Relevant bibliographies by topics / Spectral analysis of human sleep EEG

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  1. Journal articles
  2. Dissertations / Theses
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Academic literature on the topic 'Spectral analysis of human sleep EEG'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Spectral analysis of human sleep EEG"

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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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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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Friess, E., H. Tagaya, L. Trachsel, F. Holsboer, and R. Rupprecht. "Progesterone-induced changes in sleep in male subjects." American Journal of Physiology-Endocrinology and Metabolism 272, no. 5 (May 1, 1997): E885—E891. http://dx.doi.org/10.1152/ajpendo.1997.272.5.e885.

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Progesterone administration induces a reduction of the vigilance state in humans during wakefulness. It has been been suggested that this effect is mediated via neuroactive metabolites that interact with the gamma-aminobutyric, acidA (GABAA) receptor complex. To investigate the effects of progesterone administration on the sleep electroencephalogram (EEG) in humans we made polysomnographic recordings, including sleep stage-specific spectral analysis, and concomitantly measured plasma concentrations of progesterone and its GABA-active metabolites 3 alpha-hydroxy-5 alpha-dihydroprogesterone (allopregnanolone) and 3 alpha-hydroxy-5 beta-dihydroprogesterone (pregnanolone) in nine healthy male subjects in a double-blind placebo-controlled crossover study. Progesterone administration at 9:30 PM induced a significant increase in the amount of non-rapid eye movement (REM) sleep. The EEG spectral power during non-REM sleep showed a significant decrease in the slow wave frequency range (0.4-4.3 Hz), whereas the spectral power in the higher frequency range (> 15 Hz) tended to be elevated. Some of the observed changes in sleep architecture and sleep-EEG power spectra are similar to those induced by agonistic modulators of the GABAA receptor complex and appear to be mediated in part via the conversion of progesterone into its GABA-active metabolites.
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Mukai, Junko, Sunao Uchida, Shinichi Miyazaki, Kyoko Nishihara, and Yutaka Honda. "Spectral analysis of all-night human sleep EEG in narcoleptic patients and normal subjects." Journal of Sleep Research 12, no. 1 (March 2003): 63–71. http://dx.doi.org/10.1046/j.1365-2869.2003.00331.x.

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González, Julián, Antoni Gamundi, Rubén Rial, M. Cristina Nicolau, Luis de Vera, and Ernesto Pereda. "Nonlinear, fractal, and spectral analysis of the EEG of lizard, Gallotia galloti." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 277, no. 1 (July 1, 1999): R86—R93. http://dx.doi.org/10.1152/ajpregu.1999.277.1.r86.

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Electroencephalogram (EEG) from dorsal cortex of lizard Gallotia galloti was analyzed at different temperatures to test the presence of fractal or nonlinear structure during open (OE) and closed eyes (CE), with the aim of comparing these results with those reported for human slow-wave sleep (SWS). Two nonlinear parameters characterizing EEG complexity [correlation dimension (D2)] and predictability [largest Lyapunov exponent (λ1)] were calculated, and EEG spectrum and fractal exponent β were determined via coarse graining spectral analysis. At 25°C, evidence of nonlinear structure was obtained by the surrogate data test, with EEG phase space structure suggesting the presence of deterministic chaos (D2 ∼6, λ1 ∼1.5). Both nonlinear parameters were greater in OE than in CE and for the right hemisphere in both situations. At 35°C the evidence of nonlinearity was not conclusive and differences between states disappeared, whereas interhemispheric differences remained for λ1. Harmonic power always increased with temperature within the band 8–30 Hz, but only with OE within the band 0.3–7.5 Hz. Qualitative similarities found between lizard and human SWS EEG support the hypothesis that reptilian waking could evolve into mammalian SWS.
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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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Uchida, Sunao, Irwin Feinberg, Jonathan D. March, Yoshikata Atsumi, and Tom Maloney. "A Comparison of Period Amplitude Analysis and FFT Power Spectral Analysis of All-Night Human Sleep EEG." Physiology & Behavior 67, no. 1 (August 1999): 121–31. http://dx.doi.org/10.1016/s0031-9384(99)00049-9.

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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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Olbrich, Eckehard, Thomas Rusterholz, Monique K. LeBourgeois, and Peter Achermann. "Developmental Changes in Sleep Oscillations during Early Childhood." Neural Plasticity 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/6160959.

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Although quantitative analysis of the sleep electroencephalogram (EEG) has uncovered important aspects of brain activity during sleep in adolescents and adults, similar findings from preschool-age children remain scarce. This study utilized our time-frequency method to examine sleep oscillations as characteristic features of human sleep EEG. Data were collected from a longitudinal sample of young children (n=8; 3 males) at ages 2, 3, and 5 years. Following sleep stage scoring, we detected and characterized oscillatory events across age and examined how their features corresponded to spectral changes in the sleep EEG. Results indicated a developmental decrease in the incidence of delta and theta oscillations. Spindle oscillations, however, were almost absent at 2 years but pronounced at 5 years. All oscillatory event changes were stronger during light sleep than slow-wave sleep. Large interindividual differences in sleep oscillations and their characteristics (e.g., “ultrafast” spindle-like oscillations, theta oscillation incidence/frequency) also existed. Changes in delta and spindle oscillations across early childhood may indicate early maturation of the thalamocortical system. Our analytic approach holds promise for revealing novel types of sleep oscillatory events that are specific to periods of rapid normal development across the lifespan and during other times of aberrant changes in neurobehavioral function.
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Dijk, D. J., D. P. Brunner, and A. A. Borbely. "Time course of EEG power density during long sleep in humans." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 258, no. 3 (March 1, 1990): R650—R661. http://dx.doi.org/10.1152/ajpregu.1990.258.3.r650.

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In nine subjects sleep was recorded under base-line conditions with a habitual bedtime (prior wakefulness 16 h; lights off at 2300 h) and during recovery from sleep deprivation with a phase-advanced bedtime (prior wakefulness 36 h; lights off at 1900 h). The duration of phase-advanced recovery sleep was greater than 12 h in all subjects. Spectral analysis of the sleep electroencephalogram (EEG) revealed that slow-wave activity (SWA; 0.75-4.5 Hz) in non-rapid-eye-movement (NREM) sleep was significantly enhanced during the first two NREM-REM sleep cycles of displaced recovery sleep. The sleep stages 3 and 4 (slow-wave sleep) and SWA decreased monotonically over the first three and four NREM-REM cycles of, respectively, base-line and recovery sleep. The time course of SWA in base-line and recovery sleep could be adequately described by an exponentially declining function with a horizontal asymptote. The results are in accordance with the two-process model of sleep regulation in which it is assumed that SWA rises as a function of the duration of prior wakefulness and decreases exponentially as a function of prior sleep. We conclude that the present data do not provide evidence for a 12.5-h sleep-dependent rhythm of deep NREM sleep.
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Dissertations / Theses on the topic "Spectral analysis 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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Himes, Benjamin John. "Development and Analysis of a Vibration Based Sleep Improvement Device." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/9168.

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Many research studies have analyzed the effect that whole-body vibration (WBV) has on sleep, and some have sought to use vibration to treat sleep disorders such as insomnia. It has been shown that low frequencies (f < 2Hz) are generally sleep inducing, but oscillations of this frequency are typically difficult to achieve using electromagnetic vibration drives. In the research that has been performed, optimal vibration parameters have not been determined, and the effects of multiple vibration sources vibrating at different frequencies to induce a low frequency traveling wave have not been explored. Insomnia affects millions of people worldwide, and non-pharmacological treatment options are limited. A bed excited with multiple vibration sources was used to explore beat frequency vibration as a non-pharmacological treatment for insomnia. A repeated measures design pilot study of 14 participants with mild-moderate insomnia symptom severity was conducted to determine the effects of beat frequency vibration, and traditional standing wave vibration on sleep latency and quality. Participants were monitored using high-density electroencephalography (HD-EEG). Sleep latency was compared between treatment conditions. Trends of a decrease in sleep latency due to beat frequency vibration were found (p ≤ 0.181 for AASM latency, and p ≤ 0.068 for unequivocal sleep latency). Neural complexity during wake, N1, and N2 stages were compared using Multi-Scale Sample Entropy (MSE), which demonstrated significantly lower MSE between wake and N2 stages (p ≤ 0.002). Lower MSE was found in the transition from wake to N1 stage sleep but did not reach significance (p ≤ 0.300). During N2 sleep, beat frequency vibration shows lower MSE than the control session in the left frontoparietal region. This indicates that beat frequency vibration may lead to a decrease of conscious awareness during deeper stages of sleep. Standing wave vibration caused reduced Alpha activity and increased Delta activity during wake. Beat frequency vibration caused increased Delta activity during N2 sleep. These preliminary results suggest that beat frequency vibration may help individuals with insomnia symptoms by decreasing sleep latency, by reducing their conscious awareness, and by increasing sleep drive expression during deeper stages of sleep. Standing wave vibration may be beneficial for decreasing expression of arousal and increasing expression of sleep drive during wake, implying that a dynamic vibration treatment may be beneficial. The application of vibration treatment as part of a heuristic sleep model is discussed.
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Yeung, Wai. "Sleep, pain and daytime functioning in patients with fibromyalgia syndrome and osteoarthritis : a cross-sectional comparative study." Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/21798.

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Fibromyalgia syndrome (FMS) is a disorder characterised by chronic widespread pain, non-restorative sleep, fatigue and daytime dysfunction. Occurring in 2-5% of the population, the aetiology is largely unknown. Sleep dysfunction occurs in over 90% of FMS patients. While research has shown that both the macrostructure and microstructure of sleep may be altered, there remain inconsistencies in the polysomnographic (PSG) findings, and wide variations in methodological approaches. Few studies have controlled for symptom duration or the time elapsed between diagnosis and PSG sleep assessments. In addition, while psychometric analyses have suggested a distinctive FMS psychological profile (which includes higher levels of depressive symptoms, anxiety and fatigue) few studies have simultaneously, and thoroughly examined sleep and psychological status in the same participants. A frequently reported alteration found in the sleep microstructure of FMS patients is the alpha-delta sleep anomaly, characterised by an increase in alpha wave activity during slow wave sleep. Originally considered a possible neurological contribution to FMS, whether the alpha-delta sleep anomaly is fundamental to the development of fibromyalgia syndrome, or results mainly from the pain experience of FMS patients remains unknown. No previous study has directly compared the sleep of FMS and other (non-FMS) patients experiencing similar levels of chronic pain and sleep dysfunction. The present study was designed to examine sleep macrostructure and microstructure in FMS patients, and evaluate the role of the alpha-delta sleep anomaly as either a possible contributor to fibromyalgia syndrome, or a likely consequence of pain experience. In order to explore these relationships, detailed sleep, activity and psychological profiles were compared in 3 groups: 1) FMS patients (n = 19); 2) osteoarthritis patients with sleep disturbance (n = 17); and non-clinical (normal healthy) adults (n = 10). In order to standardise diagnostic reliability and symptom chronicity, the FMS group was recruited from a single rheumatology facility immediately following diagnosis. Guided by a series of formal research questions, analyses compared sleep macrostructure (using American Academy of Sleep Medicine criteria), sleep microstructure (using spectral analysis), and a range of psychological variables (including pain experience, sleepiness, fatigue, depression, anxiety, perceived social support, health locus of control, pain catastrophizing and personality). The results indicated that the alpha-delta sleep anomaly is not unique to FMS, but appears to be a feature found in the sleep of normal healthy adults and (to a greater extent) those with FMS and osteoarthritis. The incidence of the anomaly was statistically similar in both clinical (FMS and osteoarthritis) groups, a pattern consistent of its being a secondary feature of pain, rather than a primary abnormality of FMS. Overall, the psychometric assessments of state and trait anxiety and depression better discriminated between the three groups than did the sleep variables. Nevertheless, on measures of sleep, perceived social support, health locus of control, and pain catastrophizing, FMS and osteoarthritis patients were not significantly different, though both clinical groups differed on these variables from healthy controls.
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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
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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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"EEG-Based Estimation of Human Reaction Time Corresponding to Change of Visual Event." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.55526.

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abstract: The human brain controls a person's actions and reactions. In this study, the main objective is to quantify reaction time towards a change of visual event and figuring out the inherent relationship between response time and corresponding brain activities. Furthermore, which parts of the human brain are responsible for the reaction time is also of interest. As electroencephalogram (EEG) signals are proportional to the change of brain functionalities with time, EEG signals from different locations of the brain are used as indicators of brain activities. As the different channels are from different parts of our brain, identifying most relevant channels can provide the idea of responsible brain locations. In this study, response time is estimated using EEG signal features from time, frequency and time-frequency domain. Regression-based estimation using the full data-set results in RMSE (Root Mean Square Error) of 99.5 milliseconds and a correlation value of 0.57. However, the addition of non-EEG features with the existing features gives RMSE of 101.7 ms and a correlation value of 0.58. Using the same analysis with a custom data-set provides RMSE of 135.7 milliseconds and a correlation value of 0.69. Classification-based estimation provides 79% & 72% of accuracy for binary and 3-class classication respectively. Classification of extremes (high-low) results in 95% of accuracy. Combining recursive feature elimination, tree-based feature importance, and mutual feature information method, important channels, and features are isolated based on the best result. As human response time is not solely dependent on brain activities, it requires additional information about the subject to improve the reaction time estimation.
Dissertation/Thesis
Masters Thesis Electrical Engineering 2019
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Books on the topic "Spectral analysis of human sleep EEG"

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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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Book chapters on the topic "Spectral analysis of human sleep EEG"

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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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ANGELERI, F., G. NOLFE, E. PUCCI, N. BELARDINELLI, A. QUATTRINI, and M. SIGNORINO. "EEG Spectral Analysis in Generalised Primary Epilepsies (Awake, S2 Sleep, Spindles and K-complexes)." In Thalamic Networks for Relay and Modulation, 409–24. Elsevier, 1993. http://dx.doi.org/10.1016/b978-0-08-042274-9.50041-9.

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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 "Spectral analysis of human sleep EEG"

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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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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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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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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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Ong, Zhi Ying, A. Saidatul, and Z. Ibrahim. "Power Spectral Density Analysis for Human EEG-based Biometric Identification." In 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA). IEEE, 2018. http://dx.doi.org/10.1109/icassda.2018.8477604.

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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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Malinowska, Urszula, Jakub Wojciechowski, Marek Waligora, Andrzej Wrobel, Pawel Niedbalski, and Jacek Rogala. "Spectral analysis versus signal complexity methods for assessing attention related activity in human EEG*." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856798.

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Ghorbanian, Parham, Subramanian Ramakrishnan, Adam J. Simon, and Hashem Ashrafiuon. "Stochastic Dynamic Modeling of the Human Brain EEG Signal." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3881.

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Abstract:
The occurrence and risk of recurrence of brain related injuries and diseases are difficult to characterize due to various factors including inter-individual variability. A useful approach is to analyze the brain electroencephalogram (EEG) for differences in brain frequency bands in the signals obtained from potentially injured and healthy normal subjects. However, significant shortcomings include: (1) contrary to empirical evidence, current spectral signal analysis based methods often assume that the EEG signal is linear and stationary; (2) nonlinear time series analysis methods are mostly numerical and do not possess any predictive features. In this work, we develop models based on stochastic differential equations that can output signals with similar frequency and magnitude characteristics of the brain EEG. Initially, a coupled linear oscillator model with a large number of degrees of freedom is developed and shown to capture the characteristics of the EEG signal in the major brain frequency bands. Then, a nonlinear stochastic model based on the Duffing oscillator with far fewer degrees of freedom is developed and shown to produce outputs that can closely match the EEG signal. It is shown that such a compact nonlinear model can provide better insight into EEG dynamics through only few parameters, which is a step towards developing a framework with predictive capabilities for addressing brain injuries.
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Emri, Zsuzsa, Károly Antal, Georgina Csordás, Csilla Kvaszingerné Prantner, and Réka Kissné Zsámboki. "EEG mérés pedagógiai alkalmazási lehetőségei." In Agria Média 2020 : „Az oktatás digitális átállása korunk pedagógiai forradalma”. Eszterházy Károly Egyetem Líceum Kiadó, 2021. http://dx.doi.org/10.17048/am.2020.271.

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A hordozható, könnyen használható elektroencefalográfok (EEG) segítségével a kognitív folyamatokat úgy vizsgálhatjuk, hogy a természetes viselkedést alig befolyásoljuk. A hajas fejbőrön mért idegi aktivitás mikrovoltos nagyságrendű, emiatt az izomaktivitásból, szemmozgásból származó műtermékektől meg kell tisztítani. Ezeknek a műtermékeknek a tökéletes eltávolítására még nincs megfelelő megoldás, emiatt az EEG-t főleg olyan feladatokhoz érdemes használni, amelyek nem járnak sok izomaktivitással. E kötöttség ellenére a pedagógia számos területén sikeresen alkalmazták már az EEG-t. A különböző feladatok alatt mért agyi aktivitásokból a feladatok alatti figyelemre, kognitív terhelésre és a feladatmegoldási stratégiákra tudtak következtetni. Sajátos nevelési igényű gyerekeknél neurofeedback módszerrel segítik az önreguláció és a megfelelő viselkedés kialakítását. Különböző személyek agyi aktivitásának szinkronizációja pedig a szociális közelséggel és az információ átadás sikerével van kapcsolatban. Ezek az alkalmazások az EEG spektrális jellemzőinek követésén alapulnak. Fourier transzformációval az EEG jelet különböző frekvenciájú összetevőire bontják, és meghatározzák az egyes frekvenciák arányát a teljes aktivitásban. A humán hajas fejbőrről az α tartomány nagy biztonsággal regisztrálható. Habár ezt a hullámot az inaktivitással hozzák kapcsolatba leginkább, az irányított figyelem kialakításában is kulcsfontosságú szerepe van, a feladat szempontjából zavaró információt α aktivitás segítségével nyomjuk el. Kísérleteimben a különböző feladatok alatt az egyes elektródákkal regisztrált α aktivitás mértéke karakterisztikus mintázatot mutatott. Az α aktivitás legmagasabb occipitálisan volt relaxáció alatt, a kognitív terheléssel pedig csökkent, főleg frontálisan. Ez az aktivitási mintázat nem mindenkire volt jellemző, néhány résztvevőnél a relaxáció alatti α aktivitás növekedés még occipitálisan sem volt regisztrálható. Emiatt az EEG adatok interpretációjához az egyéni jellegzetességek felderítése elengedhetetlen. Az eddigi eredmények alapján az EEG pedagógiai alkalmazása ígéretesnek tűnik, érdemes alkalmazási spektrumát bővíteni a pedagógiai kutatási programokban. ----- Portable, inexpensive, and easy-to-use electroencephalography (EEG) devices allow the examination of cognitive processes without a dramatic interference with normal ongoing behaviors. The scalp EEG signal amplitude is in the microvolts range and it is easily contaminated with different artifacts, such as ocular or muscle activities. Artifact removal has not been resolved satisfactory, therefore the use of EEG devices is limited to activities requiring minimal muscle activity. Despite this limitation several areas of pedagogy have already benefited from EEG measurements. Brain activities during different tasks provided information about engagement, mental workload, and cognitive strategies. Training in neurofeedback helped children with Special Educational Needs to maintain self-regulation and controll skills. Brain-to-brain synchrony measurements predicted the efficiency of information transfer, and showed social connectedness. These applications use the spectral analysis of the EEG signal. Fourier analysis decomposes the EEG into different sine waves and estimates the spectral power (which is proportional to the number of active neurons) at each frequency. From the human scalp α wave can be recorded the most reliably. Although this activity is mainly associated with iddleness, it is also important to maintain focus by blocking unwanted sensory processes. In our experiments α power showed characteristic differences among tasks. It was the highest occipitally during relaxation and it decreased especially frontally with cognitive engagement. Tasks requiring similar mental activity showed similar α power distribution. Individuals might showed EEG activity with distinct characteristics, forexample some participants did not have increased α power during relaxation. Therefore the reliable interpretation of EEG requires the consideration of individual differences as well. In conclusion, application of EEG in pedagogy is promising, and it is worth considering its incorporation into educational research programs.
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