://d-nb.info/1098130588/34.

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Geissler, Eva. "Adenosine A₁ receptors in human sleep regulation studied by electroencephalography (EEG) and positron emission tomography (PET) /." Zürich : ETH, 2007. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=17227.

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Loughran, Sarah Patricia, and n/a. "The efffects of eletromagnetic fields emitted by mobile phones on human sleep and melatonin production." Swinburne University of Technology, 2007. http://adt.lib.swin.edu.au./public/adt-VSWT20070731.100218.

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The use of mobile phones is continually increasing throughout the world, with recent figures showing that there are currently more than 2 billion mobile phone users worldwide. However, despite the recognised benefits of the introduction and widespread use of mobile phone technologies, concerns regarding the potential health effects of exposure to the radiofrequency electromagnetic fields emitted by mobile phone handsets have similarly increased, leading to an increase in demand for scientific research to investigate the possibility of health effects related to the use of mobile phones. An increasing amount of radiofrequency bioeffects research related to mobile phone use has focussed on the possible effects of mobile phone exposure on human brain activity and function, particularly as the absorption of energy in the head and brain region is much higher than in other body regions, which is a direct result from the close proximity of the mobile phone to the head when in normal use. In particular, the use of sleep research has become a more widely used technique for assessing the possible effects of mobile phones on human health and wellbeing, and is particularly useful for providing important information in the establishment of possible radiofrequency bioeffects, especially in the investigation of potential changes in sleep architecture resulting from mobile phone use. A review of the previous literature showed that a number of studies have reported an increase in the electroencephalogram spectral power within the 8 � 14 Hz frequency range in both awake and sleep states following radiofrequency electromagnetic field exposure. In regards to sleep, the enhancements reported have not been entirely consistent, with some early studies failing to find an effect, while more recent studies have reported that the effect differs in terms of particular frequency range. However, in general the previous literature suggests that there is an effect of mobile phone emissions on the sleep electroencephalogram, particularly in the frequency range of sleep spindle activity. In addition to changes in spectral power, changes in other conventional sleep parameters and the production and secretion of melatonin have also been investigated, however, there has been little or no consistency in the findings of previous studies, with the majority of recent studies concluding that there is no influence of mobile phone radiofrequency fields on these parameters of sleep or melatonin. Following a detailed review of the previous research, the current study was developed with the aim to improve on previous methodological and statistical limitations, whilst also being the largest study to investigate mobile phone radiofrequency bioeffects on human sleep. The principle aims were thus to test for the immediate effects of mobile phone radiofrequency electromagnetic fields on human sleep architecture and the secretion of the pineal hormone, melatonin. The experiment included 50 participants who were randomly exposed to active and sham mobile phone exposure conditions (one week apart) for 30 minutes prior to a full night-time sleep episode. The experimental nights employed a randomised exposure schedule using a double-blind crossover design. Standard polysomnography was used to measure subsequent sleep, and in addition, participants were required to provide urine samples immediately following exposure and upon waking in the morning. A full dosimetric assessment of the exposure system was also performed in order to provide sufficient details of the exposure set-up used in the current thesis and to account for the lack of detailed dosimetric data provided in the majority of previous studies. The results of the current study suggest that acute exposure to a mobile phone prior to sleep significantly enhances electroencephalogram spectral power in the sleep spindle frequency range compared to the sham exposure condition. The current results also suggest that this mobile phone-induced enhancement in spectral power is largely transitory and does not linger throughout the night. Furthermore, a reduction in rapid eye movement sleep latency following mobile phone exposure was also found compared to the sham exposure, although interestingly, neither this change in rapid eye movement sleep latency or the enhancement in spectral power following mobile phone exposure, led to changes in the overall quality of sleep. Finally, the results regarding melatonin suggested that, overall, overnight melatonin secretion is unaffected by acute exposure to a mobile phone prior to sleep. In conclusion, the current study has confirmed that a short exposure to the radiofrequency electromagnetic fields emitted by a mobile phone handset immediately prior to sleep is sufficient to induce changes in brain activity in the initial part of sleep. The consequences or functional significance of this effect are currently unknown and it would be premature to draw conclusions about possible health consequences based on the findings of the current study.
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Cajochen, Christian Lorenz Anton. "Heart rate, submental EMG and core body temperature in relation to EEG slow-wave activity during human sleep : effect of light exposure and sleep deprivation /." [S.l.] : [s.n.], 1993. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=10384.

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Sin-JhanWei and 魏新展. "EEG-EOG Sensing Devices for Human-Computer Interaction and Sleep Analysis." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/379jdh.

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碩士
國立成功大學
醫學資訊研究所
106
Humans spend one-third of their lifetimes on sleep. One with good quality of sleep can improve his or her attention, memories and metabolism. However, not all of the humans can have good quality of sleep. For those people who have been plagued with sleep disorders should use a multi-channel polysomnography (PSG) to improve them at some selected hospitals. The PSG records whole-night sleep physiological signals and the measurement of brain activity (EEG), eye movements (EOG) and muscle activity (EMG) as the parameters for the experts to undertake the research for sleep stage scoring. The PSG has enormous and multiple biophysiological recording functions. During sleep recording, attaching a large number of electrodes leads to subjects has become one of the sleep disturbances to them, which often requires extra help from technicians. Compared with EEG, EOG uses electrodes placed around the eyes without blocked by thick hair on the forehead, where EEG signals are relatively accessible to be measured for sleep scoring. Therefore, we invented a set of eye-mask sensing device based on brain signals. In terms of hard drive device, we used low noise analog front-end (AFE) to design signals and harvest circuits in cooperation with a built-in Bluetooth of system on chip (SoC) for wired and wireless communication. In order to verify the convenience, accuracy, stability and applicability of this system, we conducted four types of experiments in this study. In Experiment 1, we collected 24 recording times of wearing eye-masks and 10 recording ones of setting up PSG to compare the two systems concerning their convenience. In Experiment 2, this system and PSG collected whole-night sleep recordings of both EEG and EOG signals on 11 healthy adults. This experiment attempts to prove that the signals are relevant and consistent with sleep staging scoring. In Experiment 3, for the purpose of daily uses, we collected recordings for 4 consecutive whole-night sleeps and 8 napping recordings to implement its stability. In Experiment 4, simultaneous eye movement detection algorithm was well applied to human-computer interaction games based on the structure of eye mask. According to the results of the three previous experiments, we suggested that this system and PSG have acquired 85% agreement with sleep scoring reaching up to the standard of clinical judgment at present. Furthermore, in comparison to PSG setting time of 47 minutes on average, this system enabled the subjects to spend only two minutes wearing it. Its wearable and convenient features were proven to be true on reducing time for its set up. For the research on human-computer interaction games, it was also carried out to detect eye movements in 0.377 seconds (standard deviation: 0.043) reaching up to 96% accuracy (standard deviation: 5.6). As discussed above, this system is expected to have significant effect on the measurement of sleep signals and human-computer interaction.
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Chen, Yen-Ru, and 陳彥儒. "Effects of Ambient Temperature Change on EEG, Sleep Quality and Autonomic Functions in Healthy Subjects: to Explore the Mechanism and Representable Indices for Human Comfort during Sleep." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/6fjzz2.

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碩士
國立陽明大學
腦科學研究所
105
Background: Thermal environment affect the sleep quality; however, there lacks the measurements for assessment of thermal environment during sleep. The thermal comfort during sleep still needs other objective parameters. Furthermore, it is also important to provide suitable environmental temperature for different sleep stages. Therefore, we want to investigate the influences of different environmental temperature on sleep-related physiological signals. Methods: This study is divided into two parts. Firstly, to measure the changes of autonomic nerves system (ANS) functions, electroencephalogram (EEG), electrocardiogram (EKG), and subjective feelings among different temperature during awake. Secondly, to explore the effects of different temperature on objective sleep quality. Analysis: The EEG, EKG data are received by miniature polysomnography, made by K&Y lab, and using FFT to analyze the ANS function and brain activity. Result: From the first part of experiment, we find the most comfortable temperature is 26 °C. When subjects in 26 °C, the Theta Power % is higher and Beta Power % is lower than other temperature. The RR variability in 22 °C, 24 °C is significant higher than in 26 °C and 28 °C. There is negative correlation between RR variability and temperature. (r = -0.309, P<0.01). In the second part of experiment, male subjects have significant longer slow wave sleep. But there is no significant different between subjective sleep quality questionnaire and the temperature. Conclusion: Different temperature will affect subjective feeling, physiological parameters, and sleep. According to the relationship between physiological parameters and subject questionnaire, we may build up the assessment of thermal comfort indices.
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Books on the topic "Stationarity of human sleep EEG"

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Sousek, Alexandra, and Mehdi Tafti. The genetics of sleep. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0005.

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Although there is strong evidence for a genetic contribution to inter-individual variations in sleep, the underlying factors and their interaction remain largely elusive. Much effort has been expended in studying genetic variations contributing to circadian and sleep phenotypes, the individual pattern of the human sleep EEG, reactions to sleep loss, and the pathophysiology of sleep-related disorders. Certain sleep-related diseases may be caused by single genes, while the etiology of others seems to be variable and complex. This is especially the case when the immune system is involved. This chapter reports on twin and familial studies, genetic variations and mutations affecting neurotransmitters and other signaling pathways and thereby affecting sleep, and impacts of gene expression processes and the immune system on sleep. Although much knowledge has been gained, further research is needed to elucidate the all-embracing mechanisms and their interactions that regulate sleep.
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Massimini, Marcello, and Giulio Tononi. Assessing Consciousness in Other Humans: From Theory to Practice. Translated by Frances Anderson. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198728443.003.0007.

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This chapter translates the theoretical principles illustrated in Chapter 5 into an empirical measure that can be applied to real human brains. It explains how transcranial magnetic stimulation (TMS) and simultaneous electroencephalography (EEG) can be employed to derive a surrogate measure of information integration, the perturbational complexity index (PCI). By describing the results of a series of experiments, it demonstrates that PCI can discriminate with very high accuracy between consciousness and unconsciousness, across many different conditions, ranging from wakefulness to sleep, dreaming esthesia and coma patients. The chapter ends by suggesting that principled measures of brain complexity can also help understanding the mechanisms of loss and recovery of consciousness in both physiological and pathological conditions.
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Book chapters on the topic "Stationarity of human sleep EEG"

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Song, In-Ho, In-Young Kim, Doo-Soo Lee, and Sun I. Kim. "Multiscale Characteristics of Human Sleep EEG Time Series." In Computational Science – ICCS 2006, 164–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758501_26.

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Born, J., and E. Späth-Schwalbe. "Effects of cytokines on human EEG and sleep." In Current Update in Psychoimmunology, 103–18. Vienna: Springer Vienna, 1997. http://dx.doi.org/10.1007/978-3-7091-6870-7_14.

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Khasawneh, Amro, Sergio A. Alvarez, Carolina Ruiz, Shivin Misra, and Majaz Moonis. "Similarity Grouping of Human Sleep Recordings Using EEG and ECG." In Biomedical Engineering Systems and Technologies, 380–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29752-6_28.

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Satapathy, Santosh Kumar, D. Loganathan, Shrinibas Pattnaik, and Ramakrushna Rath. "Automated Sleep Staging of Human Polysomnography Recordings Using Single-Channel of EEG Signals." In Advances in Mechanical Engineering, 183–92. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0942-8_17.

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NM, Muthayya. "Chapter-13 Reticular Formation, Sleep and EEG." In Human Physiology (4th ed), 573–81. NM Muthayya, 2009. http://dx.doi.org/10.5005/jp/books/10366_69.

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Ryvlin, Philippe, and Fabienne Picard. "Invasive EEG Investigation of the Insula." In Invasive Studies of the Human Epileptic Brain, edited by Samden D. Lhatoo, Philippe Kahane, and Hans O. Lüders, 367–77. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198714668.003.0027.

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Invasive EEG investigation of the insular cortex is performed in various forms of focal drug-resistant epilepsies, including patients with a clear-cut intra-insular epileptogenic lesion, such as focal cortical dysplasia, as well as patients whose non-invasive presurgical evaluation suggests perisylvian epilepsy, temporal plus epilepsy, sleep hypermotor epilepsy, or MRI-negative frontal or parietal lobe epilepsy. Stereo-EEG (SEEG) is currently the preferred method for investigating the insula, using orthogonal or oblique trajectories, or a combination, with no evidence of higher risk of intracranial bleeding than in other brain regions. Intra-insular ictal EEG patterns are often characterized by a prolonged focal discharge restricted to one of the five insular gyri, requiring dense sampling of the insular cortex in suspected insular epilepsies. SEEG also offers the potential to perform thermolesion of insular epileptogenic zones, which, together with MRI-guided laser ablation, represents a possibly safer alternative to open-skull surgical resection of the insula.
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DORR, C., T. CECCHIN, D. SAUTER, N. DI RENZO, and M. MOUZE-AMADY. "DETECTION, EXTRACTION AND SPECTRAL ANALYSIS OF SLEEP SPINDLES IN HUMAN EEG." In Signal Processing, 1729–32. Elsevier, 1992. http://dx.doi.org/10.1016/b978-0-444-89587-5.50134-1.

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Hari, Riitta, and Aina Puce. "Brain Rhythms." In MEG-EEG Primer, edited by Riitta Hari and Aina Puce, 165–88. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190497774.003.0010.

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This chapter provides examples of studies of MEG and EEG brain rhythms that have considerably increased understanding of the human brain’s sensory, motor, cognitive, and affective functions. Parieto-occipital alpha, Rolandic mu and auditory-cortex tau rhythms, as well as more widely spread beta, theta, gamma, delta and ultra-slow oscillations are described. Additionally, MEG/EEG signal changes that accompany different vigilance states, such as drowsiness and sleep, as well as anesthesia, are discussed. We emphasize the importance of timing information that MEG and EEG recordings, including the brain rhythms, provide. In this and subsequent chapters, we rely somewhat on our own studies and experiences, so as to give educational insights from the pitfalls and challenges we ourselves have experienced.
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Diehl, Beate, and Catherine A. Scott. "Physiological Activity and Artefacts in Epileptic Brain in Subdural EEG." In Invasive Studies of the Human Epileptic Brain, edited by Samden D. Lhatoo, Philippe Kahane, and Hans O. Lüders, 84–97. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198714668.003.0007.

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‘Physiological activity and artefacts in epileptic brain in subdural EEG’ reviews intracranial appearances of physiological brain rhythms in each brain region, many of which are also seen on scalp EEG. The alpha rhythm has been described as originating from multiple occipital and extra-occipital cortical generators variously overlapping and influencing each other, probably under the relative control of a central pacemaker. Another more focal pattern has been described in intracranial EEG recordings in the calcarine region, with a third rhythm arising in midtemporal regions, not detectable in scalp EEG, with a frequency in the alpha or theta range. Lambda waves, sleep structures, and mu rhythms over motor cortex can also be detected on subdural electrodes. On a region-by-region basis, intracranial EEG appearances are summarized, including brain oscillations in hippocampus and motor cortex and their modifiers, as well as ongoing rhythms in cingulum. Common sources of physiological and non-physiological artefacts are reviewed.
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Sengupta, Anwesha, Sibsambhu Kar, and Aurobinda Routray. "Study of Loss of Alertness and Driver Fatigue Using Visibility Graph Synchronization." In Innovative Research in Attention Modeling and Computer Vision Applications, 171–93. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-8723-3.ch007.

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Electroencephalogram (EEG) is widely used to predict performance degradation of human subjects due to mental or physical fatigue. Lack of sleep or insufficient quality or quantity of sleep is one of the major reasons of fatigue. Analysis of fatigue due to sleep deprivation using EEG synchronization is a promising field of research. The present chapter analyses advancing levels of fatigue in human drivers in a sleep-deprivation experiment by studying the synchronization between EEG data. A Visibility Graph Similarity-based method has been employed to quantify the synchronization, which has been formulated in terms of a complex network. The change in the parameters of the network has been analyzed to find the variation of connectivity between brain areas and hence to trace the increase in fatigue levels of the subjects. The parameters of the brain network have been compared with those of a complex network with a random degree of connectivity to establish the small-world nature of the brain network.
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Conference papers on the topic "Stationarity of human sleep EEG"

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Sheng, Hu, and YangQuan Chen. "Multifractional Property Analysis of Human Sleep EEG Signals." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47878.

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Electroencephalogram (EEG), the measures and records of the electrical activity of the brain, exhibits evidently nonlinear, non-stationary, 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 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 Holder 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 Ho¨lder exponent H(t) are summarized with tables and figures in the paper. The analysis results show that local Ho¨lder exponent provides a novel and valid tool for dynamic assessment of brain activities in different sleep stages.
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Sadovsky, Petr, Robert Macku, Massimo Macucci, and Giovanni Basso. "Human Sleep EEG Stochastic Artefact Analysis." In NOISE AND FLUCTUATIONS: 20th International Conference on Noice and Fluctuations (ICNF-2009). AIP, 2009. http://dx.doi.org/10.1063/1.3140532.

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Takajyo, A., M. Katayama, K. Inoue, K. Kumamaru, and S. Matsuoka. "Time-Frequency Analysis of Human Sleep EEG." In 2006 SICE-ICASE International Joint Conference. IEEE, 2006. http://dx.doi.org/10.1109/sice.2006.315011.

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Amin, Md Shahedul, Md Riayasat Azim, Tahmid Latif, Md Ashraful Hoque, and Foisal Mahedi Hasan. "Spectral analysis of human sleep EEG signal." In 2010 2nd International Conference on Signal Processing Systems (ICSPS). IEEE, 2010. http://dx.doi.org/10.1109/icsps.2010.5555438.

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"EEG AND ECG CHARACTERISTICS OF HUMAN SLEEP COMPOSITION TYPES." In International Conference on Health Informatics. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003173900970106.

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Li, Duan, Meijing Ni, and Shijun Dun. "Phase-amplitude coupling in human scalp EEG during NREM sleep." In 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2015. http://dx.doi.org/10.1109/bmei.2015.7401504.

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Hasib, Md Musaddaqul, Tapsya Nayak, and Yufei Huang. "A hierarchical LSTM model with attention for modeling EEG non-stationarity for human decision prediction." In 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2018. http://dx.doi.org/10.1109/bhi.2018.8333380.

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"DO MOBILE PHONES AFFECT SLEEP? - Investigating Effects of Mobile Phone Exposure on Human Sleep EEG." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001061505650569.

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Katsuhiro Inoue, Akihiko Takajo, Makoto Maeda, and Shigeaki Matsuoka. "Tuning method of modified wavelet transform in human sleep EEG analysis." In 2007 International Conference on Control, Automation and Systems. IEEE, 2007. http://dx.doi.org/10.1109/iccas.2007.4406842.

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Lofgren, Nils A., Nicholas Outram, and Magnus Thordstein. "EEG entropy estimation using a Markov model of the EEG for sleep stage separation in human neonates." In 2007 3rd International IEEE/EMBS Conference on Neural Engineering. IEEE, 2007. http://dx.doi.org/10.1109/cne.2007.369753.

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