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Journal articles on the topic 'Eeg'

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1

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

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AbstractObjectiveTo assess the reliability of interictal spike discharge in routine electroencephalography (EEG) testing in children.MethodEEG results of all children diagnosed in Nova Scotia with epilepsy onset between 1977–85 (excluding myoclonic, akinetic-atonic and absence) were reviewed. The results of the EEG at time of diagnosis (EEG1) were compared with those of a second EEG (EEG2) within 6 months.ResultsOf 504 children with epilepsy, 159 had both EEG1 and EEG2. EEG2 was more likely ordered if EEG1 was normal or showed focal slowing but less likely if EEG1 contained sleep (p < 0.05)
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2

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

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3

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

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4

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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5

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

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Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The
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6

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

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7

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

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8

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

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9

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

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10

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

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Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal resolution. In contrast to electrocardiography (ECG), EEG can be uncomfortable and inconvenient for patients. Moreover, deep-learning techniques require a large dataset and a long time for training from scratch. Therefore, in this study, EEG–EEG or EEG–ECG transfer learning strategies were applied to explore their effectiveness for the training of simple cross-domain convolutional neural networks (CNNs) used in seizure prediction and sleep staging sy
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11

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

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12

Michel, V., L. Mazzola, M. Lemesle, and L. Vercueil. "Long-term EEG in adults: Sleep-deprived EEG (SDE), ambulatory EEG (Amb-EEG) and long-term video-EEG recording (LTVER)." Neurophysiologie Clinique/Clinical Neurophysiology 45, no. 1 (2015): 47–64. http://dx.doi.org/10.1016/j.neucli.2014.11.004.

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13

Sema, Yildirim. "An Overview of ECG Artifact Detection in EEG Signals." Journal of Cardiovascular Medicine and Cardiology 12, no. 2 (2025): 017–21. https://doi.org/10.17352/2455-2976.000222.

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Electroencephalography (EEG) is an important technique for recording brain signals and is particularly used in the diagnosis and treatment of neurological diseases such as epilepsy. However, due to the complex nature of EEG signals, their interpretation is difficult and time-consuming. In EEG recordings, physiological noises such as eye movements (EOG) and electrocardiography (ECG) can affect the signals and hinder accurate diagnosis. This study emphasizes the importance of removing noise from EEG signals, with a focus on the impact of ECG-induced noise. The detection of QRS complexes in the E
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14

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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15

Issa, Mohamed F., Gergely Tuboly, György Kozmann, and Zoltan Juhasz. "Automatic ECG Artefact Removal from EEG Signals." Measurement Science Review 19, no. 3 (2019): 101–8. http://dx.doi.org/10.2478/msr-2019-0016.

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Abstract Electroencephalography (EEG) signals are frequently contaminated by ocular, muscle, and cardiac artefacts whose removal normally requires manual inspection or the use of reference channels (EOG, EMG, ECG). We present a novel, fully automatic method for the detection and removal of ECG artefacts that works without a reference ECG channel. Independent Component Analysis (ICA) is applied to the measured data and the independent components are examined for the presence of QRS waveforms using an adaptive threshold-based QRS detection algorithm. Detected peaks are subsequently classified by
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16

Chi, Yu M., Patrick Ng, and Gert Cauwenberghs. "Wireless noncontact ECG and EEG biopotential sensors." ACM Transactions on Embedded Computing Systems 12, no. 4 (2013): 1–19. http://dx.doi.org/10.1145/2485984.2485991.

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17

Von Rosenberg, Wilhelm, Theerasak Chanwimalueang, Valentin Goverdovsky, David Looney, David Sharp, and Danilo P. Mandic. "Smart Helmet: Wearable Multichannel ECG and EEG." IEEE Journal of Translational Engineering in Health and Medicine 4 (2016): 1–11. http://dx.doi.org/10.1109/jtehm.2016.2609927.

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18

Todd, Michael M. "EEGs, EEG Processing, and the Bispectral Index." Anesthesiology 89, no. 4 (1998): 815–17. http://dx.doi.org/10.1097/00000542-199810000-00002.

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19

Stephenson, J. B. P. "Need for ECG channel in ambulatory EEG." Electroencephalography and Clinical Neurophysiology 61, no. 3 (1985): S137. http://dx.doi.org/10.1016/0013-4694(85)90535-8.

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20

Thatcher, Robert W. "NORMATIVE EEG DATABASES AND EEG BIOFEEDBACK." Journal of Neurotherapy 2, no. 4 (1998): 8–39. http://dx.doi.org/10.1300/j184v02n04_02.

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21

Zhirong, M. "EEG hysterical provocative test (EEG - HPT)." Electroencephalography and Clinical Neurophysiology 103, no. 1 (1997): 217. http://dx.doi.org/10.1016/s0013-4694(97)89047-9.

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22

Schenk, S., K. Lamm, H. G�ndel, and K. H. Ladwig. "Neurofeedbackgest�tztes EEG-?- und EEG-?-Training." HNO 53, no. 1 (2005): 29–38. http://dx.doi.org/10.1007/s00106-004-1066-4.

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23

Mandekar, Swati, Abigail Holland, Moritz Thielen, Mehdi Behbahani, and Mark Melnykowycz. "Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG." Sensors 22, no. 4 (2022): 1568. http://dx.doi.org/10.3390/s22041568.

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Wearable EEG has gained popularity in recent years driven by promising uses outside of clinics and research. The ubiquitous application of continuous EEG requires unobtrusive form-factors that are easily acceptable by the end-users. In this progression, wearable EEG systems have been moving from full scalp to forehead and recently to the ear. The aim of this study is to demonstrate that emerging ear-EEG provides similar impedance and signal properties as established forehead EEG. EEG data using eyes-open and closed alpha paradigm were acquired from ten healthy subjects using generic earpieces
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24

Cai, Mengpu, Junxiang Chen, Chengcheng Hua, Guilin Wen, and Rongrong Fu. "EEG emotion recognition using EEG-SWTNS neural network through EEG spectral image." Information Sciences 680 (October 2024): 121198. http://dx.doi.org/10.1016/j.ins.2024.121198.

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25

Niedermeyer, E. "Leitfaden für die EEG-Praxis. Ein Bildkompendium (EEG Atlas, illustrated EEG textbook)." Electroencephalography and Clinical Neurophysiology 87, no. 4 (1993): 260. http://dx.doi.org/10.1016/0013-4694(93)90033-r.

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26

Thatcher, R. W., D. North, and C. Biver. "EEG and intelligence: Relations between EEG coherence, EEG phase delay and power." Clinical Neurophysiology 116, no. 9 (2005): 2129–41. http://dx.doi.org/10.1016/j.clinph.2005.04.026.

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27

Barry, Robert J., and Frances M. De Blasio. "EEG frequency PCA in EEG-ERP dynamics." Psychophysiology 55, no. 5 (2017): e13042. http://dx.doi.org/10.1111/psyp.13042.

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28

Hoppe, Matthias. "EEG-Befundung einschließlich Darstellung des normalen EEG." Das Neurophysiologie-Labor 40, no. 1 (2018): 14–43. http://dx.doi.org/10.1016/j.neulab.2017.11.002.

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29

Ramantani, Georgia, Louis Maillard, and Laurent Koessler. "Correlation of invasive EEG and scalp EEG." Seizure 41 (October 2016): 196–200. http://dx.doi.org/10.1016/j.seizure.2016.05.018.

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30

Nuwer, M. R. "S42-1 Digital EEG and EEG Standards." Clinical Neurophysiology 121 (October 2010): S61. http://dx.doi.org/10.1016/s1388-2457(10)60257-x.

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31

Brogger, Jan, Tom Eichele, Eivind Aanestad, Henning Olberg, Ina Hjelland, and Harald Aurlien. "Visual EEG reviewing times with SCORE EEG." Clinical Neurophysiology Practice 3 (2018): 59–64. http://dx.doi.org/10.1016/j.cnp.2018.03.002.

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32

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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33

Salmi, T. K., J. Voipio, and P. Kauppinen. "PO08-MO-04 Innovation in emergency neurology: recording of EEG with an EEG-to-ECG adapter." Journal of the Neurological Sciences 285 (October 2009): S190. http://dx.doi.org/10.1016/s0022-510x(09)70731-2.

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34

Bénar, Christian-G., Christophe Grova, Eliane Kobayashi, et al. "EEG–fMRI of epileptic spikes: Concordance with EEG source localization and intracranial EEG." NeuroImage 30, no. 4 (2006): 1161–70. http://dx.doi.org/10.1016/j.neuroimage.2005.11.008.

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35

Pittau, Francesca, Pierre LeVan, Friederike Moeller, et al. "Changes preceding interictal epileptic EEG abnormalities: Comparison between EEG/fMRI and intracerebral EEG." Epilepsia 52, no. 6 (2011): 1120–29. http://dx.doi.org/10.1111/j.1528-1167.2011.03072.x.

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36

Heckmann, J. G., P. Portwich, F. Kerling, H. Stefan, and B. Neundörfer. "Simultaneous EEG and ECG recording of sinus arrest." Intensive Care Medicine 27, no. 8 (2001): 1432. http://dx.doi.org/10.1007/s001340100997.

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37

Gilchrist, J. M. "Arrhythmogenic seizures: Diagnosis by simultaneous EEG/ECG recording." Neurology 35, no. 10 (1985): 1503. http://dx.doi.org/10.1212/wnl.35.10.1503.

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38

Chakrabarti, Dhritiman, and Sonia Bansal. "ECG contamination of EEG signals: effect on entropy." Journal of Clinical Monitoring and Computing 30, no. 1 (2015): 119–22. http://dx.doi.org/10.1007/s10877-015-9694-7.

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39

Ahn, Joong Woo, Yunseo Ku, and Hee Chan Kim. "A Novel Wearable EEG and ECG Recording System for Stress Assessment." Sensors 19, no. 9 (2019): 1991. http://dx.doi.org/10.3390/s19091991.

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Suffering from continuous stress can lead to serious psychological and even physical disorders. Objective stress assessment methods using noninvasive physiological responses such as heart rate variability (HRV) and electroencephalograms (EEG) have therefore been proposed for effective stress management. In this study, a novel wearable device that can measure electrocardiograms (ECG) and EEG simultaneously was designed to enable continuous stress monitoring in daily life. The developed system is easily worn by hanging from both ears, is lightweight (i.e., 42.5 g), and exhibits an excellent nois
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40

Boßelmann, Christian, and Holger Lerche. "Elektroenzephalografie (EEG)." Neurologie up2date 04, no. 04 (2021): 314–22. http://dx.doi.org/10.1055/a-1645-6477.

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41

Tatum, William O. "EEG Essentials." CONTINUUM: Lifelong Learning in Neurology 28, no. 2 (2022): 261–305. http://dx.doi.org/10.1212/con.0000000000001129.

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42

C., M. "EEG mapping." Neurology 41, no. 6 (1991): 951. http://dx.doi.org/10.1212/wnl.41.6.951-b.

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43

Miskin, Chandrabhaga, Karen S. Carvalho, Ignacio Valencia, Agustin Legido, and Divya S. Khurana. "EEG Duration." Journal of Child Neurology 30, no. 13 (2015): 1767–69. http://dx.doi.org/10.1177/0883073815579969.

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44

Swatzyna, Ronald J., Gerald P. Kozlowski, and Jay D. Tarnow. "Pharmaco-EEG." Clinical EEG and Neuroscience 46, no. 3 (2014): 192–96. http://dx.doi.org/10.1177/1550059414556120.

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45

Boczko, M. L. "Quantitative EEG." Neurology 45, no. 9 (1995): 1785. http://dx.doi.org/10.1212/wnl.45.9.1785.

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46

Wusthoff, Courtney J. "Neonatal EEG." Journal of Clinical Neurophysiology 33, no. 5 (2016): 375. http://dx.doi.org/10.1097/wnp.0000000000000298.

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47

Kochs, E. "EEG Monitoring." European Journal of Anaesthesiology 15, Supplement 17 (1998): 65–66. http://dx.doi.org/10.1097/00003643-199801001-00044.

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48

Alhambra, Marabella A., Timothy P. Fowler, and Antonio A. Alhambra. "EEG Biofeedback:." Journal of Neurotherapy 1, no. 2 (1995): 39–43. http://dx.doi.org/10.1300/j184v01n02_03.

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49

Bhatia, Manvir. "Neonatal EEG." Journal of Neonatology 20, no. 2 (2006): 147–58. http://dx.doi.org/10.1177/0973217920060207.

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50

Wazir, Sanjay, and Praveen Kumar. "EEG Machine." Journal of Neonatology 20, no. 2 (2006): 187–92. http://dx.doi.org/10.1177/0973217920060214.

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