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Relevant bibliographies by topics / Eeg

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Academic literature on the topic 'Eeg'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Eeg"

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Camfield, Peter, Kevin Gordon, Carol Camfield, John Tibbies, Joseph Dooley, and Bruce Smith. "EEG Results are Rarely the Same if Repeated within Six Months in Childhood Epilepsy." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 22, no. 4 (1995): 297–300. http://dx.doi.org/10.1017/s0317167100039512.

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AbstractObjectiveTo assess the reliability of interictal spike discharge in routine electroencephalography (EEG) testing in children.MethodEEG results of all children diagnosed in Nova Scotia with epilepsy onset between 1977–85 (excluding myoclonic, akinetic-atonic and absence) were reviewed. The results of the EEG at time of diagnosis (EEG1) were compared with those of a second EEG (EEG2) within 6 months.ResultsOf 504 children with epilepsy, 159 had both EEG1 and EEG2. EEG2 was more likely ordered if EEG1 was normal or showed focal slowing but less likely if EEG1 contained sleep (p < 0.05)

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Reilly, Richard B., and T. Clive Lee. "Electrograms (ECG, EEG, EMG, EOG)." Technology and Health Care 18, no. 6 (2010): 443–58. http://dx.doi.org/10.3233/thc-2010-0604.

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Hawkins, Margaret. "ECG for the EEG Technologist." American Journal of EEG Technology 32, no. 1 (1992): 46–57. http://dx.doi.org/10.1080/00029238.1992.11080391.

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Kamata, K., T. Ylinen, N. P. Subramaniyam, A. Yli-Hankala, A. J. Aho, and V. Jäntti. "ECG artifact in EEG monitoring." European Journal of Anaesthesiology 29 (June 2012): 51. http://dx.doi.org/10.1097/00003643-201206001-00165.

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Fadia, Noori Hummadi Al-Nuaimy. "A new eliminating EOG artifacts technique using combined decomposition methods with CCA and H.P.F. techniques." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 5 (2020): 2580~2586. https://doi.org/10.12928/TELKOMNIKA.v18i5.14143.

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Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The

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Bleichner, Martin G., and Stefan Debener. "Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG." Frontiers in Human Neuroscience 11 (June 5, 2017): 163. https://doi.org/10.3389/fnhum.2017.00163.

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Zaiwalla, Zenobia. "To EEG or not EEG." Paediatrics and Child Health 28, no. 6 (2018): 289–92. http://dx.doi.org/10.1016/j.paed.2018.04.013.

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Guevara, Miguel Angel, and María Corsi-Cabrera. "EEG coherence or EEG correlation?" International Journal of Psychophysiology 23, no. 3 (1996): 145–53. http://dx.doi.org/10.1016/s0167-8760(96)00038-4.

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Ikeda, Akio. "WS1.9. Advances in EEG Analysis – Wide-Band EEG, Dense-Array EEG and Quantitative EEG." Clinical Neurophysiology 132, no. 8 (2021): e53. http://dx.doi.org/10.1016/j.clinph.2021.02.072.

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Yang, Chia-Yen, Pin-Chen Chen, and Wen-Chen Huang. "Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models." Sensors 23, no. 5 (2023): 2458. http://dx.doi.org/10.3390/s23052458.

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Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal resolution. In contrast to electrocardiography (ECG), EEG can be uncomfortable and inconvenient for patients. Moreover, deep-learning techniques require a large dataset and a long time for training from scratch. Therefore, in this study, EEG–EEG or EEG–ECG transfer learning strategies were applied to explore their effectiveness for the training of simple cross-domain convolutional neural networks (CNNs) used in seizure prediction and sleep staging sy

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Dissertations / Theses on the topic "Eeg"

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Zhang, Shuoyue [Verfasser], and Jürgen [Akademischer Betreuer] Hennig. "Artifacts denoising of EEG acquired during simultaneous EEG-FMRI." Freiburg : Universität, 2021. http://d-nb.info/1228786968/34.

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Balli, Tugce. "Nonlinear analysis methods for modelling of EEG and ECG signals." Thesis, University of Essex, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.528852.

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JESSY, PAROKARAN. "Analysis of EEG Signals for EEG-based Brain-Computer Interface." Thesis, Mälardalen University, School of Innovation, Design and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-6622.

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<p>Advancements in biomedical signal processing techniques have led Electroencephalography (EEG) signals to be more widely used in the diagnosis of brain diseases and in the field of Brain Computer Interface(BCI). BCI is an interfacing system that uses electrical signals from the brain (eg: EEG) as an input to control other devices such as a computer, wheel chair, robotic arm etc. The aim of this work is to analyse the EEG data to see how humans can control machines using their thoughts.In this thesis the reactivity of EEG rhythms in association with normal, voluntary and imagery of hand movem

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Babaeeghazvini, Parinaz. "EEG enhancement for EEG source localization in brain-machine speller." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-6016.

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A Brain-Computer Interface (BCI) is a system to communicate with external world through the brain activity. The brain activity is measured by Electro-Encephalography (EEG) and then processed by a BCI system. EEG source reconstruction could be a way to improve the accuracy of EEG classification in EEGbased brain–computer interface (BCI). In this thesis BCI methods were applied on derived sources which by their EEG enhancement it became possible to obtain a more accurate EEG detection and brought a new application to BCI technology that are recognition of writing letters imagery from brain waves

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Caat, Michael ten. "Multichannel EEG visualization." [S.l. : Groningen : s.n. ; University Library of Groningen] [Host], 2008. http://irs.ub.rug.nl/ppn/306087987.

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Congedo, Marco. "EEG Source Analysis." Habilitation à diriger des recherches, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00880483.

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Electroencephalographic data recorded on the human scalp can be modeled as a linear mixture of underlying dipolar source generators. The characterization of such generators is the aim of several families of signal processing methods. In this HDR we consider in several details three of such families, namely 1) EEG distributed inverse solutions, 2) diagonalization methods, including spatial filtering and blind source separation and 3) Riemannian geometry. We highlight our contributions in each of this family, we describe algorithms reporting all necessary information to make purposeful use of th

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Lovelace, Joseph A. "Ambulatory EEG Platform." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479816584544204.

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Holdova, Kamila. "Klasifikace spánkových EEG." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-219944.

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This thesis deals with wavelet analysis of sleep electroencephalogram to sleep stages scoring. The theoretical part of the thesis deals with the theory of EEG signal creation and analysis. The polysomnography (PSG) is also described. This is the method for simultaneous measuring the different electrical signals; main of them are electroencephalogram (EEG), electromyogram (EMG) and electrooculogram (EOG). This method is used to diagnose sleep failure. Therefore sleep, sleep stages and sleep disorders are also described in the present study. In practical part, some results of application of disc

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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

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Chowdhury, Muhammad Enamul Hoque. "Simultaneous EEG-fMRI : novel methods for EEG artefacts reduction at source." Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/14297/.

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This thesis describes the development and application of novel techniques to reduce the EEG artefacts at source during the simultaneous acquisition of EEG and fMRI data. The work described in this thesis was carried out by the author in the Sir Peter Mansfield Magnetic Resonance Centre, School of Physics & Astronomy at the University of Nottingham, between October 2010 and January 2013. Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variatio

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Books on the topic "Eeg"

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Lutzenberger, Werner, Thomas Elbert, Brigitte Rockstroh, and Niels Birbaumer. Das EEG. Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-662-06459-7.

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Mulert, Christoph, and Louis Lemieux, eds. EEG - fMRI. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-87919-0.

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Mulert, Christoph, and Louis Lemieux, eds. EEG - fMRI. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07121-8.

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National Institutes of Health (U.S.). Office of Clinical Center Communications, ed. EEG (electroencephalogram). Clinical Center Communications, National Institutes of Health, 1989.

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Mark, Quigg, ed. EEG pearls. Mosby Elsevier, 2006.

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Axmacher, Nikolai, ed. Intracranial EEG. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20910-9.

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Payne, Mary, and David Gloss II, eds. Neonatal EEG. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-92556-6.

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Tatum, William O., ed. Ambulatory EEG Monitoring. Springer Publishing Company, 2017. http://dx.doi.org/10.1891/9781617052781.

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Sanei, Saeid, and J. A. Chambers. EEG Signal Processing. John Wiley & Sons Ltd,, 2007. http://dx.doi.org/10.1002/9780470511923.

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Im, Chang-Hwan, ed. Computational EEG Analysis. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0908-3.

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Book chapters on the topic "Eeg"

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Richter, Michael M., Sheuli Paul, Veton Këpuska, and Marius Silaghi. "Biomedical Signals: ECG, EEG." In Signal Processing and Machine Learning with Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-45372-9_25.

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Ellenbroek, Bart, Alfonso Abizaid, Shimon Amir, et al. "EEG." In Encyclopedia of Psychopharmacology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_3227.

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LaCaille, Lara, Anna Maria Patino-Fernandez, Jane Monaco, et al. "EEG." In Encyclopedia of Behavioral Medicine. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_100532.

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Ozcan, Mehmet S. "EEG." In Data Interpretation in Anesthesia. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55862-2_11.

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Gupta, Rashmi, Sonu Purohit, and Jeetendra Kumar. "EEG." In Computational Techniques in Neuroscience. CRC Press, 2023. http://dx.doi.org/10.1201/9781003398066-5.

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Luque, David. "EEG." In Encyclopedia of Animal Cognition and Behavior. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47829-6_1273-1.

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Luque, David. "EEG." In Encyclopedia of Animal Cognition and Behavior. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-55065-7_1273.

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Inai, Kei, Alexander K. C. Leung, Jouni Uitto, et al. "EEG." In Encyclopedia of Molecular Mechanisms of Disease. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-29676-8_6573.

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Villringer, Arno, Christoph Mulert, and Louis Lemieux. "Principles of Multimodal Functional Imaging and Data Integration." In EEG - fMRI. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-87919-0_1.

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Carmichael, David. "Image Quality Issues." In EEG - fMRI. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-87919-0_10.

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Conference papers on the topic "Eeg"

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Hendrawan, Muhammad Afif, Noprianto, Maskur, and Sofyan Noor Arief. "Enhancing Biometric Identification Through EEG and ECG Fusion." In 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, 2024. https://doi.org/10.1109/isriti64779.2024.10963629.

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Ye, Haili, Stephan Goerttler, and Fei He. "EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782694.

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Truong, Dung, Muhammad Abdullah Khalid, and Arnaud Delorme. "EEG-SSL: A Framework for Self-Supervised Learning on EEG." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822136.

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Wu, Anqi, Yifan Zhang, Yang Yu, and Ling-Li Zeng. "EEG-ARNet: An Autoregressive Pre-training Model for Extracting EEG Features." In 2024 5th International Conference on Computers and Artificial Intelligence Technology (CAIT). IEEE, 2024. https://doi.org/10.1109/cait64506.2024.10962980.

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Zhang, Fuquan, Yu Wu, Xinxin Xu, et al. "EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for Edge Devices." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821905.

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Kumar, Lokesh, Kumar Kaustubh, Tonmoy Rajkhowa, and Shashaank Aswatha Mattur. "EEG-ViLSTM: A Deep Learning Approach for Depression Detection Using EEG Signals." In 2025 National Conference on Communications (NCC). IEEE, 2025. https://doi.org/10.1109/ncc63735.2025.10983346.

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Duplevska, Diana, Anete Savicka, and Maksims Ivanovs. "Motor Imagery EEG Classification with Real-World and EEG-GAN-Generated Data." In 2025 IEEE 12th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE). IEEE, 2025. https://doi.org/10.1109/aieee66149.2025.11050769.

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Lin, Songlu, Zhihong Wang, Yuzhe Wang, and Jie Liu. "CS-EEG: A Deep Compressed Sensing Approach for Single-Channel EEG Reconstruction." In 2024 3rd International Conference on Health Big Data and Intelligent Healthcare (ICHIH). IEEE, 2024. https://doi.org/10.1109/ichih63459.2024.11064872.

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Lux. "Advances In Ecg And Eeg Mapping." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.590388.

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Lux, Robert L. "Advances in ECG and EEG mapping." In 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1992. http://dx.doi.org/10.1109/iembs.1992.5762134.

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Reports on the topic "Eeg"

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Morton, Paul E., and Glenn F. Wilson. Backpropagation and EEG Data. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada279073.

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Glickman, Matthew R., and Akaysha Tang. EEG analyses with SOBI. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/978914.

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Peterson, Matthew S. Electroencephalogy (EEG) Feedback in Decision-Making. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ad1007472.

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Hively, L. M., N. E. Clapp, C. S. Daw, W. F. Lawkins, and M. L. Eisenstadt. Nonlinear analysis of EEG for epileptic seizures. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/366563.

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Samaras, George M. Development of an EEG Artifact Correction Device. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada227360.

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Mosher, J. C., M. Huang, R. M. Leahy, and M. E. Spencer. Modeling versus accuracy in EEG and MEG data. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/554813.

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Oh, Keunyoung. EEG/ERP Research in Consumer Perceptions of Apparel Products. Iowa State University, Digital Repository, 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-22.

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Johnson, Michael K. Probe-Independent EEG Assessment of Mental Workload in Pilots. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada619147.

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Fray, Donald H., Sally C. Ballard, and James K. Channell. EEG operational radiation surveillance of the WIPP Project during 2001. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/1184414.

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Bhagavatula, Vijayakumar. Advanced Signal Processing and Machine Learning Approaches for EEG Analysis. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada535204.

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