Relevant bibliographies by topics / Human sleep EEG analysis

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

Academic literature on the topic 'Human sleep EEG analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Human sleep EEG analysis"

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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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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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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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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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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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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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Stassen, H. H. "The octave approach to EEG analysis." Methods of Information in Medicine 30, no. 04 (1991): 304–10. http://dx.doi.org/10.1055/s-0038-1634849.

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Abstract:A “tonal” approach to EEG spectral analysis is presented which is compatible with the concept of physical octaves, thus providing a constant resolution of partial tones over the full frequency range inherent to human brain waves, rather than for equidistant frequency steps in the spectral domain. The specific advantages of the tonal approach, however, mainly pay off in the field of EEG sleep analysis where the interesting information is predominantly located in the lower octaves. In such cases the proposed method reveals a fine structure which displays regular maxima possessing typical properties of “overtones” within the three octaves 1-2 Hz, 2-4 Hz and 4-8 Hz. Accordingly, spectral patterns derived from tonal spectral analyses are particularly suited to measure the fine gradations of mutual differences between individual EEG sleep patterns and will therefore allow a more efficient investigation of the genetically determined proportion of sleep EEGs. On the other hand, we also tested the efficiency of tonal spectral analyses on the basis of our 5-year follow-up data of 30 healthy volunteers. It turned out that 28 persons (93.3%) could be uniquely recognized after five years by means of their EEG spectral patterns. Hence, tonal spectral analysis proved to be a powerful tool also in cases where the main EEG information is typically located in the medium octave 8-16 Hz.
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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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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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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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Dissertations / Theses on the topic "Human sleep EEG analysis"

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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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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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Laxminarayan, Parameshvyas. "Exploratory analysis of human sleep data." Worcester, Mass. : Worcester Polytechnic Institute, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0119104-120134/.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: association rule mining; logistic regression; statistical significance of rules; window-based association rule mining; data mining; sleep data. Includes bibliographical references (leaves 166-167).
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Misra, Shivin Satyawon. "A Database For Exploratory Analysis of Human Sleep." Digital WPI, 2008. https://digitalcommons.wpi.edu/etd-theses/181.

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This thesis focuses on the design, development, and exploratory analysis of a human sleep data repository. We have successfully collected comprehensive data for 1,046 sleep disorder patients and created a Terabyte-scale database system to handle it. The data for each patient was collected from the patient's medical records, and from the patient's allnight sleep study (for a total of about 0.6 Gigabytes per patient). Data collected from the patient's medical record contain more than 70 attributes, including demographic data, smoking, drinking, and exercise habits, depression and daytime sleepiness questionnaires, and overall medical history. Data collected from the patient's all-night sleep study consist of 50-55 time-series signals recorded during a period of 6-8 hours at the hospital's sleep clinic. These signals include among others an electroencephalogram, electromyogram, electrooculogram, electrocardiogram, and signals tracking blood oxygen level, body position, limb movements, snoring and blood pressure. 350 additional attributes summarize sleep related events taking place during the night long study, including sleep stages, arousals, and respiratory disturbances. Particular attention during the development of our database system was paid to a database design that effectively handles the data size and complexity, that describes the structure of sleep data in clinically meaningful terms, and that will facilitates the discovery of patterns in sleep data using machine learning algorithms. We have interfaced our database with Weka, a well known data mining system. To the best of our knowledge, our database is one of the world's largest and most comprehensive in the domain of human sleep disorders.
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Wang, Yuehe. "Model based dynamic analysis of human sleep electroencephalogram." Thesis, University of Leicester, 1997. http://hdl.handle.net/2381/30210.

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For sleep classification, automatic electroencephalogram (EEG) interpretation techniques are of interest because they are labour saving, in contrast to manual (visual) methods. More importantly, some automatic methods, which offer a less subjective approach, can provide additional information which it is not possible to obtain by manual analysis. An extensive literature review has been undertaken to investigate the background of automatic EEG analysis techniques. Frequency domain and time domain methods are considered and their limitations are summarised. The weakness in the R & K rules for visual classification and from which most of the automatic systems borrow heavily are discussed. A new technique - model based dynamic analysis - was developed in an attempt to classify the sleep EEG automatically. The technique comprises of two phases, these are the modelling of EEG signals and the analysis of the model's coefficients using dynamic systems theory. Three techniques of modelling EEG signals are compared: the implementation of the non-linear prediction technique of Schaffer and Tidd (1990) based on chaos theory; Kalman filters and a recursive version of a radial basis function for modelling and forecasting the EEG signals during sleep. The Kalman filter approach produced good results and this approach was used in an attempt to classify the EEG automatically. For classifying the model's (Kalman filter's) coefficients, a new technique was developed by a state-space approach. A 'state variable' was defined based on the state changes of the EEG and was shown to be correlated with the depth of sleep. Furthermore it is shown that this technique may be useful for automatic sleep staging. Possible applications include automatic staging of sleep, detection of micro-arousals, anaesthesia monitoring and monitoring the alertness of workers in sensitive or potentially dangerous environments.
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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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Congedo, Marco. "EEG Source Analysis." Habilitation à diriger des recherches, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00880483.

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Electroencephalographic data recorded on the human scalp can be modeled as a linear mixture of underlying dipolar source generators. The characterization of such generators is the aim of several families of signal processing methods. In this HDR we consider in several details three of such families, namely 1) EEG distributed inverse solutions, 2) diagonalization methods, including spatial filtering and blind source separation and 3) Riemannian geometry. We highlight our contributions in each of this family, we describe algorithms reporting all necessary information to make purposeful use of these methods and we give numerous examples with real data pertaining to our published studies. Traditionally only the single-subject scenario is considered; here we consider in addition the extension of some methods to the simultaneous multi-subject recording scenario. This HDR can be seen as an handbook for EEG source analysis. It will be particularly useful to students and other colleagues approaching the field.
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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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Ježek, Martin. "Analýza spánkového signálu EEG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217961.

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Cílem této práce byl vývoj programu pro automatickou detekci arousalu v signálu spánkového EEG s použitím metod časově-frekvenční analýzy. Předmětem studie bylo 13 celonočních polysomnografických nahrávek (čtyři svody EEG, EMG, EKG a EOG), tj. celkově více než 100 hodin záznamu. Jednalo se o část dat z dřívějších výzkumných prací expertní lékařky v problematice spánku Dr. Emilie Sforzy, Ženeva, Švýcarsko, která rovněž poskytla základní hodnocení těchto dat. V záznamech bylo celkem označeno 1551 arousal událostí. Pro usnadnění výběru konkrétní metody časově-frekvenční analýzy byla následně vytvořena sada nástrojů pro vizualizaci jednotlivých signálů a jejich různých časově-frekvenčních vyjádření. S ohledem na závěry vizuální analýzy, charakter signálu EEG a efektivitu výpočetních metod byla pro analýzu vybrána waveletová transformace s mateřskou vlnkou Daubechies řádu 6. Jednotlivé svody EEG byly dekomponovány do šesti frekvenčních pásem. Z takto odvozených signálů a signálu EMG byly následně stanoveny ukazatele možné přítomnosti události arousalu. Tyto ukazatele byly dále váhovány lineárním klasifikátorem, jehož hodnoty vah byly optimalizovány pomocí genetického algoritmu. Na základě hodnoty lineárního klasifikátoru bylo rozhodnuto o přítomnosti události arousalu v daném svodě EEG – arousal byl detekován, jestliže hodnota klasifikátoru překročila danou mez na dobu více než 3 a méně než 30 vteřin. V celém záznamu pak byl arousal označen, byl-li detekován alespoň v jednom ze svodů EEG. Následně byly odvozeny míry senzitivity a selektivity detekce, jež byly rovněž základem pro stanovení fitness funkce genetického algoritmu. Pro učení genetického algoritmu byly vybrány první čtyři záznamy. Na základě takto optimalizovaných vah vznikl program pro automatickou detekci, který na celém souboru 13 záznamů dosáhl ve srovnání s expertním hodnocením míry senzitivity 76,09%, selektivity 53,26% a specificity 97,66%.
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Books on the topic "Human sleep EEG analysis"

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Vanhatalo, Sampsa, and J. Matias Palva. Infraslow EEG Activity. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0032.

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Infraslow electroencephalographic (EEG) activity refers to frequencies below the conventional clinical EEG range that starts at about 0.5 Hz. Evidence suggests that salient EEG signals in the infraslow range are essential parts of many physiological and pathological conditions. In addition, brain is known to exhibit multitude of infraslow processes, which may be observed directly as fluctuations in the EEG signal amplitude, as infraslow fluctuations or intermittency in other neurophysiological signals, or as fluctuations in behavioural performance. Both physiological and pathological EEG activity may range from 0.01 Hz to several hundred Hz. In the clinical context, infraslow activity is commonly observed in the neonatal EEG, during and prior to epileptic seizures, and during sleep and arousals. Laboratory studies have demonstrated the presence of spontaneous infraslow EEG fluctuations or very slow event-related potentials in awake and sleeping subjects. Infraslow activity may not only arise in cortical and subcortical networks but is also likely to involve non-neuronal generators such as glial networks. The full, physiologically relevant range of brain mechanisms can be readily recorded with wide dynamic range direct-current (DC)-coupled amplifiers or full-band EEG (FbEEG). Due to the different underlying mechanisms, a single FbEEG recording can even be perceived as a multimodal recording where distinct brain modalities can be studied simultaneously by performing data analysis for different frequency ranges. FbEEG is likely to become the standard approach for a wide range of applications in both basic science and in the clinic.
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Hari, MD, PhD, Riitta, and Aina Puce, PhD. MEG-EEG Primer. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190497774.001.0001.

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This book provides newcomers and more experienced researchers with the very basics of magnetoencephalography (MEG) and electroencephalography (EEG)—two noninvasive methods that can inform about the neurodynamics of the human brain on a millisecond scale. These two closely related methods are addressed side by side, starting from their physical and physiological bases and then advancing to methods of data acquisition, analysis, visualization, and interpretation. Special attention is paid to careful experimentation, guiding the readers to differentiate brain signals from various biological and non-biological artifacts and to ascertain that the collected data are reliable. The strengths and weaknesses of MEG and EEG are presented relative to each other and to other available brain-imaging methods. Necessary instrumentation and laboratory set-ups, as well as potential pitfalls in data collection and analysis are discussed. Spontaneous brain rhythms and evoked responses to sensory and multisensory stimulation are covered and examined both in healthy individuals and in various brain disorders, such as epilepsy. MEG/EEG signals related to motor, cognitive, and social events are discussed as well. The integration of MEG and EEG information with other methods to assess human brain function is discussed with respect to the current state-of-the art in the field. The book ends with a look to future developments in equipment design, and experimentation, emphasizing the role of accurate temporal information for human brain function.
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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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Sutter, Raoul, Peter W. Kaplan, and Donald L. Schomer. Historical Aspects of Electroencephalography. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0001.

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Electroencephalography (EEG), a dynamic real-time recording of electrical neocortical brain activity, began in the 1600s with the discovery of electrical phenomena and the concept of an “action current.” The galvanometer was introduced in the 1800s and the first bioelectrical observations of human brain signals were made in the 1900s. Certain EEG patterns were associated with brain disorders, increasing the clinical and scientific use of EEG. In the 1980s, technical advances allowed EEGs to be digitized and linked with videotape recording. In the 1990s, digital data storage increased and computer networking enabled remote real-time EEG reading, which made possible continuous EEG (cEEG) monitoring. Manual cEEG analysis became increasingly labor-intensive, calling for methods to assist this process. In the 2000s, complex algorithms enabling quantitative EEG analyses were introduced, with a new focus on shared activity between rhythms, including phase and magnitude synchrony. The automation of spectral analysis enabled studies of spectral content.
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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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Weil, Andrew. Integrative Environmental Medicine. Edited by Aly Cohen and Frederick S. vom Saal. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190490911.001.0001.

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Integrative Environmental Medicine looks at the history and changing landscape of environmental issues in the United States, including water supply, air quality, extensive plastic pollution, harmful chemicals in cleaning and personal care products, radiofrequency radiation, food additives, pesticides, and medications. The unique properties of compounds such as endocrine-disrupting chemicals are explored along with their ability to disturb the body’s normal signaling pathways, genetic profile, and gut microbiome. Resulting molecular derangements promote thyroid and other autoimmune diseases, diabetes, cardiovascular disease, cancer, and influence developmental problems in children. Analysis of current research identifies ways to reduce exposures and health risks, improve regulations and appropriate testing for chemicals, remediate environmental pollution, and design healthier products for the future. Best practices are considered for clinicians to ascertain exposure history, test for toxins, and teach patients how to avoid harmful exposures. Patients will be prepared and empowered with information about healthier food choices and cooking practices, appropriate supplement use, water filtration, cleaning and personal care product selection, improved sleep, stress reduction, sauna, fasting, chelation, safe cell phone use, and other means of reducing harmful environmental exposures. Solutions at every level require interdisciplinary collaboration to advance assessment, design, stewardship, and regulation of chemicals to promote environmental and human health.
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Book chapters on the topic "Human sleep EEG analysis"

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Grass, P., and H. Fruhstorfer. "EEG Sleep Pattern Recognition by Cluster Analysis." In Medical Informatics Europe 85, 777. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-93295-3_151.

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Farag, Amr F., Shereen M. El-Metwally, and Ahmed Abdel Aal Morsy. "Automated Sleep Staging Using Detrended Fluctuation Analysis of Sleep EEG." In Soft Computing Applications, 501–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33941-7_44.

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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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Cohen, Arnon, Felix Flomen, and Nir Drori. "EEG Sleep Staging Using Vectorial Autoregressive Models." In Advances in Processing and Pattern Analysis of Biological Signals, 45–55. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-9098-6_4.

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Roberts, Stephen, and Lionel Tarassenko. "Automated Sleep EEg Analysis using an RBF Network." In Applications of Neural Networks, 305–20. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-2379-3_13.

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Kohlmorgen, J., K. R. Müller, J. Rittweger, and K. Pawelzik. "Analysis of wake/sleep EEG with competing experts." In Lecture Notes in Computer Science, 1077–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0020296.

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Chen, Chao, Xuequan Zhu, Abdelkader Nasreddine Belkacem, Lin Lu, Long Hao, Jia You, Duk Shin, Wenjun Tan, Zhaoyang Huang, and Dong Ming. "Automatic Sleep Spindle Detection and Analysis in Patients with Sleep Disorders." In Human Brain and Artificial Intelligence, 113–24. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1288-6_8.

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Singh, Shatakshi, Ashutosh Pradhan, Koushik Bakshi, Bablu Tiwari, Dimple Dawar, Mahesh Kate, Jeyaraj Pandian, C. S. Kumar, and Manjunatha Mahadevappa. "Monitoring Post-stroke Motor Rehabilitation Using EEG Analysis." In Intelligent Human Computer Interaction, 13–22. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44689-5_2.

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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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Conference papers on the topic "Human sleep EEG analysis"

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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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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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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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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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Song, I. H., Y. S. Ji, B. K. Cho, J. H. Ku, Y. J. Chee, J. S. Lee, S. M. Lee, I. Y. Kim, and Sun I. Kim. "Multifractal Analysis of Sleep EEG Dynamics in Humans." In 2007 3rd International IEEE/EMBS Conference on Neural Engineering. IEEE, 2007. http://dx.doi.org/10.1109/cne.2007.369730.

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Makoto Maeda, Akihiko Takajyo, Katsuhiro Inoue, Kousuke Kumamaru, and Shigeki Matsuoka. "Time-frequency analysis of human sleep EEG and its application to feature extraction about biological rhythm." In SICE Annual Conference 2007. IEEE, 2007. http://dx.doi.org/10.1109/sice.2007.4421304.

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Muñoz, David. "New strategies in proprioception’s analysis for newer theories about sensorimotor control." In Systems & Design 2017. Valencia: Universitat Politècnica València, 2017. http://dx.doi.org/10.4995/sd2017.2017.6903.

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Abstract Human’s motion and its mechanisms had become interesting in the last years, where the medecine’s field search for rehabilitation methods for handicapped persons. Other fields, like sport sciences, professional or military world, search to distinguish profiles and ways to train them with specific purposes. Besides, recent findings in neuroscience try to describe these mechanisms from an organic point of view. Until now, different researchs had given a model about control motor that describes how the union between the senses’s information allows adaptable movements. One of this sense is the proprioception, the sense which has a quite big factor in the orientation and position of the body, its members and joints. For this reason, research for new strategies to explore proprioception and improve the theories of human motion could be done by three different vias. At first, the sense is analysed in a case-study where three groups of persons are compared in a controlled enviroment with three experimental tasks. The subjects belong to each group by the kind of sport they do: sedentary, normal sportsmen (e.g. athletics, swimming) and martial sportmen (e.g. karate, judo). They are compared thinking about the following hypothesis: “Martial Sportmen have a better proprioception than of the other groups’s subjects: It could be due to the type of exercises they do in their sports as empirically, a contact sportsman shows significantly superior motor skills to the members of the other two groups. The second via are records from encephalogram (EEG) while the experimental tasks are doing. These records are analised a posteriori with a set of processing algorithms to extract characteristics about brain’s activity of the proprioception and motion control. Finally , the study tries to integrate graphic tools to make easy to understand final scientific results which allow us to explore the brain activity of the subjects through easy interfaces (e.g. space-time events, activity intensity, connectivity, specific neural netwoks or anormal activity). In the future, this application could be a complement to assist doctors, researchers, sports center specialists and anyone who must improve the health and movements of handicapped persons. Keywords: proprioception, EEG, assesment, rehabilitation.References: Röijezon, U., Clark, N.C., Treleaven, J. (2015). Proprioception in musculoskeletal rehabilitation. Part 1: Basic science and principles of assessment and clinical interventions. ManualTher.10.1016/j.math.2015.01.008. Röijezon, U., Clark, N.C., Treleaven, J. (2015). Proprioception in musculoskeletal rehabilitation. Part 2: Clinical assessment and intervention. Manual Ther.10.1016/j.math.2015.01.009. Roren, A., Mayoux-Benhamou, M.A., Fayad, F., Poiraudeau, S., Lantz, D., Revel, M. (2008). Comparison of visual and ultrasound based techniques to measure head repositioning in healthy and neck-pain subjects. Manual Ther. 10.1016/j.math.2008.03.002. Hillier, S., Immink, M., Thewlis, D. (2015). Assessing Proprioception: A Systematic Review of Possibilities. Neurorehab. Neural Repair. 29(10) 933–949. Hooper, T.L., James, C.R., Brismée, J.M., Rogers, T.J., Gilbert, K.K., Browne, K.L, Sizer, P.S. (2016). Dynamic Balance as Measured by the Y-Balance Test Is Reduced in Individuals with low Back Pain: A Cross-Sectional Comparative Study. Phys. Ther. Sport,10.1016/j.ptsp.2016.04.006. Zemková, G., Stefániková, G., Muyor, J.M. (2016). Load release balance test under unstable conditions effectivelydiscriminates between physically active and sedentary young adults. Glave, A.P., Didier, J.J., Weatherwax, J., Browning, S.J., Fiaud, Vanessa. (2014). Testing Postural Stability: Are the Star Excursion Balance Test and Biodex Balance System Limits of Stability Tests Consistent? Gait Posture. 43(2016) 225-227. Han, Jian., Waddington, G., Adams, R., Anson, J., Liu, Y. (2014). Assessing proprioception: A critical review of methods. J. Sport Health Sci.10.1016/j.jshs.2014.10.004. Hosp, S., Bottoni, G., Heinrich, D., Kofler, P., Hasler, M., Nachbauer, W. (2014). A pilot study of the effect of Kinesiology tape on knee proprioception after physical activity in healthy women. J. Sci. Med. Sport. 18 (2015) 709-713. Mima, T., Terada, K., Ikeda, A., Fukuyama, H., Takigawa, T., Kimura, J., Shibasaki, H. (1996). Afferent mechanism of cortical myoclonus studied by proprioception-related SEPs. Clin. Neurophysiol. 104 (1997) 51-59. Myers, J.B., Lephart, S.M. (2000). The Role of the Sensorimotor System in the Athletic Shoulder. J. Athl.Training.35 (3) 351-363. Rossi, S., della Volpe, R., Ginannesch, F., Ulivelli, M., Bartalini, S., Spidalieri, R., Rossi, A. (2003). Early somatosensory processing during tonic muscle pain in humans: relation to loss of proprioception and motor 'defensive' strategies. Clin. Neurophysiol. 10.1016/S1388-2457(03)00073-7. Chaudhary, U., Birbaumer, N., Curado, M.R. (2014). Brain-Machine Interface (BMI) in paralysis. Ann. Phys. Rehabil. Med.10.1016/j.rehab.2014.11.002. Delorme, A., Makeig, S. (2003). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Meth.10.1016/j.jneumeth.2003.10.009. Morup, M., Hansen, L.K., Arnfred, S.M. (2006). ERPWAVELAB: A toolbox for multi-channel analysis of time-frequency transformed event related potentials. J. Neurosci. Meth.10.1016/j.jneumeth.2003.11.008. Kaminski, M., Blinowska, K., Szelenberger, W. (1996). Topographic analysis of coherence and propagation of EEG activity during sleep and wakefulness. Clin. Neurophysiol. 102 (1997) 216-227. Korzeniewska, A., Manczak, M., Kaminski, M., Blinowska, K.J., Kasicki, S. (2003). Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method. J. Neurosci. Meth.10.1016/S0165-0270(03)00052-9. Morup, M., Hansen, L.K., Parnas, J., Arnfred, S.M. (2005). Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG. Neuroimage. 10.1016/j.neuroimage.2005.08.005. Barwick, F., Arnett, P., Slobounov, S. (2011). EEG correlates of fatigue during administration of a neuropsychological test battery. Clin. Neurophysiol. 10.1016/j.clinph.2011.06.027. Osuagwu, B.A., Vuckovic, A. (2014). Similarities between explicit and implicit motor imagery in mental rotation of hands: An EEG study. Neuropsycholgia. Buzsáki, G. (2006). Rhythms of the brain. Ed. Oxford. USA. Trappenberg, T.P. (2010). Fundamentals of Computational Neuroscience. Ed. Oxford. UK. Koessler, L., Maillard, L., Benhadid, A., Vignal, J.P., Felblinger, J., Vespignani, H., Braun, M. (2009). Automated cortical projection of EEG: Anatomical correlation via the international 10-10 system. Neuroimage. 10.1016/j.neuroimage.2009.02.006. Jurcak, V., Tsuzuki, Daisuke., Dan, I. (2007). 10/20, 10/10, and 10/5 systems revisited: Their validity as relativehead-surface-based positioning systems. Neuroimage. 10.1016/j.neuroimage.2006.09.024. Chuang, L.Y., Huang, C.J., Hung, T.M. (2013). The differences in frontal midline theta power between successful and unsuccessful basketball free throws of elite basketball players. Int. J. Psychophysiology.10.1016/j.ijpsycho.2013.10.002. Wang, C.H., Tsai, C.L., Tu, K.C., Muggleton, N.G., Juan, C.H., Liang, W.K. (2014). Modulation of brain oscillations during fundamental visuo-spatialprocessing: A comparison between female collegiate badmintonplayers and sedentary controls. Psychol. Sport Exerc. 10.1016/j.psychsport.2014.10.003. Proverbio, A.L., Crotti, N., Manfredi, Mirella., Adomi, R., Zani, A. (2012). Who needs a referee? How incorrect basketball actions are automatically detected by basketball players’ brain. Sci Rep-UK. 10.1038/srep00883. Cheng, M.Y., Hung, C.L., Huang, C.J., Chang, Y.K., Lo, L.C., Shen, C., Hung, T.M. (2015). Expert-novice differences in SMR activity during dart throwing. Biol. Psychol.10.1016/j.biopsycho.2015.08.003. Ring, C., Cooke, A., Kavussanu, M., McIntyre, D., Masters, R. (2014). Investigating the efficacy of neurofeedback training for expeditingexpertise and excellence in sport. Psychol. SportExerc. 10.1016/j.psychsport.2014.08.005. Park, J.L., Fairweather, M.M., Donaldson, D.I. (2015). Making the case for mobile cognition: EEG and sports performance. Neurosci. Biobehav. R. 10.1016/j.neubiorev.2015.02.014. Babiloni, C., Marzano, N., Infarinato, F., Iacoboni, M., Rizza, G. (2009). Neural efficency of experts’ brain during judgement of actions: A high -resolution EEG study in elite and amateur karate athletes. Behav. Brain. Res. 10.1016/j.bbr.2009.10.034. Jain, S., Gourab, K., Schindler-Ivens, S., Schmit, B.D. (2012). EEG during peddling: Evidence for cortical control of locomotor tasks. Clin. Neurophysiol.10.1016/j.clinph.2012.08.021. Behmer Jr., L.P., Fournier, L.R. (2013). Working memory modulates neural efficiency over motor components during a novel action planning task: An EEG study. Behav. Brain. Res. 10.1016/j.bbr.2013.11.031.
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Li, Ling, and Ruiping Wang. "Complexity Analysis of Sleep EEG Signal." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5515699.

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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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