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Artículos de revistas sobre el tema "Clinical EEG data"

Autor: Grafiati

Publicado: 1 de junio de 2024

Última modificación: 31 de julio de 2025

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1

Antony, Mary Judith, Baghavathi Priya Sankaralingam, Shakir Khan, Abrar Almjally, Nouf Abdullah Almujally, and Rakesh Kumar Mahendran. "Brain–Computer Interface: The HOL–SSA Decomposition and Two-Phase Classification on the HGD EEG Data." Diagnostics 13, no. 17 (2023): 2852. http://dx.doi.org/10.3390/diagnostics13172852.

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An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain–Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful

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2

Járdánházy, T., I. Somogyi, and T. Asztalos. "Compression methods for EEG spectral data." Electroencephalography and Clinical Neurophysiology 87, no. 2 (1993): S133. http://dx.doi.org/10.1016/0013-4694(93)91489-n.

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3

Banquet, J. P., W. Guenther, and D. Breitling. "Multidimensional factorial methods for EEG data." Electroencephalography and Clinical Neurophysiology 61, no. 3 (1985): S231. http://dx.doi.org/10.1016/0013-4694(85)90874-0.

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4

Gu, Yuqiao, Geir Halnes, Hans Liljenström, and Björn Wahlund. "A cortical network model for clinical EEG data analysis." Neurocomputing 58-60 (June 2004): 1187–96. http://dx.doi.org/10.1016/j.neucom.2004.01.184.

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5

Goldenholz, Daniel M., Joseph J. Tharayil, Rubin Kuzniecky, Philippa Karoly, William H. Theodore, and Mark J. Cook. "Simulating clinical trials with and without intracranial EEG data." Epilepsia Open 2, no. 2 (2017): 156–61. http://dx.doi.org/10.1002/epi4.12038.

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6

Ivanov, А. А. "Overview of mathematical EEG analysis. Quantitative EEG." Epilepsy and paroxysmal conditions 15, no. 2 (2023): 171–92. http://dx.doi.org/10.17749/2077-8333/epi.par.con.2023.154.

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The purpose of this article is to familiarize medical specialists involved in registration and analysis of electroencephalographic (EEG) studies using methods of mathematical processing and analysis for recorded EEG data. Understanding the principles of how quantitative EEG analysis tools work should help medical personnel to properly use their capabilities and ultimately improve quality of medical care. Here, we discuss basic and innovative mathematical tools for EEG processing and analysis.

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7

Salam, Abdus, Selina Husna Banu, Abu Nayeem, and Zobaida Sultana Susan. "Clinical Finding of Electroencephalographic (EEG) Data in Adults: A Retrospective study." Journal of Shaheed Suhrawardy Medical College 6, no. 1 (2017): 14–17. http://dx.doi.org/10.3329/jssmc.v6i1.31486.

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Background: Electroencephalography (EEG) is the first and only real-time monitor of epileptic seizures, and is a powerful measure of cerebral function in the seriously ill.Objective: The purpose of this study was to see the common conditions for doing EEG.Methods: This study was performed retrospectively by collecting and reviewing the electro-clinical information of the adult patients to whom EEGs were done at the 'Central Hospital' laboratory. Clinical problems were categorized into seizures, fainting attacks, headache, giddiness, vertigo, stroke, suicidal tendency, sudden aggressiveness and

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8

Cisotto, Giulia, and Davide Chicco. "Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing." PeerJ Computer Science 10 (September 3, 2024): e2256. http://dx.doi.org/10.7717/peerj-cs.2256.

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Electroencephalography (EEG) is a medical engineering technique aimed at recording the electric activity of the human brain. Brain signals derived from an EEG device can be processed and analyzed through computers by using digital signal processing, computational statistics, and machine learning techniques, that can lead to scientifically-relevant results and outcomes about how the brain works. In the last decades, the spread of EEG devices and the higher availability of EEG data, of computational resources, and of software packages for electroencephalography analysis has made EEG signal proce

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9

Cincotti, F., C. Babiloni, C. Miniussi, et al. "EEG Deblurring Techniques in a Clinical Context." Methods of Information in Medicine 43, no. 01 (2004): 114–17. http://dx.doi.org/10.1055/s-0038-1633846.

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Summary Objectives: EEG scalp potential distributions recorded in humans are affected by low spatial resolution and by the dependence on the electrical reference used. High resolution EEG technologies are available to drastically increase the spatial resolution of the raw EEG. Such technologies include the computation of surface Laplacian (SL) of the recorded potentials, as well as the use of realistic head models to estimate the cortical sources via linear inverse procedure (low resolution brain electromagnetic tomography, LORETA). However, these deblurring procedures are generally used in co

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10

Kutafina, Ekaterina, Alexander Brenner, Yannic Titgemeyer, Rainer Surges, and Stephan Jonas. "Comparison of mobile and clinical EEG sensors through resting state simultaneous data collection." PeerJ 8 (May 1, 2020): e8969. http://dx.doi.org/10.7717/peerj.8969.

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Development of mobile sensors brings new opportunities to medical research. In particular, mobile electroencephalography (EEG) devices can be potentially used in low cost screening for epilepsy and other neurological and psychiatric disorders. The necessary condition for such applications is thoughtful validation in the specific medical context. As part of validation and quality assurance, we developed a computer-based analysis pipeline, which aims to compare the EEG signal acquired by a mobile EEG device to the one collected by a medically approved clinical-grade EEG device. Both signals are

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11

Noachtar, Soheyl, Jan Remi, and Elisabeth Kaufmann. "EEG-Update." Klinische Neurophysiologie 53, no. 04 (2022): 243–52. http://dx.doi.org/10.1055/a-1949-1691.

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Durch die rasante Entwicklung digitaler Computertechniken und neuer Analysemethoden hat sich ein neuer Ansatz zur Analyse der Hirnströme (quantitatives EEG) ergeben, die in verschiedenen klinischen Bereichen der Neurologie und Psychiatrie bereits Ergebnisse zeigen. Die neuen Möglichkeiten der Analyse des EEG durch Einsatz künstlicher Intelligenz (Deep Learning) und großer Datenmengen (Big Data) sowie telemedizinischer Datenübermittlung und Interaktion wird den Einsatz der Methode vermutlich in den nächsten Jahren erweitern.

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12

Adarsh H, Anees Basheer, Nithin Nandakumar, Sidharth H Kurup, and Aby Rose Varghese. "Deep Learning Techniques for Analyzing EEG Data." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 12 (2024): 3591–96. https://doi.org/10.47392/irjaem.2024.0529.

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Deep Learning models’ applicability to the prediction and classification of EEG-seizures has certainly been a challenge to researchers and academia. This paper examines the relation of deep learning approaches and the decoding of seizure events building upon the vast amount of empirical data present in the EEG recordings of the epilepsy patients. We are able to discover several prospects of using deep learning in seizure prediction models by understanding the aspects of signal processing, classification accuracy, and clinical relevance. Our conclusions point out that more than deep learning mo

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13

da Silva Lourenço, Catarina, Marleen C. Tjepkema-Cloostermans, and Michel J. A. M. van Putten. "Efficient use of clinical EEG data for deep learning in epilepsy." Clinical Neurophysiology 132, no. 6 (2021): 1234–40. http://dx.doi.org/10.1016/j.clinph.2021.01.035.

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14

Natale, E., A. Mattaliano, G. Alia, M. G. Perpero, and O. Daniele. "‘Alpha pattern’ coma: Clinical and EEG data, aetiology, evolution and prognosis." Electroencephalography and Clinical Neurophysiology 75 (January 1990): S103. http://dx.doi.org/10.1016/0013-4694(90)92091-a.

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15

Gastaut, H., N. Pinsard, C. Raybaud, and B. Zifkin. "Clinical data, EEG patterns, and CT scanning in the lissencephaly syndrome." Electroencephalography and Clinical Neurophysiology 61, no. 3 (1985): S168. http://dx.doi.org/10.1016/0013-4694(85)90646-7.

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16

Swart, M. D., E. J. Jonkman, and A. W. de Weerd. "P451 Late changes after stroke: Clinical, EEG, and CT-scan data." Electroencephalography and Clinical Neurophysiology 99, no. 4 (1996): 379. http://dx.doi.org/10.1016/0013-4694(96)88626-3.

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17

Hassan, Ala Farid Zaineb Adil Yakob Haithem Jawad Kadhum Ali Raheem Hashim Nareen Haikaz Hasrat Nazik Haikaz Hasrat. "Evaluation Of The Knowledge Of Medical Doctors, Graduates, And Students About The Field Of Clinical Neurophysiology." Multicultural Education 7, no. 8 (2021): 318. https://doi.org/10.5281/zenodo.5209387.

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<em>Clinical neurophysiology is a branch of medicine that analyses the central and peripheral nervous systems using bioelectrical activity recordings. Electromyography and nerve conduction tests, Electroencephalography, Evoked potentials, and Polysomnography are among the most common diagnostic modalities used. Clinical neurophysiology is a new subspecialty of neurology and physiology in Iraq. Clinical neurophysiology is still underrepresented in medicine, and there is a paucity of useful understanding and education in this discipline. A survey structure by using google form is created and 330

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18

Glaser, J., V. Schöpf, R. Beisteiner, H. Bauer, and F. Fischmeister. "Optimum gradient artifact removal from EEG-data using facet." Journal of the Neurological Sciences 333 (October 2013): e622. http://dx.doi.org/10.1016/j.jns.2013.07.2164.

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19

Kemp, Bob, Teunis van Beelen, Marion Stijl, Paul van Someren, Marco Roessen, and J. Gert van Dijk. "A DC attenuator allows common EEG equipment to record fullband EEG, and fits fullband EEG into standard European Data Format." Clinical Neurophysiology 121, no. 12 (2010): 1992–97. http://dx.doi.org/10.1016/j.clinph.2010.05.006.

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20

Klotz, Jürgen Michael. "Topographic EEG Mapping Methods." Cephalalgia 13, no. 1 (1993): 45–52. http://dx.doi.org/10.1046/j.1468-2982.1993.1301045.x.

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After almost 40 years of research on EEG computer analysis, present clinical applications of this method remain limited. At the present time, EEG mapping is suited primarily for research. Despite the pitfalls of an uncritical application of EEG mapping, progress in clinical research made possible by EEG mapping techniques has been considerable. Some problems of data acquisition, display and statistical analysis are discussed in this paper. For headache research examination of the activated EEG, especially with photic stimulation, has greater diagnostic importance than mapping under resting con

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21

Krystal, Andrew D., Henry S. Greenside, Paul S. Rapp, Alfonso Albano, Chris Cellucci, and Richard D. Weiner. "PARTIAL LEAST SQUARES ANALYSIS OF MULTICHANNEL EEG DATA." Journal of Clinical Neurophysiology 15, no. 3 (1998): 274. http://dx.doi.org/10.1097/00004691-199805000-00028.

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22

Tedrus, Gloria M. A. S. "NCSE-coma in older adults: Clinical data, initial EEG, and hospital outcome." Epilepsy & Behavior 158 (September 2024): 109929. http://dx.doi.org/10.1016/j.yebeh.2024.109929.

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23

Hunter, M., R. L. L. Smith, W. Hyslop, et al. "The Australian EEG Database." Clinical EEG and Neuroscience 36, no. 2 (2005): 76–81. http://dx.doi.org/10.1177/155005940503600206.

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The Australian EEG Database is a web-based de-identified searchable database of 18,500 EEG records recorded at a regional public hospital over an 11-year period. Patients range in age from a premature infant born at 24 weeks gestation, through to people aged over 90 years. This paper will describe the history of the database, the range of patients represented in the database, and the nature of the text-based and digital data contained in the database. Preliminary results of the first two studies undertaken using the database are presented. Plans for sharing data from the Australian EEG databas

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24

Drobný, M., B. Drobná Sániová, S. Učňová, et al. "3D EEG and Clinical Evidence of Brain Dying. Preliminary Report." General Reanimatology 19, no. 1 (2023): 34–42. http://dx.doi.org/10.15360/1813-9779-2023-1-34-42.

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Determination of brain dying means reversible or irreversible injury to the brain, including the brainstem. Current guidelines rely on clinical examination including the proof of coma, absent brain stem reﬂexes, and apnoea test. Neurophysiological testing using electroencephalography and evoked potentials — somatosensory evoked potentials and brainstem auditory evoked potential could have been helpful in the ﬁnal diagnostic brain death conclusion, but the diagnostic accuracy of these methods in the last years has revealed controversies. Here, we present data on quantitative EEG signal evaluati

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25

Kira, Jun-ichi, Sei-ichiro Minato, Yasuto Itoyama, Ikuo Goto, Motohiro Kato, and Kanehiro Hasuo. "Leukoencephalopathy in HTLV-I-associated myelopathy: MRI and EEG data." Journal of the Neurological Sciences 87, no. 2-3 (1988): 221–32. http://dx.doi.org/10.1016/0022-510x(88)90247-x.

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26

Vogrin, Simon J., and Chris Plummer. "EEG Source Imaging—Clinical Considerations for EEG Acquisition and Signal Processing for Improved Temporo-Spatial Resolution." Journal of Clinical Neurophysiology 41, no. 1 (2024): 8–18. http://dx.doi.org/10.1097/wnp.0000000000001023.

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Summary: EEG source imaging (ESI) has gained traction in recent years as a useful clinical tool for the noninvasive surgical work-up of patients with drug-resistant focal epilepsy. Despite its proven benefits for the temporo-spatial modeling of spike and seizure sources, ESI remains widely underused in clinical practice. This partly relates to a lack of clarity around an optimal approach to the acquisition and processing of scalp EEG data for the purpose of ESI. Here, we describe some of the practical considerations for the clinical application of ESI. We focus on patient preparation, the impa

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27

Loula, P., E. Rauhala, M. Erkinjuntti, E. Raty, K. Hirvonen, and V. Hakkinen. "Distributed clinical neurophysiology." Journal of Telemedicine and Telecare 3, no. 2 (1997): 89–95. http://dx.doi.org/10.1258/1357633971930922.

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We have developed a consultation forum for clinical neurophysiology in Finland. The system connects local digital electroencephalography EEG recording and analysing networks using a high-speed asynchronous transfer mode ATM network. Clinicians can obtain a second opinion using interactive data and video consultations or using data-only consultations. In addition, the system can be used for off-line review of prerecorded data. During a one-month evaluation, 66 EEG recordings were made altogether in Satakunta Central Hospital and consultations were required on 12 occasions. Nine of them were dat

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28

Nielsen, Jonas Munch, Ástrós Eir Kristinsdóttir, Ivan Chrilles Zibrandtsen, et al. "Out-of-hospital multimodal seizure detection: a pilot study." BMJ Neurology Open 5, no. 2 (2023): e000442. http://dx.doi.org/10.1136/bmjno-2023-000442.

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BackgroundOut-of-hospital seizure detection aims to provide clinicians and patients with objective seizure documentation in efforts to improve the clinical management of epilepsy. In-patient studies have found that combining different modalities helps improve the seizure detection accuracy. In this study, the objective was to evaluate the viability of out-of-hospital seizure detection using wearable ECG, accelerometry and behind-the-ear electroencephalography (EEG). Furthermore, we examined the signal quality of out-of-hospital EEG recordings.MethodsSeventeen patients were monitored for up to

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Arns, M. "EEG and ECG based response predictors in depression: Time for personalised medicine or treatment stratification?" European Psychiatry 64, S1 (2021): S6—S7. http://dx.doi.org/10.1192/j.eurpsy.2021.40.

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In depression (MDD) treatment there is a clear need for novel treatments, biomarkers and individualized treatment approaches. One of the most promising and most widely investigated biomarkers for antidepressant treatments is the EEG. Most EEG biomarkers however, still lack robustness and reproducibility and suffer significant publication bias as highlighted in a recent meta-analysis (Widge et al., 2018). Therefore, large controlled validation studies are needed with a focus on robustness, replication and clinical relevance. In this presentation results will be presented from the largest EEG Bi

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30

Sarrigiannis, Ptolemaios G., Yifan Zhao, Hua-Liang Wei, Stephen A. Billings, Jayne Fotheringham, and Marios Hadjivassiliou. "Quantitative EEG analysis using error reduction ratio-causality test; validation on simulated and real EEG data." Clinical Neurophysiology 125, no. 1 (2014): 32–46. http://dx.doi.org/10.1016/j.clinph.2013.06.012.

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31

Birvinskas, Darius, Vacius Jusas, Ignas Martisius, and Robertas Damasevicius. "Fast DCT algorithms for EEG data compression in embedded systems." Computer Science and Information Systems 12, no. 1 (2015): 49–62. http://dx.doi.org/10.2298/csis140101083b.

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Electroencephalography (EEG) is widely used in clinical diagnosis, monitoring and Brain - Computer Interface systems. Usually EEG signals are recorded with several electrodes and transmitted through a communication channel for further processing. In order to decrease communication bandwidth and transmission time in portable or low cost devices, data compression is required. In this paper we consider the use of fast Discrete Cosine Transform (DCT) algorithms for lossy EEG data compression. Using this approach, the signal is partitioned into a set of 8 samples and each set is DCT-transformed. Th

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32

Matsuo, Fumisuke. "Rapid Scanning of EEG Data in Long-Term Monitoring." Journal of Clinical Neurophysiology 5, no. 4 (1988): 336. http://dx.doi.org/10.1097/00004691-198810000-00032.

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33

Chen, Hsin Yi, Jonathan Elmer, Sahar F. Zafar, et al. "Combining Transcranial Doppler and EEG Data to Predict Delayed Cerebral Ischemia After Subarachnoid Hemorrhage." Neurology 98, no. 5 (2021): e459-e469. http://dx.doi.org/10.1212/wnl.0000000000013126.

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Background and ObjectivesDelayed cerebral ischemia (DCI) is the leading complication of subarachnoid hemorrhage (SAH). Because DCI was traditionally thought to be caused by large vessel vasospasm, transcranial Doppler ultrasounds (TCDs) have been the standard of care. Continuous EEG has emerged as a promising complementary monitoring modality and predicts increased DCI risk. Our objective was to determine whether combining EEG and TCD data improves prediction of DCI after SAH. We hypothesize that integrating these diagnostic modalities improves DCI prediction.MethodsWe retrospectively assessed

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34

Litovchenko, Tetyana, Olga Sukhonosova, Oleksii Sorochan, Vladlena Salnikova, and Maryna Gekova. "Comparison of clinical, electroencephalographic and tomographic data in children with epilepsy with controlled and uncontrolled seizures." Ukrains'kyi Visnyk Psykhonevrolohii 27, no. 3 (2019): 72–75. http://dx.doi.org/10.36927/2079-0325-v27-is3-2019-13.

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The article presents the results of a examination of 124 children with epilepsy aged 1 month to 18 years to detect clinical electroencephalographic (EEG) and tomographic correlations in patients with controlled and uncontrolled seizures. It was shown that clinical manifestations (seizure types) are not always correlated with local changes on EEG and focus on MRI. In children, especially with uncontrolled seizures, even in the case of a focal onset of the seizure, secondary generalization is often observed, which is due to the functional immaturity of the brain and the tendency to rapid general

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35

Vanhatalo, Sampsa, Juha Voipio, and Kai Kaila. "Full-Band EEG (FbEEG): A New Standard for Clinical Electroencephalography." Clinical EEG and Neuroscience 36, no. 4 (2005): 311–17. http://dx.doi.org/10.1177/155005940503600411.

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A variety of neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET) and magnetoencephalography (MEG), have been established during the last few decades, with progressive improvements continuously taking place in the underlying technologies. In contrast to this, the recording bandwidth of the routine clinical EEG (typically around 0.5–50 Hz) that was originally set by trivial technical limitations has remained practically unaltered for over half a decade. An increasing amount of evidence shows that salient EEG signals take place and can

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Kozinska, D., F. Carducci, and K. Nowinski. "Automatic alignment of EEG/MEG and MRI data sets." Clinical Neurophysiology 112, no. 8 (2001): 1553–61. http://dx.doi.org/10.1016/s1388-2457(01)00556-9.

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Fousek, J. "13. Processing and visualization of high resolution EEG data." Clinical Neurophysiology 125, no. 5 (2014): e29. http://dx.doi.org/10.1016/j.clinph.2013.12.051.

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Whittingstall, Kevin, Gerhard Stroink, and Bruce Dick. "Dipole localization accuracy using grand-average EEG data sets." Clinical Neurophysiology 115, no. 9 (2004): 2108–12. http://dx.doi.org/10.1016/j.clinph.2004.04.004.

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Ratnayake, Chathri, Ramja Kokulan, and Patrick Carney. "115 Is MBS restriction on EEGs truly beneficial? Retrospective observational analysis of EEG referral patterns." Journal of Neurology, Neurosurgery & Psychiatry 90, e7 (2019): A37.2—A37. http://dx.doi.org/10.1136/jnnp-2019-anzan.102.

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IntroductionThe November 2018 Medicare Benefits Schedule (MBS) Taskforce draft report recommends standardised national referral form for routine electroencephalogram (EEG) requests to prevent low value clinician referrals. MBS defines twelve clinical presentations where routine EEG is of relatively low diagnostic value. We aim to identify proportion of MBS defined low diagnostic value EEGs and likely referral patterns.MethodsRetrospective single centre observational study was conducted from January to December 2018. All EEG referrals to a tertiary hospital neurodiagnostic unit were categorised

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Andraus, Maria Emilia Cosenza, Cesar Fantezia Andraus, and Soniza Vieira Alves-Leon. "Periodic EEG patterns: importance of their recognition and clinical significance." Arquivos de Neuro-Psiquiatria 70, no. 2 (2012): 145–51. http://dx.doi.org/10.1590/s0004-282x2012000200014.

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Periodic electroencephalographic (EEG) patterns consist of discharges usually epileptiform in appearance, which occur at regular intervals, in critical patients. They are commonly classified as periodic lateralized epileptiform discharges (PLEDs), bilateral independent PLEDs or BIPLEDs, generalized epileptiform discharges (GPEDs) and triphasic waves. Stimulus-induced rhythmic, periodic or ictal discharges (SIRPIDs) are peculiar EEG patterns, which may be present as periodic discharges. The aim of this study is to make a review of the periodic EEG patterns, emphasizing the importance of their r

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Luccas, Francisco José Carchedi, Thalita Bártolo, Nayara Lucio da Silva, and Barbara Cavenaghi. "Clinical electroencephalogram (EEG) evaluation is improved by the amplitude asymmetry index." Arquivos de Neuro-Psiquiatria 74, no. 7 (2016): 536–43. http://dx.doi.org/10.1590/0004-282x20160082.

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ABSTRACT Cerebral hemispheres, although similar, are neither completely symmetrical in structure nor equivalent in function. EEG asymmetry studies have been directed more to frequency than to amplitude analysis. Objective Better definition of normal amplitude asymmetry values on the classical EEG frequency bands. Results EEG amplitude asymmetry index (AAI) is physiologically low in normal adults, differences usually lesser than 7%. Conclusion Persistent or intermittent amplitude asymmetry regional differences higher than 7% may be suggestive of pathology after adequate correlation with clinica

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Wieser, H. G., S. Hailemariam, M. Regard, and T. Landis. "Unilateral Limbic Epileptic Status Activity: Stereo EEG, Behavioral, and Cognitive Data." Epilepsia 26, no. 1 (1985): 19–29. http://dx.doi.org/10.1111/j.1528-1157.1985.tb05184.x.

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Zhou, Dingfu, Zhihang Liao, and Rong Chen. "Deep Learning Enabled Diagnosis of Children’s ADHD Based on the Big Data of Video Screen Long-Range EEG." Journal of Healthcare Engineering 2022 (April 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/5222136.

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Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children. At the same time, ADHD is prone to coexist with other mental disorders, so the diagnosis of ADHD in children is very important. Electroencephalogram (EEG) is the sum of the electrical activity of local neurons recorded from the extracranial scalp or intracranial. At present, there are two main methods of long-range EEG monitoring commonly used in clinical practice: one is ambulatory EEG monitoring, and the other is long-range video EEG monitoring. The purpose of this study is to summarize the b

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Jiang, Zhen, and Wenshan Zhao. "Fusion Algorithm for Imbalanced EEG Data Processing in Seizure Detection." Seizure 91 (October 2021): 207–11. http://dx.doi.org/10.1016/j.seizure.2021.06.023.

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Hur, Yun Jung, Andrew J. Kim, and Douglas R. Nordli. "MRI supersedes ictal EEG when other presurgical data are concordant." Seizure 53 (December 2017): 18–22. http://dx.doi.org/10.1016/j.seizure.2017.10.013.

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Dasgupta, Abhijit, Losiana Nayak, Ritankar Das, Debasis Basu, Preetam Chandra, and Rajat K. De. "Pattern and Rule Mining for Identifying Signatures of Epileptic Patients from Clinical EEG Data." Fundamenta Informaticae 176, no. 2 (2020): 141–66. http://dx.doi.org/10.3233/fi-2020-1968.

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Epilepsy is a neurological condition of human being, mostly treated based on the patients’ seizure symptoms, often recorded over multiple visits to a health-care facility. The lengthy time-consuming process of obtaining multiple recordings creates an obstacle in detecting epileptic patients in real time. An epileptic signature validated over EEG data of multiple similar kinds of epilepsy cases will haste the decision-making process of clinicians. In this paper, we have identified EEG data derived signatures for differentiating epileptic patients from normal individuals. Here we define the sign

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Buzzi, MG, C. Tassorelli, and G. Nappi. "Peripheral and Central Activation of Trigeminal Pain Pathways in Migraine: Data From Experimental Animal Models." Cephalalgia 23, no. 1_suppl (2003): 1–4. http://dx.doi.org/10.1046/j.1468-2982.23.s1.1.x.

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EEG-studies in migraine in the last decade has contributed modestly to the understanding of headache pathogenesis. Headache patient groups seem to have increased EEG responses to photic stimulation, but a useful biological marker for migraine in single patients has not been found. In future EEG and QEEG studies we recommend to use follow-up designs and record several EEGs across the migraine cycle. It is also important to use a blinded study design in order to avoid selection bias. A clinical EEG should be performed in patients with acute headache attacks when either epilepsy, basilar migraine

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Han, Lina, Liyan Li, Yanyan Chen, et al. "Utilizing Machine Learning Techniques to Enhance Attention-Deficit Hyperactivity Disorder Diagnosis Using Resting-State EEG Data." Journal of Clinical and Nursing Research 9, no. 1 (2025): 209–17. https://doi.org/10.26689/jcnr.v9i1.9505.

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Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Methods: Resting-state EEG recordings were obtained from 57 children, comprising 28 typically developing children and 29 children diagnosed with ADHD. The EEG signal data from both groups were analyzed. To ensure analytical accuracy, artifacts and noise in the EEG signals were removed using the EEGLAB toolbox within the MATLAB environment. Following preprocessing, a comparative analysi

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Beumer, Steven, Paul Boon, Debby C. W. Klooster, et al. "Personalized tDCS for Focal Epilepsy—A Narrative Review: A Data-Driven Workflow Based on Imaging and EEG Data." Brain Sciences 12, no. 5 (2022): 610. http://dx.doi.org/10.3390/brainsci12050610.

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Conventional transcranial electric stimulation(tES) using standard anatomical positions for the electrodes and standard stimulation currents is frequently not sufficiently selective in targeting and reaching specific brain locations, leading to suboptimal application of electric fields. Recent advancements in in vivo electric field characterization may enable clinical researchers to derive better relationships between the electric field strength and the clinical results. Subject-specific electric field simulations could lead to improved electrode placement and more efficient treatments. Throug

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Kennedy, Ashleigh, and Jordan Hassin. "EEG Markers of Cognitive Engagement." Neurology 93, no. 14 Supplement 1 (2019): S3.1—S3. http://dx.doi.org/10.1212/01.wnl.0000580848.54563.2d.

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ObjectiveThe purpose of this study was to use portable electroencephalography and qualitative assessments to characterize cognitive changes associated with perceived increase in mental load and to identify markers of mental fatigue in these individuals.BackgroundThe ability to focus on cognitive tasks impacts everything from our social interactions to our success in the classroom or workplace. Concussion negatively impacts the ability to focus and causes patients to experience signs of mental fatigue more quickly than those without concussion. The mechanisms behind these changes are still not

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