Готові списки джерел за темами / EEG rhythms

Добірка наукової літератури з теми "EEG rhythms"

Автор: Grafiati
Опубліковано: 30 травня 2022
Оновлено: 31 травня 2022

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Статті в журналах з теми "EEG rhythms"

1

Morris, Harold H. "EEG Rhythms." Journal of Clinical Neurophysiology 7, no. 2 (April 1990): 155–56. http://dx.doi.org/10.1097/00004691-199004000-00001.

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2

Prado, G. Fernandes do, L. B. C. Carvalho, A. Baptista da Silva, and J. G. C. Lima. "EEG and dementia indicators in AIDS patients' Rorschach test." Arquivos de Neuro-Psiquiatria 52, no. 3 (September 1994): 314–19. http://dx.doi.org/10.1590/s0004-282x1994000300005.

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We studied the EEG and Rorschach test (RT) of nineteen AIDS patients and eight normal people in the same age group. Eight patients presented slow alpha rhythms (8 to 9 Hz); three, not-slow alpha rhythms (>9 to 13Hz); and eight, beta rhythms in background activity. Paroxystic activity, characterized by diffuse theta or delta waves, was present in eleven patients. We observed Oberholzer syndrome (organic dementia diagnosed by RT) in ten patients and Piotrowski syndrome (organic dementia diagnosed by RT) in eleven patients; six presented both. When considering only the group of AIDS patients, we did not observe a significant relation among slow alpha rhythm, not-slow alpha rhythm and the presence of paroxystic activity with the above-mentioned syndromes. AIDS patients with slow alpha rhythms showed a significantly greater number of Piotrowski syndrome dementia indicators when compared to normal individuals or those with slow alpha rhythms. We did not observe the same with Oberholzer syndrome.
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3

Gilmore, Sean A., and Frank A. Russo. "Neural and Behavioral Evidence for Vibrotactile Beat Perception and Bimodal Enhancement." Journal of Cognitive Neuroscience 33, no. 4 (April 2021): 635–50. http://dx.doi.org/10.1162/jocn_a_01673.

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The ability to synchronize movements to a rhythmic stimulus, referred to as sensorimotor synchronization (SMS), is a behavioral measure of beat perception. Although SMS is generally superior when rhythms are presented in the auditory modality, recent research has demonstrated near-equivalent SMS for vibrotactile presentations of isochronous rhythms [Ammirante, P., Patel, A. D., & Russo, F. A. Synchronizing to auditory and tactile metronomes: A test of the auditory–motor enhancement hypothesis. Psychonomic Bulletin & Review, 23, 1882–1890, 2016]. The current study aimed to replicate and extend this study by incorporating a neural measure of beat perception. Nonmusicians were asked to tap to rhythms or to listen passively while EEG data were collected. Rhythmic complexity (isochronous, nonisochronous) and presentation modality (auditory, vibrotactile, bimodal) were fully crossed. Tapping data were consistent with those observed by Ammirante et al. (2016), revealing near-equivalent SMS for isochronous rhythms across modality conditions and a drop-off in SMS for nonisochronous rhythms, especially in the vibrotactile condition. EEG data revealed a greater degree of neural entrainment for isochronous compared to nonisochronous trials as well as for auditory and bimodal compared to vibrotactile trials. These findings led us to three main conclusions. First, isochronous rhythms lead to higher levels of beat perception than nonisochronous rhythms across modalities. Second, beat perception is generally enhanced for auditory presentations of rhythm but still possible under vibrotactile presentation conditions. Finally, exploratory analysis of neural entrainment at harmonic frequencies suggests that beat perception may be enhanced for bimodal presentations of rhythm.
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4

Hu, Hai, Zihang Pu, and Peng Wang. "A flexible and accurate method for electroencephalography rhythms extraction based on circulant singular spectrum analysis." PeerJ 10 (March 23, 2022): e13096. http://dx.doi.org/10.7717/peerj.13096.

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Rhythms extraction from electroencephalography (EEG) signals can be used to monitor the physiological and pathological states of the brain and has attracted much attention in recent studies. A flexible and accurate method for EEG rhythms extraction was proposed by incorporating a novel circulant singular spectrum analysis (CiSSA). The EEG signals are decomposed into the sum of a set of orthogonal reconstructed components (RCs) at known frequencies. The frequency bandwidth of each RC is limited to a particular brain rhythm band, with no frequency mixing between different RCs. The RCs are then grouped flexibly to extract the desired EEG rhythms based on the known frequencies. The extracted brain rhythms are accurate and no mixed components of other rhythms or artifacts are included. Simulated EEG data based on the Markov Process Amplitude EEG model and experimental EEG data in the eyes-open and eyes-closed states were used to verify the CiSSA-based method. The results showed that the CiSSA-based method is flexible in alpha rhythms extraction and has a higher accuracy in distinguishing between the eyes-open and eyes-closed states, compared with the basic SSA method, the wavelet decomposition method, and the finite impulse response filtering method.
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5

Suzuki, Takako, Makoto Suzuki, Kilchoon Cho, Naoki Iso, Takuhiro Okabe, Toyohiro Hamaguchi, Junichi Yamamoto, and Naohiko Kanemura. "EEG Oscillations in Specific Frequency Bands Are Differently Coupled with Angular Joint Angle Kinematics during Rhythmic Passive Elbow Movement." Brain Sciences 12, no. 5 (May 14, 2022): 647. http://dx.doi.org/10.3390/brainsci12050647.

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Rhythmic passive movements are often used during rehabilitation to improve physical functions. Previous studies have explored oscillatory activities in the sensorimotor cortex during active movements; however, the relationship between movement rhythms and oscillatory activities during passive movements has not been substantially tested. Therefore, we aimed to quantitatively identify changes in cortical oscillations during rhythmic passive movements. Twenty healthy young adults participated in our study. We placed electroencephalography electrodes over a nine-position grid; the center was oriented on the transcranial magnetic stimulation hotspot of the biceps brachii muscle. Passive movements included elbow flexion and extension; the participants were instructed to perform rhythmic elbow flexion and extension in response to the blinking of 0.67 Hz light-emitting diode lamps. The coherence between high-beta and low-gamma oscillations near the hotspot of the biceps brachii muscle and passive movement rhythms was higher than that between alpha oscillation and passive movement rhythm. These results imply that alpha, beta, and gamma oscillations of the primary motor cortex are differently related to passive movement rhythm.
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6

Thuraisingham, R. A. "Revisiting ICEEMDAN and EEG rhythms." Biomedical Signal Processing and Control 68 (July 2021): 102701. http://dx.doi.org/10.1016/j.bspc.2021.102701.

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7

Andrew, Colin. "Sensorimotor EEG rhythms and their connection to local/global neocortical dynamic theory." Behavioral and Brain Sciences 23, no. 3 (June 2000): 399–400. http://dx.doi.org/10.1017/s0140525x0022325x.

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Анотація:
The EEG activity recorded from the human sensorimotor cortical area exhibits rhythmic activity covering a broad range of frequencies, including alpha, mu, beta, and gamma (40-Hz) rhythms. This commentary elaborates on connections between these sensorimotor rhythms and Nunez's neocortical dynamic theory.
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8

Yakovenko, Irina A., Nadejda E. Petrenko, Evgeniy A. Cheremoushkin, Vladimir B. Dorokhov, Zarina B. Bakaeva, Elena B. Yakunina, Vladimir I. Torshin, Yuri P. Starshinov, and Dmitry S. Sveshnikov. "Influence of lack of night sleep on the cognitive set by indicators of EEG rhythms coupling." SOCIALNO-ECOLOGICHESKIE TECHNOLOGII 10, no. 2 (2020): 226–39. http://dx.doi.org/10.31862/2500-2961-2020-10-2-226-239.

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The aim of the study is investigation of nighttime sleep effect on the performance of a cognitive setting in terms of the coupling of EEG rhythms. The coupling of 5 rhythm: beta-1, beta-2, gamma, alpha and theta rhythms of EEG during the formation and testing of cognitive set was studied for 120 students (17 with short-term night sleep and 15 with a full night sleep). Multi-channel EEG was recorded. EEG evaluation was carried out by continuous wavelet transform based on the “mother” complex Morlet wavelet in the range of 1–35 Hz. Maps of the distribution of the values of the modulus of the wavelet transformation coefficient, which reflect amplitude changes of the potentials were analyzed. The Pearson correlation coefficient was a measure evaluating the coupling of EEG rhythms. The subjects with a short night’s sleep showed almost all of the relations of EEG rhythms (8 couples) during the formation stage of presentation. Students with a full night’s sleep showed statistically significant coupling of the following pairs of rhythms: alpha–beta-1, alpha–gamma and beta-2–gamma. Students with short-term night sleep demonstrated the 3 significant couples: alpha–beta-1, beta-1–gamma and beta-2–gamma during the testing stage. Well-slept students showed an increase in the number of connections (6 couples) in relation to the stage of formation of the set due to the addition of connections with the theta rhythm. The obtained data could indicate that the thalamo-cortical and cortico-hippocampal structural-functional associations work differently in the groups of subjects.
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9

Eismont, Ye V., T. A. Aliyeva, N. V. Lutsyuk, and V. B. Pavlenko. "APPLICATION OF EEG FEEDBACK FOR THE CORRECTION OF PSYCHOEMOTIONAL STATE OF CHILDREN." Bulletin of Siberian Medicine 12, no. 2 (April 28, 2013): 175–81. http://dx.doi.org/10.20538/1682-0363-2013-2-175-181.

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Evaluated the efficacy of neurofeedbacktherapy to normalize the psychoemotional state of children. Trained parameters of EEG were the amplitude of alpha-rhythm, the ratio of amplitudes of alphaand theta-rhythms, sensorimotor and theta-rhythms. In the experimental group showed an increase in the trained parameters of the electroencephalogram, reduced anxiety, “feelings of inferiority” and improvement of voluntary attention. The results indicate the feasibility of neurofeedbacktherapy to optimize the psychoemotional state of children.
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10

Bushov, Yu V., and M. V. Svetlik. "PHASE INTERACTION BETWEEN EEG RHYTHMS IN THE STUDY OF PROCESSES OF TIME PERCEPTION." Bulletin of Siberian Medicine 13, no. 6 (December 28, 2014): 121–25. http://dx.doi.org/10.20538/1682-0363-2014-6-121-125.

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The present study pursued to investigate the role of phase interactions between EEG rhythms in the process of the perception of time. The purpose of the study was to analyse the dependence of these interactions on the type and stage of the activity being performed, as well as on the individual characteristics of a human. For this purpose, 27 boys and 29 girls, all university students, were asked to reproduce and measure short intervals of time (200 and 800 ms), during which their EEG was recorded in frontal, central, parietal, temporal, and occipital lobes, according to the system 10–20%. While studying phase interactions between EEG rhythms, we used wavelet bispectral analysis and calculated the bicoherence function. As it follows from the conducted research, most often close phase interactions are observed between the gamma-rhythm and other rhythms of EEG or between different frequencies of the gamma-rhythm. It was established that the phase interactions under study were influenced by the factors of “sex”, “activity type”, and “activity stage”. The study showed correlations of phase interactions with the levels of intellect, extraversion, neuroticism, with the particularities of the lateral organisation of brain, and the accuracy of time perception.
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Більше джерел

Дисертації з теми "EEG rhythms"

1

Ste̜pień, Magdalena [Verfasser]. "Event-related desynchronization (ERD) of sensorimotor EEG rhythms in hemiparetic patients with acute hemispheric stroke / Magdalena Stępień." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2011. http://d-nb.info/1025510593/34.

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2

Armitage, Roseanne Carleton University Dissertation Psychology. "Ultradian rhythms in EEG and performance; an assessment of individual differences in the basic rest-activity cycle." Ottawa, 1986.

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3

Moosmann, Matthias Walter. "Characterization of human background rhythms with functional magnetic resonance imaging." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2007. http://dx.doi.org/10.18452/15593.

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Diese Dissertation zeigt, dass Hintergrundrhythmen mit Hilfe der gleichzeitigen Messung von EEG und fMRI Signalen untersucht werden können. Die Methodik dieses Ansatzes wurde durch den Einsatz einer speziellen fMRI Sequenz weiterentwickelt, und die Signalqualität durch visuell evozierte Potentiale überprüft. Der prominente okzipitale Alpha-Rhythmus und die vergleichsweise schwächeren rolandischen Rhythmen konnten in der elektromagnetisch störenden Umgebung des Magnetresonanztomografen, auch und gerade während der funktionellen Messsequenzen identifiziert werden. Durch den Einsatz der in dieser Arbeit vorgestellten Nachverarbeitungsmethoden kann die simultane Aufnahme von EEG und fMRI Signalen wertvolle Informationen über die neuronale Grundlage von Hirnrhythmen und ihrer hemodynamischer Korrelate liefern. Die hier vorgestellten Daten bekräftigen die Hypothese, dass die Amplitude der Hintergrundrhythmen mit spezifischen Deaktivierungen in sensorischen Hirnarealen einhergehen. Eine erhöhte Amplitude aller untersuchter Rhythmen war mit einem negativen BOLD Signal in sensorischen kortikalen Arealen verknüpft, was auf einen erniedrigten Energieverbrauch in Arealen mit höherer Synchronizität schliessen lässt. Der posteriore Alpha Rhythmus, ist invers mit dem hemodynamischen Signal in primären visuellen Arealen gekoppelt, während hämodynamische Korrelate der rolandischen Alpha und Beta Rhythmen in somatomotorischen Arealen lokalisiert wurden. Für den rolandischen Alpha und Beta Rhythmus wurden unterschiedliche regionale Netzwerke gefunden. Der rolandische Beta Rhythmus ist mit dem Motornetzwerk, während der rolandische Alpha Rhythmus mit einen somatosensorischen bzw. Assoziationsnetzwerk assoziert ist, was eine fundamentale Eigenschaft des Somatomotorischen Systems zu sein scheint. Die rolandischen Rhythmen könnten dadurch somatomotorische Areale während der Erhaltung oder Planung von Bewegungsabläufen funktional koppeln [Brovelli, et al., 2004]. Desweiteren wurde gezeigt, dass thalamische und cinguläre Strukturen mögliche Generatoren oder Modulatoren der hier untersuchten Hintergrundrhythmen sind. Die experimentellen Daten der hier vorgestellten Studien legen nahe, dass eine inverse Beziehung der Stärke eines Hintergrundrhythmus mit regional kortikalem Metabolismus und gleichzeitig eine „antagonistische“, positive Beziehung mit thalamischen oder cingulären Struktuen ein gernerelles orgnaisatorisches Prinzip des Gehirns zu sein scheint. Der Begriff der Grundaktivität des Gehirns [Gusnard, et al., 2001] müsste daher in verschiedene Netzwerke der Grundaktivität unterteilt werden, die elektrophysiologisch durch Hintergrundrhythmen definiert wären.
The data provided by this thesis show that imaging of brain rhythms can be achieved by simultaneous EEG-fMRI recordings. This methodology was developed further by implementing an adapted MR sequence and the EEG-fMRI signal quality was confirmed by means of visual evoked potentials. Together with the post processing methods applied in this work, simultaneous EEG-fMRI recordings can thus provide valuable information about the neuronal basis of brain rhythms and their regional hemodynamic correlates. The data further substantiate the hypothesis that ‘idling’ rhythms indicate distinct deactivated sensory cortical areas. Increased power of all examined rhythms was associated with negative BOLD signal in sensory cortical areas, indicating less energy consumption in those areas with higher synchronicity. The posterior alpha or so-called Berger rhythm is coupled inversely to the hemodynamics in primary visual areas, whereas rolandic alpha and beta rhythm could be localized to somatomotor areas. Different networks were found for rolandic alpha and beta rhythms. The rolandic beta rhythm is more associated with a motor-network whereas the rolandic alpha rhythm is more associated with a sensory and association network which represents a fundamental characteristic of the sensorimotor system. The rolandic oscillations may bind sensorimotor areas into a functional loop during pre-movement motor maintenance behaviour [Brovelli, et al., 2004]. Furthermore thalamic and cingulate structures were shown to be possible generative or modulatory structures for the brain rhythms examined in this study. The experimental data obtained in this work suggest that the inverse correlation of an ‘idling’ rhythm’s strength with the metabolism in ‘its cortical areas’, and the positive correlation with cingulate or thalamic areas are both general organizational principles. The notion of a default mode of the brain [Gusnard, et al., 2001] may perhaps be further subdivided into different networks with a “default mode”, each of them electro-physiologically defined by its “idle rhythm”.
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4

Malý, Lukáš. "Ovládání invalidního vozíku pomocí klasifikace EEG signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221361.

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Tato diplomová práce představuje koncept elektrického invalidního vozíku ovládaného lidskou myslí. Tento koncept je určen pro osoby, které elektrický invalidní vozík nemohou ovládat klasickými způsoby, jakým je například joystick. V práci jsou popsány čtyři hlavní komponenty konceptu: elektroencefalograf, brain-computer interface (rozhraní mozek-počítač), systém sdílené kontroly a samotný elektrický invalidní vozík. V textu je představena použitá metodologie a výsledky provedených experimentů. V závěru jsou nastíněna doporučení pro budoucí vývoj.
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5

MORAES, Renato Barros. "Análise não-linear dos diferentes ritmos cerebrais nos registros do EEG em humanos com Epilepsia e no ECoG de ratos em status epilepticus." Universidade Federal Rural de Pernambuco, 2010. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/4661.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Over the last 25 years, major advances have occurred in the techniques of nonlinear analysis applied to time series. These techniques have helped us to understand how dynamic systems behave over time. The brain is considered the most complex dynamic system known for man, and as such, it presents great challenges to the understanding of their processes, both physiological and pathological. In this work, we try to better understand epilepsy, a brain disease that affects millions of individuals around the world. The records of electroencephalogram (EEG) and electrocorticogram (ECoG) are widely used in the clinic for diagnosis and monitoring of epilepsy, but the information contained in these records are underutilized, since they are generally analyzed by the clinical eye. It is known that is contained in the EEG and ECoG, some specific frequencies such as alpha (α), beta (β), theta (θ), delta (δ) and gamma (γ) and they have interesting properties for the diagnosis of some brain pathologies. Through the DFA (Detrended fluctuation Analysis) technique used to verify long-range correlation in time series, and a derivation of this, the Parabolicity index (b), we observed some differences in EEG and ECoG signals, to normal and epileptic conditions between different brain rhythms, both in an animal model and in human records.
Nos últimos 25 anos, grandes avanços têm ocorrido nas técnicas de análise não-linear aplicadas a séries temporais. Essas técnicas têm nos ajudado a entender como sistemas dinâmicos se comportam com o passar do tempo. O cérebro é considerado o sistema dinâmico mais complexo conhecido pelo homem, e como tal apresenta grandes desafios para a compreensão de seus processos, tanto fisiológicos quanto patológicos. Nesse trabalho, tentamos compreender melhor a epilepsia, uma patologia cerebral que afeta milhões de indivíduos em todo o mundo. Os registros de eletroencefalograma (EEG) e eletrocorticograma (ECoG) são bastante utilizados na clínica para o diagnóstico e acompanhamento da epilepsia, porém as informações contidas nestes registros são subutilizadas, uma vez que são analisadas geralmente pelo olho clínico. Sabe-se que estão contidas no EEG e ECoG, algumas freqüências específicas tais como alfa(α), beta(β), teta(θ), delta(δ) e gama(γ), e que elas possuem propriedades interessantes para diagnóstico de algumas patologias cerebrais. Através da DFA (Análise de Flutuação sem Tendência), técnica usada para verificar correlação de longo alcance em séries temporais, e de uma derivação dessa, o Índice de parabolicidade (b), conseguimos verificar algumas diferenças nos sinais de ECoG e EEG, para uma condição normal e epiléptico, entre as diferentes ondas cerebrais, tanto num modelo animal quanto em registros de humanos.
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6

Kosciessa, Julian Q. "Measurement and relevance of rhythmic and aperiodic human brain dynamics." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/22040.

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Menschliche Hirnsignale von der Kopfhaut bieten einen Einblick in die neuronalen Prozesse, denen Wahrnehmung, Denken und Verhalten zugrunde liegen. Rhythmen, die historisch den Grundstein für die Erforschung großflächiger Hirnsignale legten, sind ein häufiges Zeichen neuronaler Koordination, und damit von weitem Interesse für die kognitiven, systemischen und komputationalen Neurowissenschaften. Typischen Messungen von Rhythmizität fehlt es jedoch an Details, z. B. wann und wie lange Rhythmen auftreten. Darüber hinaus weisen neuronale Zeitreihen zahlreiche dynamische Muster auf, von denen nur einige rhythmisch erscheinen. Obwohl aperiodischen Beiträgen traditionell der Status irrelevanten „Rauschens“ zugeschrieben wird, attestieren neuere Erkenntnisse ihnen ebenfalls eine Signalrolle in Bezug auf latente Hirndynamik. Diese kumulative Dissertation fasst Projekte zusammen, die darauf abzielen, rhythmische und aperiodische Beiträge zum menschlichen Elektroenzephalogramm (EEG) methodisch zu dissoziieren, und ihre Relevanz für die flexible Wahrnehmung zu untersuchen. Projekt 1 ermittelt insbesondere die Notwendigkeit und Durchführbarkeit der Trennung rhythmischer von aperiodischer Aktivität in kontinuierlichen Signalen. Projekt 2 kehrt diese Perspektive um und prüft Multiscale Entropy als Index für die Unregelmäßigkeit von Zeitreihen. Diese Arbeit weist auf methodische Probleme in der klassischen Messung zeitlicher Unregelmäßigkeit hin, und schlägt Lösungen für zukünftige Anwendungen vor. Abschließend untersucht Projekt 3 die neurokognitive Relevanz rhythmischer und aperiodischer Zustände. Anhand eines parallelen multimodalen EEG-fMRT-Designs mit gleichzeitiger Pupillenmessung liefert dieses Projekt erste Hinweise dafür, dass erhöhte kognitive Anforderungen Hirnsignale von einem rhythmischen zu einem unregelmäßigen Regime verschieben und impliziert gleichzeitige Neuromodulation und thalamische Aktivierung in diesem Regimewechsel.
Non-invasive signals recorded from the human scalp provide a window on the neural dynamics that shape perception, cognition and action. Historically motivating the assessment of large-scale network dynamics, rhythms are a ubiquitous sign of neural coordination, and a major signal of interest in the cognitive, systems, and computational neurosciences. However, typical descriptions of rhythmicity lack detail, e.g., failing to indicate when and for how long rhythms occur. Moreover, neural times series exhibit a wealth of dynamic patterns, only some of which appear rhythmic. While aperiodic contributions are traditionally relegated to the status of irrelevant ‘noise’, they may be informative of latent processing regimes in their own right. This cumulative dissertation summarizes and discusses work that (a) aims to methodologically dissociate rhythmic and aperiodic contributions to human electroencephalogram (EEG) signals, and (b) probes their relevance for flexible cognition. Specifically, Project 1 highlights the necessity, feasibility and limitations of dissociating rhythmic from aperiodic activity at the single-trial level. Project 2 inverts this perspective, and examines the utility of multi-scale entropy as an index for the irregularity of brain dynamics, with a focus on the relation to rhythmic and aperiodic descriptions. By highlighting prior biases and proposing solutions, this work indicates future directions for measurements of temporal irregularity. Finally, Project 3 examines the neurocognitive relevance of rhythmic and aperiodic regimes with regard to the neurophysiological context in which they may be engaged. Using a parallel multi-modal EEG-fMRI design with concurrent pupillometry, this project provides initial evidence that elevated demands shift cortical dynamics from a rhythmic to an irregular regime; and implicates concurrent phasic neuromodulation and subcortical thalamic engagement in these regime shifts.
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7

Elliman, Toby. "The EEG alpha cycle as a cortical excitability rhythm." Thesis, University of Bristol, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.508091.

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8

Adikarapatti, Vikramvarun Kannan. "OPTIMAL EEG CHANNELS AND RHYTHM SELECTION FOR TASK CLASSIFICATION." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1176482808.

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9

Knebel, Timothy F. "EEG theta power during Necker cube reversals." Thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-07212009-040317/.

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10

Simms, Lori A. "Neuropsychologic correlates of a normal EEG variant: The mu rhythm." Thesis, University of North Texas, 2008. https://digital.library.unt.edu/ark:/67531/metadc9032/.

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Although the mu rhythm is traditionally defined as a normal EEG variant, recent evidence suggests that mu may have functional significance in a variety of disorders such as autism, Parkinson's disease, and multiple sclerosis. While an increasing number of articles have focused on the blocking mechanism of mu in relation to various cognitive processes and disorders, few have examined the significance of a prominent mu rhythm in the background EEG. A few studies have examined the relationship between the mu rhythm and psychological disturbance, such as attentional and affective disorders. Increasing evidence suggests that EEG and qEEG variables may be useful in classifying psychiatric disorders, presenting a neurophysiological alternative to traditional symptom-based diagnosis and classification. Thus, the intention of the present study was to examine the relationship between neuropsychological variables, gathered from multiple assessment sources, and the presence of a prominent mu rhythm in the EEG. Results did not show a statistically significant difference between individuals with and without a prominent mu rhythm on the Test of Variables of Attention (TOVA); although individuals in the mu group showed a pattern of increased impulsivity and performance decrement over time. For adults, no significant differences were observed between groups on psychological variables measured by the Minnesota Multiphasic Personality Inventory-2 (MMPI-2). However, for children, the mu and control groups differed on several behavioral and emotional variables on the Child Behavior Checklist (CBCL). Results are examined in the context of other research and clinical implications are discussed.
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Книги з теми "EEG rhythms"

1

Gillis, Jesse A. Deconstructing hippocampal EEG rhythms using time-frequency analysis. Ottawa: National Library of Canada, 2003.

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2

Nashmi, Raad. EEG rhythms of the human sensorimotor cortex during hand movements. Ottawa: National Library of Canada, 1993.

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3

A, Ochs Melvin, and Jones Karen Milazzo, eds. Recognition & interpretation of ECG rhythms. 3rd ed. Stamford, Conn: Appleton & Lange, 1997.

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4

E-Z ECG rhythm interpretation. Philadelphia, PA: F.A. Davis Co., 2006.

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5

Huijer, Marli. Ritme: Op zoek naar een terugkerende tijd. Amsterdam: Boom, 2011.

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6

L, Nunez Paul, and Cutillo Brian A, eds. Neocortical dynamics and human EEG rhythms. New York: Oxford University Press, 1995.

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7

MD, Edward B. Bromfield, and Wendi M. Nugent REEGT RPSGT. Atlas of Adult EEG: Rhythms in Sleep and Wake. Butterworth-Heinemann, 2000.

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8

Amzica, Florin, and Fernando H. Lopes da Silva. Cellular Substrates of Brain Rhythms. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0002.

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The purpose of this chapter is to familiarize the reader with the basic electrical patterns of the electroencephalogram (EEG). Brain cells (mainly neurons and glia) are organized in multiple levels of intricate networks. The cellular membranes are semipermeable media between extracellular and intracellular solutions, populated by ions and other electrically charged molecules. This represents the basis of electrical currents flowing across cellular membranes, further generating electromagnetic fields that radiate to the scalp electrodes, which record changes in the activity of brain cells. This chapter presents these concepts together with the mechanisms of building up the EEG signal. The chapter discusses the various behavioral conditions and neurophysiological mechanisms that modulate the activity of cells leading to the most common EEG patterns, such as the cellular interactions for alpha, beta, gamma, slow, delta, and theta oscillations, DC shifts, and some particular waveforms such as sleep spindles and K-complexes and nu-complexes.
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9

Babiloni, Claudio, Claudio Del Percio, and Ana Buján. EEG in Dementing Disorders. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0016.

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This chapter reviews the most relevant literature on qualitative and quantitative abnormalities in resting-state eyes-closed electroencephalographic (rsEEG) rhythms recorded in patients with dementing disorders due to Alzheimer’s disease, frontotemporal lobar degeneration, vascular disease, Parkinson’s disease, Lewy body disease, human immunodeficiency virus infection, and prion disease, mainly Creutzfeldt–Jakob disease. This condition of quiet wakefulness is the most used in clinical practice, as it involves a simple, innocuous, quick, noninvasive, and cost-effective procedure that can be repeated many times without effects of stress, learning, or habituation. While rsEEG has a limited diagnostic value (not reflecting peculiar pathophysiological processes directly), delta, theta, and alpha rhythms might be promising candidates as “topographical markers” for the prognosis and monitoring of disease evolution and therapy response, at least for the most diffuse dementing disorders. More research is needed before those topographical biomarkers can be proposed for routine clinical applications.
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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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Частини книг з теми "EEG rhythms"

1

Laufs, Helmut. "Brain Rhythms." In EEG - fMRI, 263–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-87919-0_13.

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2

Pfurtscheller, G. "EEG Rhythms - Event-Related Desynchronization and Synchronization." In Springer Series in Synergetics, 289–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76877-4_20.

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3

Petsche, Hellmuth, and Peter Rappelsberger. "Is There any Message Hidden in the Human EEG?" In Induced Rhythms in the Brain, 103–16. Boston, MA: Birkhäuser Boston, 1992. http://dx.doi.org/10.1007/978-1-4757-1281-0_5.

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4

Sing, H. C. "High Frequency EEG and Its Relationship to Cognitive Function." In Ultradian Rhythms from Molecules to Mind, 303–41. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8352-5_14.

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5

Başar, Erol, Canan Başar-Eroglu, Joachim Röschke, and Martin Schürmann. "Chaotic EEG Dynamics, Alpha and Gamma Rhythms Related to Brain Function." In Basic Mechanisms of the EEG, 73–95. Boston, MA: Birkhäuser Boston, 1993. http://dx.doi.org/10.1007/978-1-4612-0341-4_6.

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6

Petsche, H., P. Rappelsberger, O. Filz, and G. H. Gruber. "EEG studies in the perception of simple and complex rhythms." In Music, Language, Speech and Brain, 318–26. London: Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-12670-5_30.

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7

Dongare, Snehal, and Dinesh Padole. "Implementation of Different Methods for Decomposing the Rhythms of EEG Signal." In Information and Communication Technology for Competitive Strategies (ICTCS 2020), 483–91. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0739-4_46.

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8

Syrov, Nikolay, Anatoly Vasilyev, and Alexander Kaplan. "Sensorimotor EEG Rhythms During Action Observation and Passive Mirror-Box Illusion." In Communications in Computer and Information Science, 101–6. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90179-0_14.

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9

Samson-Dollfus, D., C. Delmer, Y. Vaschalde, E. Dreano, and D. Fodil. "Topography of Background EEG Rhythms in Normal Subjects and in Patients with Cerebrovascular Disorders." In Topographic Brain Mapping of EEG and Evoked Potentials, 185–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-72658-3_15.

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Wang, Yijun, Xiaorong Gao, Bo Hong, and Shangkai Gao. "Practical Designs of Brain–Computer Interfaces Based on the Modulation of EEG Rhythms." In Brain-Computer Interfaces, 137–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02091-9_8.

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Тези доповідей конференцій з теми "EEG rhythms"

1

Christodoulides, Pavlos, Victoria Zakopoulou, Katerina D. Tzimourta, Alexandros T. Tzallas, and Dimitrios Peschos. "THE CONTRIBUTION OF EEG RECORDINGS TO THE AUDIOVISUAL RECOGNITION OF WORDS IN UNIVERSITY STUDENTS WITH DYSLEXIA." In International Psychological Applications Conference and Trends. inScience Press, 2021. http://dx.doi.org/10.36315/2021inpact077.

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"Dyslexia is one of the most frequent specific learning disorders which has often been associated with deficits in phonological awareness mainly caused by auditory and visual inabilities to recognize and discriminate phonemes and graphemes within words. Neuroimaging techniques like EEG recordings have been widely used to assess hemispheric differences in brain activation between students with dyslexia and their typical counterparts. Although dyslexia is a lifelong disorder which persists into adulthood, very few studies have been carried out targeting in adult population. In this study, we examined the brain activation differences between 14 typical (control group) and 12 university students with dyslexia (experimental group). The participants underwent two tasks consisting of 50 3-word groups characterized by different degrees of auditory and visual distinctiveness. The whole procedure was recorded with a 14-sensor sophisticated wearable EEG recording device (Emotiv EPOC+). The findings from the auditory task revealed statistically significant differences among the two sets of groups in the left temporal lobe in ?, ? and ? rhythms, in the left occipital lobe in ? rhythm, and in the right prefrontal area in ?, ? and ? rhythms, respectively. The students with dyslexia reported higher mean scores only in ? rhythm in the left temporal lobe, and in ?, ? and ? rhythms in the right prefrontal area. Concerning the visual task, statistically significant differences were evident in the left temporal lobe in ?, ? rhythms, in the occipital lobe in ?, ? and ? rhythms, in the parietal lobe in ? rhythm, and in the right occipital lobe in ?, ? and ? rhythms. The students with dyslexia reported higher mean scores only in the ? rhythm of both the left and right occipital lobe. The results indicate that there are differences in the hemispheric brain activation of students with or without dyslexia in various rhythms in both experimental conditions, thus, shedding light in the neurophysiological discrepancies between the two groups. It also lays great emphasis on the necessity of carrying out more studies in adult population with dyslexia."
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Sri, Kavuri Swathi, and Jagath C. Rajapakse. "Extracting EEG rhythms using ICA-R." In 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong). IEEE, 2008. http://dx.doi.org/10.1109/ijcnn.2008.4634091.

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Veluvolu, K. C., H. G. Tan, S. S. Kavuri, W. T. Latt, C. Y. Shee, and W. T. Ang. "Adaptive estimation of EEG-rhythms for event classification." In 2008 IEEE International Conference on Robotics and Biomimetics. IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.4913175.

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Tychkov, Alexander Yu, Valeriy N. Gorbunov, Pyotr P. Churakov, and Alan K. Alimuradov. "HHT Modification for Automatic Separation of EEG Rhythms." In 2019 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). IEEE, 2019. http://dx.doi.org/10.1109/usbereit.2019.8736626.

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Yamada, Saki, and Yasue Mitsukura. "Detection of circadian rhythms using simple EEG device." In 2016 11th France-Japan & 9th Europe-Asia Congress on Mechatronics (MECATRONICS) /17th Internationall Conference on Research and Education in Mechatronics (REM). IEEE, 2016. http://dx.doi.org/10.1109/mecatronics.2016.7547135.

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Zachariah, Anusha, Jinu Jai, and Geevarghese Titus. "Automatic EEG artifact removal by independent component analysis using critical EEG rhythms." In 2013 International Conference on Control Communication and Computing (ICCC). IEEE, 2013. http://dx.doi.org/10.1109/iccc.2013.6731680.

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Wu, Xinyan, Fan Liu, Chen Lin, and Jiacai Zhang. "Correlation Studies of P300 and EEG Rhythms Using dVCA." In 2010 International Conference on Multimedia Technology (ICMT). IEEE, 2010. http://dx.doi.org/10.1109/icmult.2010.5631498.

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Douglas, Pamela K., and David B. Douglas. "Reconsidering Spatial Priors In EEG Source Estimation : Does White Matter Contribute to EEG Rhythms?" In 2019 7th International Winter Conference on Brain-Computer Interface (BCI). IEEE, 2019. http://dx.doi.org/10.1109/iww-bci.2019.8737307.

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Korik, Attila, Ronen Sosnik, Nazmul Siddique, and Damien Coyle. "Imagined 3D hand movement trajectory decoding from sensorimotor EEG rhythms." In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2016. http://dx.doi.org/10.1109/smc.2016.7844955.

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Sircar, Pradip, Ram Bilas Pachori, and Rupendra Kumar. "Analysis of rhythms of EEG signals using orthogonal polynomial approximation." In the 2009 International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1644993.1645025.

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