Relevant bibliographies by topics / Eeg

Academic literature on the topic 'Eeg'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 September 2024

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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 (November 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). EEG1 and EEG2 were both normal in 23%. If EEG1 was abnormal, there was a 40–70% discordance for the type of abnormality on EEG2. Abnormalities were present on both EEG1 and EEG2 in 67 cases. Of the 42/67 with major focal abnormalities on EEG1, 7 had only generalized spike wave on EEG2. Of the 17/67 with only generalized spike wave on EEG 1, 7 showed only major focal abnormalities on EEG2. Statistical testing showed low Kappa scores indicating low reliability.ConclusionsThe interictal EEG in childhood epilepsy appears to be an unstable test. A repeat EEG within 6 months of a first EEG may yield different and sometimes conflicting information.
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Reilly, Richard B., and T. Clive Lee. "Electrograms (ECG, EEG, EMG, EOG)." Technology and Health Care 18, no. 6 (November 19, 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 (March 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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SASAKI, Minoru, and Kyoung ho Choi. "Removal of artifacts From EEG in a normal subject." Proceedings of the Conference on Information, Intelligence and Precision Equipment : IIP 2002 (2002): 181–84. http://dx.doi.org/10.1299/jsmeiip.2002.181.

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Zaiwalla, Zenobia. "To EEG or not EEG." Paediatrics and Child Health 28, no. 6 (June 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 (October 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 (August 2021): e53. http://dx.doi.org/10.1016/j.clinph.2021.02.072.

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Boesebeck, Frank. "Digitales EEG und sinnvolle EEG-Montagen in der EEG-Routinediagnostik." Das Neurophysiologie-Labor 30, no. 1 (August 2008): 1–13. http://dx.doi.org/10.1016/j.neulab.2008.04.005.

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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 (February 23, 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 systems, respectively. The seizure model detected interictal and preictal periods, whereas the sleep staging model classified signals into five stages. The patient-specific seizure prediction model with six frozen layers achieved 100% accuracy for seven out of nine patients and required only 40 s of training time for personalization. Moreover, the cross-signal transfer learning EEG–ECG model for sleep staging achieved an accuracy approximately 2.5% higher than that of the ECG model; additionally, the training time was reduced by >50%. In summary, transfer learning from an EEG model to produce personalized models for a more convenient signal can both reduce the training time and increase the accuracy; moreover, challenges such as data insufficiency, variability, and inefficiency can be effectively overcome.
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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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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 movements were studied using EEGLAB, a signal processing toolbox  under MATLAB. In awake people,  primary sensory or motor cortical areas often display 8-12 Hz EEG activity called ’Mu’ rhythm ,when they are not engaged in processing sensory input or produce motor output.  Movement or preparation of movement is typically accompanied by a decrease in this mu rhythm called ’event-related desynchronization’(ERD). Four males, three right handed and one left handed participated in this study. There were two sessions for each subject and three possible types : Imagery, Voluntary and Normal. The EEG data  was sampled at 256Hz , band pass filtered between 0.1 Hz and 50 Hz and then epochs of four events : Left  button press , Right button press, Right arrow ,Left arrow were extracted followed by baseline removal.After this preprocessing of EEG data, the epoch files were studied by analysing Event Related Potential plots, Independent Component Analysis, Power spectral Analysis and Time-Frequency plots. These analysis have shown that an imagination or a movement of right hand cause a decrease in activity in the hand area of sensory motor cortex in the left side of the brain which shows the desynchronization of Mu rhythm and an imagination or a movement of left hand cause a decrease in activity in the hand area of sensory motor cortex in the right side of the brain. This implies that EEG phenomena may be utilised in a Brain Computer Interface operated simply by motor imagery and the present result can be used for classifier development and BCI use in the field of motor restoration

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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. The BCI system enables people to write and type letters by their brain activity (EEG). To this end, first part of the thesis is dedicated to EEG source reconstruction techniques to select the most optimal EEG channels for task classification purposes. Due to this reason the changes in EEG signal power from rest state to motor imagery task was used, to find the location of an active single equivalent dipole. Implementing an inverse problem solution on the power changes by Multiple Sparse Priors (MSP) method generated a scalp map where its fitting showed the localization of EEG electrodes. Having the optimized locations the secondary objective was to choose the most optimal EEG features and rhythm for an efficient classification. This became possible by feature ranking, 1- Nearest Neighbor leave-one-out. The feature vectors were computed by applying the combined methods of multitaper method, Pwelch. The features were classified by several methods of Normal densities based quadratic classifier (qdc), k-nearest neighbor classifier (knn), Mixture of Gaussians classification and Train neural network classifier using back-propagation. Results show that the selected features and classifiers are able to recognize the imagination of writing alphabet with the high accuracy.
BCI controls external devices and interacts with the environment by brain signals. Measured EEG signals over the motor cortex exhibit changes in power related to the movements or imaginations which are executed in motor tasks [1]. These changes declare increase or decrease of power in the alpha (8Hz-13Hz), and beta (13Hz-28Hz) frequency bands from resting state to motor imagery task that known as event related synchronization (in case of power increasing) and desynchronization (in case of power decreasing) [2]. The necessity to communicate with the external world for locked-in state (LIS) patients (a paralyzed patient who only communicates with eyes), made doctors and engineers motivated to develop a BCI technology for typing letters through brain commands. Many researches have been done around this area to ascertain the dream of typing for handicapped. In the brain some regions of the cerebral cortex (motor cortex) are involved in the planning, control, and execution of voluntary movements. Electroencephalography (EEG) signals are electrical potential generated by the nerve cells in the cerebral cortex. In order to execute motoric tasks, the EEG signals are appeared over the motor cortex [1]. The measured brain response to a stimulus is called eventrelated potential (ERP). P300-event related potential (ERP) is an evoked neuron response to an external auditory or visual stimulus that is detectable in scalp-recorded EEG (The P300 is evoked potential which occurs across the parieto-central on the skull 300 ms after applying the stimulus). Farwell and Donchin have proven in a P300-based BCI speller [3] that P300 response is a reliable signal for controlling a BCI system. They described the P300 speller, in which alphanumeric characters are represented in a matrix grid of six-by-six matrix. The user should focus on one of the 36 character cells while each row and column of the grid is intensified randomly and sequentially. The P300, observed in EEG signals, is created by the intersection of the target row and column which causes detection of the target stimuli with a probability of 1/6 (in case of high accuracy of flashing operation). Also when the target stimulus is rarely presented in the random sequence of stimuli causes a neural reaction to unpredictable but recognizable event and a P300 response is evoked [3]. Generally when the subject is involved with the task to recognize the targets, the P300 wave happens and the signal amplitude varies with the unlikelihood of the targets. Its dormancy changes with the difficulty of recognizing the target stimulus from the standard stimuli [3].The attended character of the matrix can be extracted by proper feature extraction and classification of P300. A plenty of procedures for feature extraction and classification have been applied to improve the performance of originally reported speller [3], such as stepwise linear discriminate analysis (SWLDA) [4, 5], wavelets [1], support vector machines [6, 7, 8] and matched filtering [9]. Till now, BCI-related P300 research has mostly considered on signals from standard P300 scalp locations. While in [10, 11, 12, 13, 14, 15, 16] it has been proven that the use of additional locations, especially posterior sites, may improve classification accuracy, but it has not been addressed to particular offline and online studies. Recently, auditory version improvement of the visual P300 speller allows locked in patients who have problem in the visual system to use the P300 speller system by relating two numbers to each letter which indicate the row and column of letter position [17]. Now a new technology is needed which can substitute a keyboard with no alphabet menu. The technology will be handy for blind people and useful for healthy persons who need to work hands free with their computer or mobile. The aim of this thesis is to improve EEG detection through source localization for a new BCI application to type with EEG signals without using alphabet menu.
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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 these methods and we give numerous examples with real data pertaining to our published studies. Traditionally only the single-subject scenario is considered; here we consider in addition the extension of some methods to the simultaneous multi-subject recording scenario. This HDR can be seen as an handbook for EEG source analysis. It will be particularly useful to students and other colleagues approaching the field.
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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 discrete wavelet transform (DWT) for decomposing the sleep EEGs using mother wavelet Daubechies 2 „db2“ are shown and the level of the seven. The classification of the resulting data was used feedforward neural network with backpropagation errors.
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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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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 variations in the artefact voltages give rise to significant residual artefacts after correction, which can easily swamp signals from brain activity. Therefore any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, and facilitating improved detection of brain activity. This thesis firstly explores a new method for reducing the gradient artefact (GA), which is induced in EEG data recorded during concurrent MRI, by investigating the effects of the cable configuration on the characteristics of the GA. This work showed that the GA amplitude and its sensitivity to movement of the cabling is reduced by minimising wire loop areas in the cabling between the EEG cap and amplifier. Another novel approach for reducing the magnitude and variability of the artefacts is the use of an EEG cap that incorporates electrodes embedded in a reference layer, which has a similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads are theoretically similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Therefore taking the difference of the voltages in the reference and scalp channels should reduce the artefacts, without affecting sensitivity to neuronal signals. The theoretical efficacy of artefact correction that can be achieved by using this new reference layer artefact subtraction (RLAS) method was investigated. This was done through separate electromagnetic simulations of the artefacts induced in a hemispherical reference layer and a spherical volume conductor in a time-varying magnetic field and the results showed that similar artefacts are induced on the surface of both conductors. Simulations are also performed to find the optimal design for an RLAS system, by varying the geometry of the system. A simple experimental realisation of the RLAS system was implemented to investigate the degree of artefact attenuation that can be achieved via RLAS. Through a series of experiments on phantoms and human subjects, it is shown here that RLAS significantly reduces the GA, pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms the standard artefact correction method, average artefact subtraction (AAS), in the removal of the GA and PA when motion is present, while the combination of RLAS and AAS always produces higher artefact attenuation than AAS alone. Additionally, this work demonstrates that RLAS greatly attenuates the unpredictable and highly variable MA that are very hard to remove using post-processing methods.
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Books on the topic "Eeg"

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Lutzenberger, Werner, Thomas Elbert, Brigitte Rockstroh, and Niels Birbaumer. Das EEG. Berlin, Heidelberg: 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. Berlin, Heidelberg: 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. Cham: 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). [Bethesda, Md.?]: Clinical Center Communications, National Institutes of Health, 1989.

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

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

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

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

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

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Kursawe, Hubertus. Übungsbuch Klinisches EEG. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56756-2.

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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, 499–508. Cham: 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, Martina de Zwaan, Sarah Parylak, Pietro Cottone, Eric P. Zorrilla, et al. "EEG." In Encyclopedia of Psychopharmacology, 456. Berlin, Heidelberg: 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, Ding Ding, C. Renn Upchurch Sweeney, Colin D. Butler, Colin L. Soskolne, et al. "EEG." In Encyclopedia of Behavioral Medicine, 656. New York, NY: 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, 57–60. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55862-2_11.

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Luque, David. "EEG." In Encyclopedia of Animal Cognition and Behavior, 1–2. Cham: 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, 2217–19. Cham: 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, Gerhard-Paul Diller, Michael A. Gatzoulis, John-John B. Schnog, Victor E. A. Gerdes, et al. "EEG." In Encyclopedia of Molecular Mechanisms of Disease, 565. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-29676-8_6573.

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Gupta, Rashmi, Sonu Purohit, and Jeetendra Kumar. "EEG." In Computational Techniques in Neuroscience, 83–100. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003398066-5.

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Villringer, Arno, Christoph Mulert, and Louis Lemieux. "Principles of Multimodal Functional Imaging and Data Integration." In EEG - fMRI, 3–17. Berlin, Heidelberg: 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, 173–99. Berlin, Heidelberg: 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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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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Schuster, Timo, Sascha Gruss, Henrik Kessler, Andreas Scheck, Holger Hoffmann, and Harald Traue. "EEG." In the 3rd International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1839294.1839374.

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Yaacob, Sazali, Nur Afrina Izzati Affandi, Pranesh Krishnan, Amir Rasyadan, Muhyi Yaakop, and Firdaus Mohamed. "Drowsiness detection using EEG and ECG signals." In 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). IEEE, 2020. http://dx.doi.org/10.1109/iicaiet49801.2020.9257867.

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Martins, Raul Carneiro, Diogo Primor, and Teresa Paiva. "High-performance groundless EEG/ECG capacitive electrodes." In 2011 IEEE International Symposium on Medical Measurements and Applications (MeMeA 2011). IEEE, 2011. http://dx.doi.org/10.1109/memea.2011.5966749.

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"Session MP7b: ECG and EEG signal processing." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421201.

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Sullivan, Thomas J., Stephen R. Deiss, and Gert Cauwenberghs. "A Low-Noise, Non-Contact EEG/ECG Sensor." In 2007 IEEE Biomedical Circuits and Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/biocas.2007.4463332.

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Bermudez, Thomas, David Lowe, and Anne-Marie Arlaud-Lamborelle. "EEG/ECG information fusion for epileptic event detection." In 2009 16th International Conference on Digital Signal Processing (DSP). IEEE, 2009. http://dx.doi.org/10.1109/icdsp.2009.5201231.

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Preez, C. c. Du, S. Sinha, and M. Du Plessis. "CMOS ECG, EEG and EMG Waveform Bio-Simulator." In 2006 International Semiconductor Conference. IEEE, 2006. http://dx.doi.org/10.1109/smicnd.2006.283925.

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Pange, Sanchita, and Vijaya Pawar. "Depression Analysis Based on EEG and ECG Signals." In 2023 4th International Conference for Emerging Technology (INCET). IEEE, 2023. http://dx.doi.org/10.1109/incet57972.2023.10170067.

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Reports on the topic "Eeg"

1

Morton, Paul E., and Glenn F. Wilson. Backpropagation and EEG Data. Fort Belvoir, VA: Defense Technical Information Center, October 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), February 2009. http://dx.doi.org/10.2172/978914.

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Peterson, Matthew S. Electroencephalogy (EEG) Feedback in Decision-Making. Fort Belvoir, VA: Defense Technical Information Center, July 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), April 1995. http://dx.doi.org/10.2172/366563.

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Samaras, George M. Development of an EEG Artifact Correction Device. Fort Belvoir, VA: Defense Technical Information Center, April 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), July 1997. http://dx.doi.org/10.2172/554813.

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Oh, Keunyoung. EEG/ERP Research in Consumer Perceptions of Apparel Products. Ames: Iowa State University, Digital Repository, November 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. Fort Belvoir, VA: Defense Technical Information Center, May 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), December 2002. http://dx.doi.org/10.2172/1184414.

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

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