Relevant bibliographies by topics / Electroencephalography

Academic literature on the topic 'Electroencephalography'

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Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Electroencephalography"

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Bottros, Michael M., Ben Julian A. Palanca, George A. Mashour, Ami Patel, Catherine Butler, Amanda Taylor, Nan Lin, and Michael S. Avidan. "Estimation of the Bispectral Index by Anesthesiologists." Anesthesiology 114, no. 5 (May 1, 2011): 1093–101. http://dx.doi.org/10.1097/aln.0b013e31820e7c5c.

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Background Processed electroencephalographic indices, such as the bispectral index (BIS), are potential adjuncts for assessing anesthetic depth. While BIS® monitors might aid anesthetic management, unprocessed or nonproprietary electroencephalographic data may be a rich source of information for clinicians. We hypothesized that anesthesiologists, after training in electroencephalography interpretation, could estimate the index of a reference BIS as accurately as a second BIS® monitor (twin BIS®) (Covidien Medical, Boulder, CO) when provided with clinical and electroencephalographic data. Methods Two sets of electrodes connected to two separate BIS® monitors were placed on the foreheads of 10 surgical patients undergoing general anesthesia. Electroencephalographic parameters, vital signs, and end-tidal anesthetic gas concentrations were recorded at prespecified time points, and were provided to two sets of anesthesiologists. Ten anesthesiologists received brief structured training in electroencephalograph interpretation and 10 were untrained. Although electroencephalographic waveforms and open-source processed electroencephalograph metrics were provided from the reference BIS®, both groups were blinded to BIS values and were asked to estimate BIS. Results The trained anesthesiologists averaged as close to or closer to the reference BIS® compared with the twin BIS® monitor for 34% of their BIS estimates versus 26% for the untrained anesthesiologists. Using linear mixed effects model analysis, there was a statistically significant difference between the trained and untrained anesthesiologists (P = 0.02), but no difference between the twin BIS® monitor and trained anesthesiologists (P = 0.9). Conclusion With limited electroencephalography training and access to clinical data, anesthesiologists can estimate the BIS almost as well as a second BIS® monitor. These results reinforce the potential utility of training anesthesia practitioners in unprocessed electroencephalogram interpretation.
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Diab, Eva, Michel Lefranc, Bertille Perin, and William Szurhaj. "Delayed intracerebral hemorrhage during stereo-electroencephalography: Electroencephalographic pattern." Neurophysiologie Clinique 52, no. 2 (April 2022): 178–81. http://dx.doi.org/10.1016/j.neucli.2021.12.002.

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Moore, Michael P., Stephen A. Greene, Robert D. Keegan, LeRoy Gallagher, Patrick R. Gavin, Susan L. Kraft, Constance DeHaan, and Kurt Klappenbach. "Quantitative electroencephalography in dogs anesthetized with 2.0% end-tidal concentration of isoflurane anesthesia." American Journal of Veterinary Research 52, no. 4 (April 1, 1991): 551–60. http://dx.doi.org/10.2460/ajvr.1991.52.04.551.

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SUMMARY Quantitative electroencephalography was assessed in dogs under controlled, 2% end-tidal isoflurane anesthetic conditions, and each variable at each electrode site was tested for normal distribution. With the quantitative electroencephalographic system used, 16 values for each of 21 electrode sites were evaluated. Absolute power ratios also were evaluated. The methods for quantitative electroencephalographic recording and analysis appear to be readily adaptable to the dog. Most of the data do not conform to a normal distribution. Therefore, distribution- free nonparametric statistics should be used when looking for differences under experimental or clinical conditions. Quantitative electroencephalography appears to be a sensitive noninvasive method that could be used to evaluate brain function under anesthetic, clinical, and experimental settings.
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Babintseva, А. G., and D. М. Kostiukova. "APPLICATION OF AMPLITUDE-INTEGRATED ELECTROENCEPHALOGRAPHY IN PATIENTS OF NEONATAL INTENSIVE CARE UNITS." Актуальні проблеми сучасної медицини: Вісник Української медичної стоматологічної академії 23, no. 4 (December 20, 2023): 5–11. http://dx.doi.org/10.31718/2077-1096.23.4.5.

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Intriduction. Amplitude-integrated electroencephalography is a technique for monitoring continuous cerebral function, when electroencephalographic signal is filtered, scaled and compressed in time. Objective of the study is to elucidate peculiarities of interpreting findings of the amplitude-integrated electroencephalography in patients of neonatal intensive care units, and represent a clinical case of establishing diagnosis by using the amplitude-integrated electroencephalography for a critically sick neonate. Materials and methods. A comprehensive literature review was conducted across international and national databases, including Elsevier, PubMed, Medline, Web of Science, Scopus, Cochrane Central Register of Controlled Trials, and Google Scholar, covering the period from 2019 to 2023. The search focused on the keywords "amplitude-integrated electroencephalography" and/or "aEEG" and "neonates" and/or "term infants" and/or "preterm infants." The clinical case presented took place at the neonatal intensive care unit of the Maternity Home "Central Municipal Clinical Hospital", Chernivtsi, Ukraine. Permission for publication was obtained from both parents of the child. Results. The main indications for conducting the amplitude-integrated electroencephalography in neonates include the assessment of the cerebral function and the degree of cerebral damage with hypoxic-ischemic encephalopathy or asphyxia at birth (often combined with therapeutic hypothermia); assessment of sleep-wake cycle; identification of seizures; assessment of cerebral function maturity in preterm neonates. The underlying cerebral activity is estimated according to the amplitude-integrated electroencephalography findings by means of a simple recognition of visual images corresponding to the five main patterns: Continuous Normal Voltage, Discontinuous Normal Voltage, Burst Suppression, Low Voltage, and Flat Trace. A typical neonatal single seizure fit on the amplitude-integrated electroencephalography looks like a “hump” or lower edge elevation interrupting the background recording. Repeated fits (epileptic status) look like a “saw” of repeated “humps”, one of each represents one attack. The article presents a clinical case of the diagnostic search in the neonate with congenital pneumonia and development of multiple organ failure syndrome including seizure syndrome. Clinical tonic-clonic seizures of the infant were associated with electroencephalographic criteria of status epilepticus both on the amplitude-integrated electroencephalography (repeated symmetrical elevations of the lower edge in the left and right) and on the standard electroencephalography (different variations of seizure graphic elements in the right and left). Considering the results of the clinical and instrumental examination, an adequate anticonvulsant therapy was administered. Conclusions. Continuous recording of the video-amplitude-integrated electroencephalography and standard electroencephalography in infants from the risk group is a good strategy of the neurological status effective monitoring. It enables to assess the underlying cerebral electric activity and it maturity, diagnose seizures and manage anticonvulsant therapy correctly. An adequate training of the staff who are at the patient’s bedside 24/7 is an important part of the interdisciplinary collaboration which is essential for a safe and effective management of patients in the neonatal intensive care units, prevention of early complications and disability in the future.
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Jamal, G. A. "Electroencephalography." Journal of Neurology, Neurosurgery & Psychiatry 51, no. 9 (September 1, 1988): 1247–48. http://dx.doi.org/10.1136/jnnp.51.9.1247-a.

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Binnie, C. D., and P. F. Prior. "Electroencephalography." Journal of Neurology, Neurosurgery & Psychiatry 57, no. 11 (November 1, 1994): 1308–19. http://dx.doi.org/10.1136/jnnp.57.11.1308.

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Biasiucci, Andrea, Benedetta Franceschiello, and Micah M. Murray. "Electroencephalography." Current Biology 29, no. 3 (February 2019): R80—R85. http://dx.doi.org/10.1016/j.cub.2018.11.052.

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D'Souza, Delon, Gosala R. K. Sarma, and Elizabeth V. T. "Teaching Electroencephalography: Persistent Altered Sensorium with Ominous Appearing Electroencephalographic Activity." International Journal of Epilepsy 05, no. 02 (October 2018): 110–11. http://dx.doi.org/10.1055/s-0038-1676560.

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AbstractA 51-year-old man presented with persistent altered sensorium following a seizure. His magnetic resonance imaging (MRI) showed features of focal encephalitis involving the left temporal, parietal, and occipital regions. His electroencephalogram (EEG) showed ongoing epileptiform discharges over the left hemisphere. This article discusses dilemmas in the diagnosis of nonconvulsive status epilepticus in such a case scenario.
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Sá, Catarina, Paulo Veloso Gomes, António Marques, and António Correia. "The Use of Portable EEG Devices in Development of Immersive Virtual Reality Environments for Converting Emotional States into Specific Commands." Proceedings 54, no. 1 (August 25, 2020): 43. http://dx.doi.org/10.3390/proceedings2020054043.

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The application of electroencephalography electrodes in Virtual Reality (VR) glasses allows users to relate cognitive, emotional, and social functions with the exposure to certain stimuli. The development of non-invasive portable devices, coupled with VR, allows for the collection of electroencephalographic data. One of the devices that embraced this new trend is Looxid LinkTM, a system that adds electroencephalography to HTC VIVETM, VIVE ProTM, VIVE Pro EyeTM, or Oculus Rift STM glasses to create interactive environments using brain signals. This work analyzes the possibility of using the Looxid LinkTM device to perceive, evaluate and monitor the emotions of users exposed to VR.
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Koles, Zoly J. "Western Electroencephalography Society and Southern Electroencephalography Society." Electroencephalography and Clinical Neurophysiology 87, no. 5 (November 1993): P96—P99. http://dx.doi.org/10.1016/0013-4694(93)90195-2.

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Dissertations / Theses on the topic "Electroencephalography"

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Jafaryrabanybastany, Zoya. "Scalp ultra-low frequency electroencephalography." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58417.

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Howell, Stephen John Lamb. "Simultaneous ambulatory cassette electroencephalography and electrocardiography." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316986.

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Gustafsson, Johan. "Finding potential electroencephalography parameters for identifying clinical depression." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-256392.

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This master thesis report describes signal processing parameters of electroencephalography (EEG) signals with a significant difference between the signals from the animal model of clinical depression and the non-depressed animal model. The signal from the depressed model had a weaker power in gamma (30 - 80 Hz) than the non-depressed model during awake and it had a stronger power in delta (1.5 - 4 Hz) during sleep. The report describes the process of using visualisation to understand the shape of the signal which helps with interpreting results and helps with the development of parameters. A generic tool for time-frequency analysis was improved to cope with the size of the weeklong EEG dataset. A method for evaluating the quality of how well the EEG parameters are able to separate the strains with as short recordings as possible was developed. This project shows that it is possible to separate an animal model of depression from an animal model of non-depression based on its EEG and that EEG-classifiers may work as indicative classifiers for depression. Not a lot of data is needed. Further studies are needed to verify that the results are not overly sensitive to recording setup and to study to what extent the results are translational. It might be some of the EEG parameters with significant differences described here are limited to describe the difference between the two strains FSL and SD. But the classifiers have reasonable biological explanations that makes them good candidates for being translational EEG-based classifiers for clinical depression.
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Niazy, Rami. "Simultaneous electroencephalography and functional MRI : methods and applications." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.483692.

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Huang, Dandan. "Electroencephalography (EEG)-based brain computer interfaces for rehabilitation." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2761.

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Objective: Brain-computer interface (BCI) technologies have been the subject of study for the past decades to help restore functions for people with severe motor disabilities and to improve their quality of life. BCI research can be generally categorized by control signals (invasive/non-invasive) or applications (e.g. neuroprosthetics/brain-actuated wheelchairs), and efforts have been devoted to better understand the characteristics and possible uses of brain signals. The purpose of this research is to explore the feasibility of a non-invasive BCI system with the combination of unique sensorimotor-rhythm (SMR) features. Specifically, a 2D virtual wheelchair control BCI is implemented to extend the application of previously designed 2D cursor control BCI, and the feasibility of the prototype is tested in electroencephalography (EEG) experiments; guidance on enhancing system performance is provided by a simulation incorporating intelligent control approaches under different EEG decoding accuracies; pattern recognition methods are explored to provide optimized classification results; and a hybrid BCI system is built to enhance the usability of the wheelchair BCI system. Methods: In the virtual wheelchair control study, a creative and user friendly control strategy was proposed, and a paradigm was designed in Matlab, providing a virtual environment for control experiments; five subjects performed physical/imagined left/right hand movements or non-control tasks to control the virtual wheelchair to move forward, turn left/right or stop; 2-step classification methods were employed and the performance was evaluated by hit rate and control time. Feature analysis and time-frequency analysis were conducted to examine the spatial, temporal and frequency properties of the utilized SMR features, i.e. event-related desynchronization (ERD) and post-movement event-related synchronization (ERS). The simulation incorporated intelligent control methods, and evaluated navigation and positioning performance with/without obstacles under different EEG decoding accuracies, to better guide optimization. Classification methods were explored considering different feature sets, tuned classifier parameters and the simulation results, and a recommendation was provided to the proposed system. In the steady state visual evoked potential (SSVEP) system for hybrid BCI study, a paradigm was designed, and an electric circuit system was built to provide visual stimulus, involving SSVEP as another type of signal being used to drive the EEG BCI system. Experiments were conducted and classification methods were explored to evaluate the system performance. Results: ERD was observed on both hemispheres during hand's movement or motor imagery; ERS was observed on the contralateral hemisphere after movement or motor imagery stopped; five subjects participated in the continuous 2D virtual wheelchair control study and 4 of them hit the target with 100% hit rate in their best set with motor imagery. The simulation results indicated that the average hit rate with 10 obstacles can get above 95% for pass-door tests and above 70% for positioning tests, with EEG decoding accuracies of 70% for Non-Idle signals and 80% for idle signals. Classification methods showed that with properly tuned parameters, an average of about 70%-80% decoding accuracy for all the classifiers could be reached, which reached the requirements set by the simulation test. Initial test on the SSVEP BCI system exhibited high classification accuracy, which may extend the usability of the wheelchair system to a larger population when finally combined with ERD/ERS BCI system. Conclusion: This research investigated the feasibility of using both ERD and ERS associated with natural hand's motor imagery, aiming to implement practical BCI systems for the end users in the rehabilitation stage. The simulation with intelligent controls provided guides and requirements for EEG decoding accuracies, based on which pattern recognition methods were explored; properly selected features and adjusted parameters enabled the classifiers to exhibit optimal performance, suitable for the proposed system. Finally, to enlarge the population for which the wheelchair BCI system could benefit for, a SSVEP system for hybrid BCI was designed and tested. These systems provide a non-invasive, practical approach for BCI users in controlling assistive devices such as a virtual wheelchair, in terms of ease of use, adequate speed, and sufficient control accuracy.
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Sellergren, Albin, Tobias Andersson, and Jonathan Toft. "Signal processing through electroencephalography : Independent project in electrical engineering." Thesis, Uppsala universitet, Elektricitetslära, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-298771.

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This report is about a project where electroencephalography (EEG) wasused to control a two player game. The signals from the EEG-electrodeswere amplified, filtered and processed. Then the signals from the playerswere compared and an algorithm decided what would happen in the gamedepending on which signal was largest. The controls and the gaming mechanismworked as intended, however it was not possible to gather a signal fromthe brain with the method used in this project. So ultimately the goal wasnot reached.
electroencephalography, EEG
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Wakeman, Daniel. "Multi-stage evaluation and improvement of MEEG." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648387.

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Sepeng, Goitsemang Gomolemo. "The Diagnostic outcomes of electroencephalogram performed on adult psychiatric patients at Dr George Mukhari Hospital, Ga-Rankuwa " over a period of January 2006 to December 2008." Thesis, University of Limpopo (Medunsa Campus), 2010. http://hdl.handle.net/10386/455.

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Thesis (M Med (Psychiatry))--University of Limpopo (Medunsa Campus), 2010.
The yield of EEG amongst psychiatric patients has been reported to be low and the value of EEG in the practice of psychiatry is questionable.EEG is used as part of a diagnostic work up for patients with psychiatric disorders .Often the reason given for its use is to exclude epilepsy as a cause of psychiatric symptoms. Epilepsy is primarily a clinical diagnosis, but the EEG may provide strong support by the findings of inter – ictal Epileptogenic discharge METHOD: All the adult EEGs requested at Dr George Mukhari psychiatric hospital, over a 36 month period ,were reviewed to describe the outcome of the requested EEG reports .The study is a simple retrospective analysis of 111 consecutive EEG requested to the department of Neurology at DGMH from psychiatric unit at DGMH. Subjects were both inpatients and outpatients .All the EEG was reported by a qualified Neurologist. Data were extracted from the EEG request form and the patients’ clinical files, which reported on the clinical reason for the EEG test, nature of psychiatric diagnosis of patients, the psychiatric treatment received prior to the EEG test and the nature of the EEG results RESULTS There were 111 EEG reports analysed, and 69 EEG reports for males and 42 EEG reports for females. The reason for EEG request was dominated mainly by exclusion of epilepsy. Majority of the patients were diagnosed with a psychotic disorder , followed second by a mood disorder , all of which was attributed to GMC (epilepsy).About 62.73% of patients were on a combination of treatment of antipsychotic drug and anticonvulsants, whilst 34.55% were on antipsychotic monotherapy prior to the EEG test. Further analysis of the requested EEG form was carried out in whom the test was to determine whether or not the patients were suffering from epilepsy.EEG abnormalities were identified amongst 24% of the patients. About 11,7% of patients presented with non specific EEG results .Out of a total number of 111 patients whom an EEG test was requested and epilepsy was highly suspected from clinical presentation, only 14 patients (12.6%),presented with epileptiform discharge on their EEG results. However majority of the patients (76%) demonstrated normal EEG pattern, which doesn’t exclude a diagnosis of epilepsy. CONCLUSION The yield of EEG in psychiatry is low. To diagnose epilepsy as a cause of psychiatric presentation, clinicians should continue to rely on the clinical history of attacks and not the EEG .In the practice of psychiatry it is not recommended to routinely order an EEG to exclude a diagnosis of epilepsy, more so to confirm a psychiatric diagnosis.The presence of a psychiatric symptoms in patients who presents with epilepsy, is rarely associated with meaningful EEG changes
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Koelstra, Reinder Alexander Lambertus. "Affective and implicit tagging using facial expressions and electroencephalography." Thesis, Queen Mary, University of London, 2012. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8481.

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Recent years have seen an explosion of user-generated, untagged multimedia data, generating a need for efficient search and retrieval of this data. The predominant method for content-based tagging is through manual annotation. Consequently, automatic tagging is currently the subject of intensive research. However, it is clear that the process will not be fully automated in the foreseeable future. We propose to involve the user and investigate methods for implicit tagging, wherein users' responses to the multimedia content are analysed in order to generate descriptive tags. We approach this problem through the modalities of facial expressions and EEG signals. We investigate tag validation and affective tagging using EEG signals. The former relies on the detection of event-related potentials triggered in response to the presentation of invalid tags alongside multimedia material. We demonstrate significant differences in users' EEG responses for valid versus invalid tags, and present results towards single-trial classification. For affective tagging, we propose methodologies to map EEG signals onto the valence-arousal space and perform both binary classification as well as regression into this space. We apply these methods in a real-time affective recommendation system. We also investigate the analysis of facial expressions for implicit tagging. This relies on a dynamic texture representation using non-rigid registration that we first evaluate on the problem of facial action unit recognition. We present results on well-known datasets (with both posed and spontaneous expressions) comparable to the state of the art in the field. Finally, we present a multi-modal approach that fuses both modalities for affective tagging. We perform classification in the valence-arousal space based on these modalities and present results for both feature-level and decision-level fusion. We demonstrate improvement in the results when using both modalities, suggesting the modalities contain complementary information.
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Iranmanesh, Saam. "Wearable electroencephalography for long-term monitoring and diagnostic purposes." Thesis, Imperial College London, 2018. http://hdl.handle.net/10044/1/62277.

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Truly Wearable EEG (WEEG) can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting, miniature and wearable devices that can be easily worn by patients will result in more EEG data being collected for extended monitoring periods. This thesis presents three new fabricated systems, in the form of Application Specific Integrated Circuits (ASICs), to aid the diagnosis of epilepsy and sleep disorders by detecting specific clinically important EEG events on the sensor node, while discarding background activity. The power consumption of the WEEG monitoring device incorporating these systems can be reduced since the transmitter, which is the dominating element in terms of power consumption, will only become active based on the output of these systems. Candidate interictal activity is identified by the developed analog-based interictal spike selection system-on-chip (SoC), using an approximation of the Continuous Wavelet Transform (CWT), as a bandpass filter, and thresholding. The spike selection SoC is fabricated in a 0.35 μm CMOS process and consumes 950 nW. Experimental results reveal that the SoC is able to identify 87% of interictal spikes correctly while only transmitting 45% of the data. Sections of EEG data containing likely ictal activity are detected by an analog seizure selection SoC using the low complexity line length feature. This SoC is fabricated in a 0.18 μm CMOS technology and consumes 1.14 μW. Based on experimental results, the fabricated SoC is able to correctly detect 83% of seizure episodes while transmitting 52% of the overall EEG data. A single-channel analog-based sleep spindle detection SoC is developed to aid the diagnosis of sleep disorders by detecting sleep spindles, which are characteristic events of sleep. The system identifies spindle events by monitoring abrupt changes in the input EEG. An approximation of the median frequency calculation, incorporated as part of the system, allows for non-spindle activity incorrectly identified by the system as sleep spindles to be discarded. The sleep spindle detection SoC is fabricated in a 0.18 μm CMOS technology, consuming only 515 nW. The SoC achieves a sensitivity and specificity of 71.5% and 98% respectively.
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Books on the topic "Electroencephalography"

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Mecarelli, Oriano, ed. Clinical Electroencephalography. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04573-9.

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Clancy, Robert R. Neonatal electroencephalography. Amsterdam: Elsevier, 1993.

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Madeleine, Couture, ed. Atlas of electroencephalography. Boston: Little, Brown, 1989.

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Sperling, Michael R. Pediatric and adult electroencephalography. Amsterdam: Elsevier, 1993.

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Mizrahi, Eli M. Atlas of neonatal electroencephalography. 3rd ed. Philadelphia: Lippincott Williams & Wilkins, 2004.

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Stockard-Pope, Janet E. Atlas of neonatal electroencephalography. 2nd ed. New York: Raven Press, 1992.

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Mizrahi, Eli M. Atlas of neonatal electroencephalography. New York, NY: Demos Medical Publishing, LLC, 2016.

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Masako, Kaibara, ed. Atlas of pediatric electroencephalography. 2nd ed. Philadelphia, Pa: Lippincott-Raven, 1999.

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Masako, Kaibara, ed. Atlas of adult electroencephalography. New York: Raven Press, 1995.

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Boutros, Nash, Silvana Galderisi, Oliver Pogarell, and Silvana Riggio, eds. Standard Electroencephalography in Clinical Psychiatry. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470974612.

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Book chapters on the topic "Electroencephalography"

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Ivanitsky, Alexey M., Andrey R. Nikolaev, and George A. Ivanitsky. "Electroencephalography." In Modern Techniques in Neuroscience Research, 971–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58552-4_35.

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Michels, L. "Electroencephalography." In Neuroimaging Techniques in Clinical Practice, 313–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48419-4_21.

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Weintraub, Alan, and John Whyte. "Electroencephalography." In Encyclopedia of Clinical Neuropsychology, 1282–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-57111-9_24.

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Ellenbroek, Bart, Alfonso Abizaid, Shimon Amir, Martina de Zwaan, Sarah Parylak, Pietro Cottone, Eric P. Zorrilla, et al. "Electroencephalography." In Encyclopedia of Psychopharmacology, 462–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_288.

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Weintraub, Alan, and John Whyte. "Electroencephalography." In Encyclopedia of Clinical Neuropsychology, 1–3. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-56782-2_24-3.

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Drinkenburg, Wilhelmus H. I. M. "Electroencephalography." In Encyclopedia of Psychopharmacology, 592–602. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-36172-2_7014.

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Suzuki, Jiro. "Electroencephalography." In Moyamoya Disease, 63–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-95483-2_5.

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van Putten, Michel J. A. M. "Electroencephalography." In Series in Biomedical Engineering, 147–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-69890-6_8.

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Charman, Tony, Susan Hepburn, Moira Lewis, Moira Lewis, Amanda Steiner, Sally J. Rogers, Annemarie Elburg, et al. "Electroencephalography." In Encyclopedia of Autism Spectrum Disorders, 1068. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_722.

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Hess, R. "Electroencephalography." In Antiepileptic Drugs, 35–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-69518-6_2.

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Conference papers on the topic "Electroencephalography"

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Schmitt, Hans J. "History of electroencephalography." In 2008 IEEE History of Telecommunications Conference - "From Semaphone to Cellular Radio Telecommunications". IEEE, 2008. http://dx.doi.org/10.1109/histelcon.2008.4668719.

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Yew, Chuah Eng, and Desmond Tan Mun Yung. "Electroencephalography Robotic Arm Control." In 2018 IEEE 4th International Symposium in Robotics and Manufacturing Automation (ROMA). IEEE, 2018. http://dx.doi.org/10.1109/roma46407.2018.8986734.

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Vo, Tien Hoang-Thuy, Tran Luu-Nha Dang, Ngan Vuong-Thuy Nguyen, and Tuan Van Huynh. "Classification Electroencephalography Using Machine Learning." In 2019 19th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2019. http://dx.doi.org/10.1109/iscit.2019.8905225.

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Wetzel, Dominik, Nico Spahn, Martin Heilemann, Marcus M. Loffler, Markus Seidel, Silke Kolbig, and Dirk Winkler. "Evaluation of electroencephalography analysis methods." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8857230.

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Albano, A. M., R. B. Duckrow, Christopher C. Bernido, and M. Victoria Carpio-Bernido. "Chaos, Boltzmann, Shannon and Electroencephalography." In STOCHASTIC AND QUANTUM DYNAMICS OF BIOMOLECULAR SYSTEMS: Proceedings of the 5th Jagna International Workshop. AIP, 2008. http://dx.doi.org/10.1063/1.2956804.

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Nguyen, Vuong-Thuy-Ngan, Van-Tuan Huynh, and Thi-Hong-Hanh Nguyen. "Electroencephalography Analysis Using Neural Network." In 2018 5th NAFOSTED Conference on Information and Computer Science (NICS). IEEE, 2018. http://dx.doi.org/10.1109/nics.2018.8606797.

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Wu, Jerry, Harold Szu, Yuechen Chen, Ran Guo, and Xixi Gu. "Spatially revolved high density electroencephalography." In SPIE Sensing Technology + Applications, edited by Harold H. Szu, Liyi Dai, and Yufeng Zheng. SPIE, 2015. http://dx.doi.org/10.1117/12.2184655.

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Choi, Ga-Young, Chang-Hee Han, Hyunmi Lim, Jeonghun Ku, Won-Seok Kim, and Han-Jeong Hwang. "Electroencephalography-based Motor Hotspot Detection." In 13th International Conference on Bio-inspired Systems and Signal Processing. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008937201950198.

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Choi, Ga-Young, Chang-Hee Han, Hyunmi Lim, Jeonghun Ku, Won-Seok Kim, and Han-Jeong Hwang. "Electroencephalography-based Motor Hotspot Detection." In 13th International Conference on Bio-inspired Systems and Signal Processing. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008937200002513.

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Lainscsek, Claudia, Manuel E. Hernandez, Howard Poizner, and Terrence J. Sejnowski. "Multivariate spectral analysis of electroencephalography data." In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2013. http://dx.doi.org/10.1109/ner.2013.6696142.

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Reports on the topic "Electroencephalography"

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Engheta, Nader, Edward N. Pugh, and Jr. Selected Electromagnetic Problems in Electroencephalography (EEG) Fields in Complex Media and Small Radiating Elements in Dissipative Media. Fort Belvoir, VA: Defense Technical Information Center, November 2004. http://dx.doi.org/10.21236/ada428876.

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Whitaker, Keith W., and W. D. Hairston. Assessing the Minimum Number of Synchronization Triggers Necessary for Temporal Variance Compensation in Commercial Electroencephalography (EEG) Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada568650.

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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
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Rawal, Sandhya. Weighted Phase Lag Index (WPLI) as a Method for Identifying Task-Related Functional Networks in Electroencephalography (EEG) Recordings during a Shooting Task. Fort Belvoir, VA: Defense Technical Information Center, August 2011. http://dx.doi.org/10.21236/ada558399.

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Stefanova, Irina, Rumyana Kuzmanova, Sevda Naydenska, and Katerina Stambolieva. Character of Epileptic Seizures and Electroencephalographic Changes in Patients with Epilepsy and Comorbid Diseases. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, July 2018. http://dx.doi.org/10.7546/crabs.2018.07.16.

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EEG data might help identify children at risk for social anxiety. ACAMH, March 2021. http://dx.doi.org/10.13056/acamh.15048.

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Electroencephalography (EEG) is a non-invasive method to monitor the electrical activity of the brain. There are five main broad frequency bands in the EEG power spectrum: alpha, beta, gamma, delta and theta. Data suggest that EEG-derived delta–beta coupling — indicating related activity in the delta and beta frequency bands — might serve as a marker of emotion regulation.
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