Academic literature on the topic 'EEG rhythms'
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Journal articles on the topic "EEG rhythms"
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.
Full textPrado, 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.
Full textGilmore, 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.
Full textHu, 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.
Full textSuzuki, 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.
Full textThuraisingham, 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.
Full textAndrew, 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.
Full textYakovenko, 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.
Full textEismont, 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.
Full textBushov, 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.
Full textDissertations / Theses on the topic "EEG rhythms"
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.
Full textArmitage, Roseanne Carleton University Dissertation Psychology. "Ultradian rhythms in EEG and performance; an assessment of individual differences in the basic rest-activity cycle." Ottawa, 1986.
Find full textMoosmann, 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.
Full textThe 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”.
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.
Full textMORAES, 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.
Full textMade available in DSpace on 2016-06-09T14:34:56Z (GMT). No. of bitstreams: 1 Renato Barros Moraes.pdf: 1461731 bytes, checksum: 72fe61b249cbff251455227ca064db5a (MD5) Previous issue date: 2010-02-09
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.
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.
Full textNon-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.
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.
Full textAdikarapatti, 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.
Full textKnebel, Timothy F. "EEG theta power during Necker cube reversals." Thesis, This resource online, 1993. http://scholar.lib.vt.edu/theses/available/etd-07212009-040317/.
Full textSimms, 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/.
Full textBooks on the topic "EEG rhythms"
Gillis, Jesse A. Deconstructing hippocampal EEG rhythms using time-frequency analysis. Ottawa: National Library of Canada, 2003.
Find full textNashmi, Raad. EEG rhythms of the human sensorimotor cortex during hand movements. Ottawa: National Library of Canada, 1993.
Find full textA, Ochs Melvin, and Jones Karen Milazzo, eds. Recognition & interpretation of ECG rhythms. 3rd ed. Stamford, Conn: Appleton & Lange, 1997.
Find full textE-Z ECG rhythm interpretation. Philadelphia, PA: F.A. Davis Co., 2006.
Find full textHuijer, Marli. Ritme: Op zoek naar een terugkerende tijd. Amsterdam: Boom, 2011.
Find full textL, Nunez Paul, and Cutillo Brian A, eds. Neocortical dynamics and human EEG rhythms. New York: Oxford University Press, 1995.
Find full textMD, Edward B. Bromfield, and Wendi M. Nugent REEGT RPSGT. Atlas of Adult EEG: Rhythms in Sleep and Wake. Butterworth-Heinemann, 2000.
Find full textAmzica, 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.
Full textBabiloni, 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.
Full textHari, MD, PhD, Riitta, and Aina Puce, PhD. MEG-EEG Primer. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190497774.001.0001.
Full textBook chapters on the topic "EEG rhythms"
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.
Full textPfurtscheller, 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.
Full textPetsche, 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.
Full textSing, 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.
Full textBaşar, 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.
Full textPetsche, 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.
Full textDongare, 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.
Full textSyrov, 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.
Full textSamson-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.
Full textWang, 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.
Full textConference papers on the topic "EEG rhythms"
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.
Full textSri, 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.
Full textVeluvolu, 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.
Full textTychkov, 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.
Full textYamada, 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.
Full textZachariah, 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.
Full textWu, 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.
Full textDouglas, 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.
Full textKorik, 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.
Full textSircar, 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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