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Academic literature on the topic 'ELECTROENCEPHALOGRA'

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Published: 11 September 2023

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Journal articles on the topic "ELECTROENCEPHALOGRA"

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Noach, LA, Jla Eekhof, LJ Bour, FE Posthumus Meyjes, Gnj Tytgat, and BW Ongerboer de Visser. "Bismuth salts and neurotoxicity. A randomised, single-blind and controlled study." Human & Experimental Toxicology 14, no. 4 (April 1995): 349–55. http://dx.doi.org/10.1177/096032719501400405.

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The aim of this work was to investigate whether the nor mal use of colloidal bismuth subcitrate (CBS) and bismuth subnitrate (BSN) exhibits neurotoxic side-effects. A ran domised, single-blind controlled study was carried out in 66 patients with H. pylori associated gastritis. Patients were randomised to receive either amoxicillin (control group) for 4 weeks or BSN for 8 weeks or CBS for 8 weeks. Clinical and neurophysiological tests including elec troneurography (ENG) and spectral electroencephalogra phy (EEG) were performed before and after therapy. No clinically relevant changes were observed with clinical tests as well as with ENG and spectral EEG recordings within each group and between the groups. It was con cluded that the normal use of CBS and BSN does not exhibit clinical neurotoxicity.
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Prendergast, Erica, Michele Grimason Mills, Jonathan Kurz, Joshua Goldstein, and Andrea C. Pardo. "Implementing Quantitative Electroencephalogram Monitoring by Nurses in a Pediatric Intensive Care Unit." Critical Care Nurse 42, no. 2 (April 1, 2022): 32–40. http://dx.doi.org/10.4037/ccn2022680.

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Background Nonconvulsive seizures occur frequently in pediatric intensive care unit patients and can be impossible to detect clinically without electroencephalogram monitoring. Quantitative electroencephalography uses mathematical signal analysis to compress data, monitoring trends over time. Nonneurologists can identify seizures with quantitative electroencephalography, but data on its use in the clinical setting are limited. Local Problem Bedside quantitative electroencephalography was implemented and nurses received education on its use for seizure detection. This quality improvement project aimed to describe the time between nurses’ recognition of electrographic seizures and seizure treatment. Methods Education was provided in phases over several months. Retrospective medical record review evaluated quantitative electroencephalograms and medication interventions from September 2019 through March 2020. A bedside form was used to measure nurses’ use of quantitative electroencephalograms, change recognition, clinician notification, and seizure treatment. A nurse survey evaluated the education after implementation. Results Data included 44 electroencephalograms from 30 pediatric intensive care unit patients aged 18 years or less with electroencephalogram monitoring durations of 4 hours or longer. Nurses monitored quantitative electroencephalograms in 73% of cases, documented at least 1 change in the quantitative electroencephalogram display in 28% of these cases, and contacted the neurocritical care team in 78% of cases in which they documented a change. Seizure treatment was initiated in response to the nursing call in 1 patient. Time to treatment was approximately 20 minutes. Conclusions An education program for quantitative electroencephalogram interpretation by nurse providers is feasible yet complex, requiring multiple reeducation cycles.
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Shafait, Saima, Wasim Alamgir, Imran Ahmad, Saeed Arif, Jahanzeb Liaqat, and Asif Hashmat. "A STUDY ON COMPARATIVE YIELDS OF STANDARD SHORT TERM ELECTROENCEPHALOGRAM AND LONG TERM ELECTROENCEPHALOGRAM RECORDING IN SUSPECTED EPILEPSY PATIENTS." PAFMJ 71, no. 5 (October 31, 2021): 1727–31. http://dx.doi.org/10.51253/pafmj.v71i5.5921.

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Objective: To compare the yield of interictal epileptiform discharges on prolonged (1-2 hours) electroencephalogram (EEG) as compared to standard routine (30 minutes) electroencephalogram (EEG). Study Design: Comparative observational study. Place and Duration of Study: Pak Emirates Military Hospital, Rawalpindi from Oct 2019 to Sep 2020. Methodology: A total of 364 outdoor patients with suspected epilepsy were recruited for the study. Out of these 55 electroencephalograms were excluded after applying exclusion criteria and 309 were included for final analysis. Electro-encephalograms were recorded using a 10-20 international system of electrode placement. The duration of each standard electroencephalogram was 30 minutes. It was followed by recording for an extended period of 60 minutes at least. The time to the appearance of the first abnormal interictal epileptiform discharge was noted. For analytical purposes, epileptiform discharges were classified as “early” if they appeared within the first 30 minutes and as “late” if appeared afterward. All electro-encephalograms were evaluated independently by two neurologists. Results: A total of 309 electroencephalograms were included for final analysis. Interictal epileptiform discharges were seen in 48 (15.6%) recordings. The mean time to appearance of first interictal epileptiform discharge was 14.6 ± 19.09 minutes. In 36 (11.7%) cases, discharges appeared early (within the first 30 minutes) whereas in the remaining 12 (3.9%) cases, discharges appeared late. This translates into a 33% increase in the diagnostic yield of electroencephalogram with an extended period of recording. Conclusion: Extending the electroencephalogram recording time results in a significantly better diagnostic yield of outdoor electroencephalogram.
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Zhu, Qianwen, Jianmin Zhang, Xingnan Wang, and Yueying Fang. "Influencing factors for cognitive impairment in patients with dorsolateral frontal lobe epilepsy." Neurology Asia 27, no. 2 (June 2022): 301–8. http://dx.doi.org/10.54029/2022crv.

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Background & Objective: To explore the influencing factors for cognitive impairment in patients with dorsolateral frontal lobe epilepsy (FLE) and the correlation between cognitive function and abnormal electroencephalograms. Methods: Eighty-two patients with dorsolateral FLE treated from April 2018 to April 2020 were selected. According to cognitive function test results, they were divided into a normal group (n=47) and an impairment group (n=35). Their general data were compared. The factors affecting cognitive function were assessed by univariate and multivariate logistic regression analyses. A nomogram prediction model was constructed for predicting cognitive impairment, and the predictive accuracy was assessed. The cognitive function and electroencephalogram results were compared. The correlation between abnormal electroencephalograms and cognitive function was analyzed. Results: Onset age ≥20 years old, educational years ≤12 years, course of disease ≥8 years, seizure frequency ≥once every 4 months, seizure duration ≥1.5 min and medication type were independent risk factors influencing the cognitive function of patients with dorsolateral FLE. The nomogram prediction model was highly accurate for predicting cognitive impairment. The levels of directional memory, associative learning memory, free recall of images, re-recognition of meaningless images, recall of character features, digital symbol substitution test, verbal fluency test, and backward digital span test of the impairment group were significantly lower than those of the normal group, and the number of patients with abnormal electroencephalograms was remarkably larger in the former group. Abnormal electroencephalogram had a significant negative correlation with cognitive impairment. Conclusion: Onset age, educational years, course of the disease, seizure frequency, seizure duration and medication type influence the cognitive function of patients with dorsolateral FLE. Abnormal electroencephalograms are closely correlated with cognitive function.
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Sidorenko, A. V., and M. A. Saladukha. "Evaluation of the depressive state of mobile technical systems operator subjected to electromagnetic noise radiation." Doklady BGUIR 18, no. 4 (June 25, 2020): 53–61. http://dx.doi.org/10.35596/1729-7648-2020-18-4-53-61.

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This paper presents the results of the work aimed at a study of the patterns of changes in the nonlinear electroencephalogram (EEG) parameters, including fractal dimension and self-similarity exponent, when the operator is irradiated with electromagnetic noise radiation. Together with the above-mentioned nonlinear parameters, a change in the spectral power density of the rhythmic components in EEG (delta-, theta-, alpha-, and beta-rhythms) has been studied. Investigation of the fractal dimension, self-similarity exponent, and spectral power density during irradiation was associated with possible changes of the parameters in the case of operator’s depression or minor depression. The radiation source was represented by a transistor electromagnetic-noise generator with the power of 30 mW operating over the frequency range of 5 GHz. The methods for calculation of the nonlinear parameters including fractal dimension and self-similarity exponent have been described. To realize the principal objectives of the work, the Java-based software was developed. The relevant literature demonstrating the changes in fractal dimension, self-similarity exponent, spectral power density of the delta-, theta-, alpha-, beta-rhythms in the case of depression and minor depression has been reviewed. Electroencephalograms were registered according to the “10/20” scheme using the MBN Neurocartograph electroencephalograph. The analyzed leads were Fp1, Fp2, T3, T4, P3, P4, O1, O2, F3, F4, C3, C4. As shown by the results of this work, there is no distinct depressive state of the operator exposed to electromagnetic noise radiation, judging by changes in the self-similarity exponent, fractal dimension, and spectral power density. However, when the operator was irradiated with electromagnetic noise radiation, the observed tendency in variation of the parameters was characteristic for minor depression.
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Bragin, A. D., and V. G. Spitsyn. "Motor imagery recognition in electroencephalograms using convolutional neural networks." Computer Optics 44, no. 3 (June 2020): 482–87. http://dx.doi.org/10.18287/2412-6179-co-669.

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Electroencephalography is a widespread method to record brain signals with the use of electrodes located on the surface of the head. This method of recording the brain activity has become popular because it is relatively cheap, compact, and does not require implanting the electrodes directly into the brain. The article is devoted to a problem of recognition of motor imagery by electroencephalogram signals. The nature of such signals is complex. Characteristics of electroencephalograms are individual for every person, also depending on their age and mental state, as well as the presence of noise and interference. The multitude of these parameters should be taken into account when analyzing encephalograms. Artificial neural networks are a good tool for solving this class of problems. Their application allows combining the tasks of extracting, selecting and classifying features in one signal processing unit. Electroencephalograms are time signals and we note that Gramian Angular Fields and Markov Transition Field transforms are used to represent time series in the form of images. The article shows the possibility of using the Gramian Angular Fields and Markov Transition Field transformations of the electroencephalogram (EEG) signal for motor imagery recognition using examples of imaginary movements with the right and left hand, also studying the effect of the resolution of Gramian Angular Fields and Markov Transition Field images on the classification accuracy. The best classification accuracy of the EEG signal into the motion and state-of-rest classes is about 99%. In future, the research results can be applied in constructing the brain-computer interface.
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Berezovchuk, L. V., and M. E. Makarchuk. "About bioelectric buffer system of the brain." Klinicheskaia khirurgiia 87, no. 7-8 (September 30, 2020): 53–57. http://dx.doi.org/10.26779/2522-1396.2020.7-8.53.

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Objective. Elaboration of objective quantitative criterion of electroencephalogram for estimation of the brain functional state in man. Маterials and methods. The background electroencephalograms analysis was conducted in 6 groups of the examined patients with various diagnosis (41 patients at all). Control group consisted of 7 patients, ageing 20 - 56 yrs (average age 35 yrs). Recording of EEG was conducted, using 16-channel electroencephalograph «NeuroCom standart» (KhАI - Меdika, Ukraine) in accordance to international system of recording «10-20». There were analyzed a quantity of meaningful interhemispheric asymmetries in accordance to power of summarized bioelectric signal in bilateral-synchronous points of the head in every group. The analysis time have constituted 1 min. Results. There was established, that the least meaningful difference in accordance to the bioelectrical signal power in bilateral-synchronous points of head may be considered in 1.4 times. Quantity of meaningful interhemispheric asymmetries in man may vary in large diapason - from 9 tо 25. Not all meaningful interhemispheric asymmetries in accordance to power of signals of separate rhythms are preserved while doing analysis of meaningful interhemispheric asymmetries in accordance to power of a summarized bioelectrical signal. Interhemispheric asymmetries in accordance to power of the summarized bioelectric signal in bilateral-synchronous points of the head may have more important informative meaning, than interhemispheric asymmetry in accordance to the signals power of separate rhythms. Conclusion. Quantity of meaningful interhemispheric asymmetries in accordance to power of signals of separate rhythms in healthy persons may vary from 16 tо 18. The interhemispheric asymmetries quantity reduction in accordance to power of the summarized bioelectric signal, comparing with quantity of interhemispheric asymmetries in accordance to power of signals of separate rhythms more than in 4 times, witnesses presence of the brain bioelectrical buffer system.
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Fonseca, Lineu C., Glória M. A. S. Tedrus, Marcelo G. Chiodi, Jaciara Näf Cerqueira, and Josiane M. F. Tonelotto. "Quantitative EEG in children with learning disabilities: analysis of band power." Arquivos de Neuro-Psiquiatria 64, no. 2b (June 2006): 376–81. http://dx.doi.org/10.1590/s0004-282x2006000300005.

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In order to better understand the mechanisms of learning disabilities it is important to evaluate the electroencephalogram parameters and their relation to the results of the Wechsler Intelligence Scale. Thirty-six children with complaints of learning disability were studied. Electroencephalograms were carried out while awake and resting, and the values for absolute and relative powers calculated. The results were compared with those of 36 healthy children paired with respect to age, gender and maternal scholastic level. In the group with learning disabilities, the absolute (in the delta, theta and alpha 1 bands) and relative (theta) power values were higher and the relative power alpha 2 value significantly lower at the majority of the electrodes in relation to the control group. There was a high positive correlation in the children with learning disabilities between the relative power alpha 2 and the verbal, performance and total IQ values. These quantitative electroencephalogram findings in children with learning disabilities have a clear relation with psychological measurements and could be due to brain immaturity.
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Patil, Miss N. R., and Prof S. N. Patil. "Review:Wavelet transform based electroencephalogram methods." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1776–79. http://dx.doi.org/10.31142/ijtsrd11542.

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Araki, Ryuhei, Kazuko Hayashi, and Teiji Sawa. "Dopamine D2-receptor Antagonist Droperidol Deepens Sevoflurane Anesthesia." Anesthesiology 128, no. 4 (April 1, 2018): 754–63. http://dx.doi.org/10.1097/aln.0000000000002046.

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Abstract Background Although midbrain dopaminergic pathways are known to contribute to arousal and emergence from anesthesia, few reports exist regarding the anesthetic effects of dopamine D2 receptor antagonism in humans. This study examined the effect of the D2 receptor antagonist droperidol on sevoflurane anesthesia by examining α and slow wave electroencephalogram oscillations. Methods Forty-five patients, age 20 to 60 yr, were enrolled. Frontal electroencephalograms were continuously collected for offline analysis via Bispectral Index monitoring. After induction of anesthesia, end-tidal sevoflurane concentration was deliberately maintained at 1%, and intravenous droperidol (0.05 mg/kg bolus) was administered. Electroencephalogram changes were examined in power spectrum and bicoherence, before and 10 min after droperidol injection, then compared using the Wilcoxon signed-ranks test and/or paired t test. Results Droperidol significantly augmented the α-bicoherence peak induced by sevoflurane from 30.3% (24.2%, 42.4%) to 50.8% (41.7%, 55.2%) (median [25th, 75th percentiles]; P < 0.0001), Hodges-Lehman median difference, 15.8% (11.3 to 21.4%) (95% CI). The frequency of the α-bicoherence peak was simultaneously shifted to the lower frequency; from 11.5 (11.0, 13.0) to 10.5 (10.0, 11.0) Hz (median [25th, 75th percentiles], P < 0.0001). Averaged bicoherence in the δ-θ area increased conspicuously from 17.2% (15.6 to 18.7%) to 25.1% (23.0 to 27.3%) (mean [95% CI]; P < 0.0001), difference, 8.0% (6.0 to 9.9%). Conclusions Droperidol augments both α and δ-θ bicoherences while shifting the α-bicoherence peaks to lower frequencies, and enhances the effect of sevoflurane anesthesia on the electroencephalogram via γ-aminobutyric acid–mediated oscillatory network regulation.
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Dissertations / Theses on the topic "ELECTROENCEPHALOGRA"

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Steffert, Tony. "Real-time electroencephalogram sonification for neurofeedback." Thesis, Open University, 2018. http://oro.open.ac.uk/57965/.

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Electroencephalography (EEG) is the measurement via the scalp of the electrical activity of the brain. The established therapeutic intervention of neurofeedback involves presenting people with their own EEG in real-time to enable them to modify their EEG for purposes of improving performance or health. The aim of this research is to develop and validate real-time sonifications of EEG for use in neurofeedback and methods for assessing such sonifications. Neurofeedback generally uses a visual display. Where auditory feedback is used, it is mostly limited to pre-recorded sounds triggered by the EEG activity crossing a threshold. However, EEG generates time-series data with meaningful detail at fine temporal resolution and with complex temporal dynamics. Human hearing has a much higher temporal resolution than human vision, and auditory displays do not require people to focus on a screen with their eyes open for extended periods of time - e.g. if they are engaged in some other task. Sonification of EEG could allow more rapid, contingent, salient and temporally detailed feedback. This could improve the efficiency of neurofeedback training and reduce the number and duration of sessions for successful neurofeedback. The same two deliberately simple sonification techniques were used in all three experiments of this research: Amplitude Modulation (AM) sonification, which maps the fluctuations in the power of the EEG to the volume of a pure tone; and Frequency Modulation (FM) sonification, which uses the changes in the EEG power to modify the frequency. Measures included, a listening task, NASA task load index; a measure of how much work it was to do the task, Pre & post measures of mood, and EEG. The first experiment used pre-recorded single channel EEG and participants were asked to listen to the sound of the sonified EEG and try and track the activity that they could hear by moving a slider on a computer screen using a computer mouse. This provided a quantitative assessment of how well people could perceive the sonified fluctuations in EEG level. The tracking accuracy scores were higher for the FM sonification but self-assessments of task load rated the AM sonification as easier to track. The second experiment used the same two sonifications, in a real neurofeedback task using participants own live EEG. Unbeknownst to the participants the neurofeedback task was designed to improve mood. A Pre-Post questionnaire showed that participants changed their self-rated mood in the intended direction with the EEG training, but there was no statistically significant change in EEG. Again the FM sonification showed a better performance but AM was rated as less effortful. The performance of sonifications in the tracking task in experiment 1 was found to predict their relative efficacy at blind self-rated mood modification in experiment 2. The third experiment used both the tracking as in experiment 1 and neurofeedback tasks as in experiment 2, but with modified versions of the AM and FM sonifications to allow two-channel EEG sonifications. This experiment introduced a physical slider as opposed to a mouse for the tracking task. Tracking accuracy increased, but this time no significant difference was found between the two sonification techniques on the tracking task. In the training task, once more the blind self-rated mood did improve in the intended direction with the EEG training, but as again there was no significant change in EEG, this cannot necessarily be attributed to the neurofeedback. There was only a slight difference between the two sonification techniques in the effort measure. In this way, a prototype method has been devised and validated for the quantitative assessment of real-time EEG sonifications. Conventional evaluations of neurofeedback techniques are expensive and time consuming. By contrast, this method potentially provides a rapid, objective and efficient method for evaluating the suitability of candidate sonifications for EEG neurofeedback.
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Nicolau, Nicoletta. "Automatic artefact removal from electroencephalograms." Thesis, University of Reading, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430848.

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Ng, Cheng Man. "Electroencephalogram analysis based on empirical mode decomposition." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493507.

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Antoniu, Angela. "Localization of the sources of the electroencephalogram." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0001/MQ59772.pdf.

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Fatoorechi, Mohsen. "Electroencephalogram signal acquisition in unshielded noisy environment." Thesis, University of Sussex, 2015. http://sro.sussex.ac.uk/id/eprint/55034/.

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Researchers have used electroencephalography (EEG) as a window into the activities of the brain. High temporal resolution coupled with relatively low cost compares favourably to other neuroimaging techniques such as magnetoencephalography (MEG). For many years silver metal electrodes have been used for non-invasive monitoring electrical activities of the brain. Although these electrodes provide a reliable method for recording EEG they suffer from noise, such as offset potentials and drifts, and usability issues, e.g. skin prepa- ration and short circuiting of adjacent electrodes due to gel running. Low frequency noise performance is the key indicator in determining the signal to noise ratio of an EEG sensor. In order to tackle these issues a prototype Electric Potential Sensor (EPS) device based on an auto-zero operational amplifier has been developed and evaluated. The absence of 1/f noise in these devices makes them ideal for use with signal frequencies ~10Hz or less. The EPS is a novel active electrode electric potential sensor with ultrahigh input impedance. The active electrodes are designed to be physically and electrically robust and chemically and biochemically inert. They are electrically insulated (anodized) and scalable. These sensors are designed to be immersed in alcohol for sterilization purposes. A comprehensive study was undertaken to compare the results of EEG signals recorded by the EPS with different commercial systems. These studies comprised measurements of both free running EEG and Event Related Potentials. Strictly comparable signals were observed with cross correlations of higher than 0.9 between the EPS and other systems.
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Tcheslavski, Gleb V. "Coherence and Phase Synchrony Analysis of Electroencephalogram." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/30186.

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Phase Synchrony (PS) and coherence analyses of stochastic time series - tools to discover brain tissue pathways traveled by electrical signals - are considered for the specific purpose of processing of the electroencephalogram (EEG). We propose the Phase Synchrony Processor (PSP), as a tool for implementing phase synchrony analysis, and examine its properties on the basis of known signals. Long observation times and wide filter bandwidths can decrease bias in PS estimates. The value of PS is affected by the difference in frequency of the sequences being analyzed and can be related to that frequency difference by the periodic sinc function. PS analysis of the EEG shows that the average PS is higher - for a number of electrode pairs - for non-ADHD than for ADHD participants. The difference is more pronounced in the δ rhythm (0-3 Hz) and in the γ rhythm (30-50 Hz) PS. The Euclidean classifier with electrode masking yields 66 % correct classification on average for ADHD and non-ADHD subjects using the δ and γ1 rhythms. We observed that the average γ1 rhythm PS is higher for the eyes closed condition than for the eyes open condition. The latter may potentially be used for vigilance monitoring. The Euclidean discriminator with electrode masking shows an average percentage of correct classification of 78 % between the eyes open and eyes closed subject conditions. We develop a model for a pair of EEG electrodes and a model-based MS coherence estimator aimed at processing short (i.e. 20 samples) EEG frames. We verify that EEG sequences can be modeled as AR(3) processes degraded by additive white noise with an average SNR of approximately 11-12 dB. Application of the MS coherence estimator to the EEG suggests that MS coherence is generally higher for non-ADHD individuals than for ADHD participants when evaluated for the θ rhythm of EEG. Also, MS coherence is consistently higher for ADHD subjects than for the majority of non-ADHD individuals when computed for the low end of the δ rhythm (i.e. below 1 Hz). ADHD produces more measurable effects in the frontal lobe EEG and for participants performing attention intensive tasks.
Ph. D.
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Chang, Nathalie. "Dipole localization using simulated intracerebral electroencephalograms." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82475.

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Interpreting intracerebral recordings in the search of an epileptic focus can be difficult because the amplitude of the potentials is misleading. Small generators located near the electrode site generate large potentials, which could swamp the signal of a nearby epileptic focus. In order to address this problem, two inverse problem algorithms, beamforming and RAP-MUSIC, were used with simulated intracerebral potentials to calculate equivalent dipole positions. Three dipoles were positioned in a semi-infinite plane medium near three intracerebral electrodes. Their potentials were simulated and contaminated with both white and correlated noise. Localization simulations for each type of noise showed that the two methods detected the sources accurately with RAP-MUSIC reporting lower orientation errors. A spatial resolution analysis for both methods was also performed to assess the separation ability of both methods. Beamforming adequately distinguished the sources separated by 1.2 cm, whereas RAP-MUSIC separated sources as close as 0.4--0.6 cm.
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Corradini, Paula L. "CLINICAL APPLICATIONS OF THE QUANTITATIVE ELECTROENCEPHALOGRAPH." Thesis, Laurentian University of Sudbury, 2014. https://zone.biblio.laurentian.ca/dspace/handle/10219/2154.

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Clinical psychology is a discipline that assesses and treats individuals experiencing a variety of psychological disorders; including brain injuries. Employing neuroimaging tools can reveal biological correlates that have not been previously studied in detail. The quantitative electroencephalograph (QEEG) is a dynamic neuroimaging tool that allows for the measurement of brain activity. QEEG source localization analysis has provided additional construct validity for neuropsychological tests by revealing increased activation in the associated brain regions. In addition, differences in resting brain activity have been found depending on the severity of neuropsychological impairment. Finally, enhancement of memory in normal individuals is shown by applying a weak physiologically-patterned electromagnetic field over the left hemisphere. Therefore, by integrating the QEEG with elements of clinical psychology it is possible to provide construct validity to neuropsychological tests, show differences in brain activation depending on the severity of neuropsychological impairment, and study emerging therapeutic techniques that could enhance memory.
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Lopez, de Diego Silvia Isabel. "Automated Interpretation of Abnormal Adult Electroencephalograms." Master's thesis, Temple University Libraries, 2017. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/463281.

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Electrical and Computer Engineering
M.S.E.E.
Interpretation of electroencephalograms (EEGs) is a process that is still dependent on the subjective analysis of the examiner. The interrater agreement, even for relevant clinical events such as seizures, can be low. For instance, the differences between interictal, ictal, and post-ictal EEGs can be quite subtle. Before making such low-level interpretations of the signals, neurologists often classify EEG signals as either normal or abnormal. Even though the characteristics of a normal EEG are well defined, there are some factors, such as benign variants, that complicate this decision. However, neurologists can make this classification accurately by only examining the initial portion of the signal. Therefore, in this thesis, we explore the hypothesis that high performance machine classification of an EEG signal as abnormal can approach human performance using only the first few minutes of an EEG recording. The goal of this thesis is to establish a baseline for automated classification of abnormal adult EEGs using state of the art machine learning algorithms and a big data resource – The TUH EEG Corpus. A demographically balanced subset of the corpus was used to evaluate performance of the systems. The data was partitioned into a training set (1,387 normal and 1,398 abnormal files), and an evaluation set (150 normal and 130 abnormal files). A system based on hidden Markov Models (HMMs) achieved an error rate of 26.1%. The addition of a Stacked Denoising Autoencoder (SdA) post-processing step (HMM-SdA) further decreased the error rate to 24.6%. The overall best result (21.2% error rate) was achieved by a deep learning system that combined a Convolutional Neural Network and a Multilayer Perceptron (CNN-MLP). Even though the performance of our algorithm still lags human performance, which approaches a 1% error rate for this task, we have established an experimental paradigm that can be used to explore this application and have demonstrated a promising baseline using state of the art deep learning technology.
Temple University--Theses
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Janwattanapong, Panuwat. "Connectivity Analysis of Electroencephalograms in Epilepsy." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3906.

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This dissertation introduces a novel approach at gauging patterns of informa- tion flow using brain connectivity analysis and partial directed coherence (PDC) in epilepsy. The main objective of this dissertation is to assess the key characteristics that delineate neural activities obtained from patients with epilepsy, considering both focal and generalized seizures. The use of PDC analysis is noteworthy as it es- timates the intensity and direction of propagation from neural activities generated in the cerebral cortex, and it ascertains the coefficients as weighted measures in formulating the multivariate autoregressive model (MVAR). The PDC is used here as a feature extraction method for recorded scalp electroencephalograms (EEG) as means to examine the interictal epileptiform discharges (IEDs) and reflect the phys- iological changes of brain activity during interictal periods. Two experiments were set up to investigate the epileptic data by using the PDC concept. For the investigation of IEDs data (interictal spike (IS), spike and slow wave com- plex (SSC), and repetitive spikes and slow wave complex (RSS)), the PDC analysis estimates the intensity and direction of propagation from neural activities gener- ated in the cerebral cortex, and analyzes the coefficients obtained from employing MVAR. Features extracted by using PDC were transformed into adjacency matrices using surrogate data analysis and were classified by using the multilayer Perceptron (MLP) neural network. The classification results yielded a high accuracy and pre- cision number. The second experiment introduces the investigation of intensity (or strength) of information flow. The inflow activity deemed significant and flowing from other regions into a specific region together with the outflow activity emanating from one region and spreading into other regions were calculated based on the PDC results and were quantified by the defined regions of interest. Three groups were considered for this study, the control population, patients with focal epilepsy, and patients with generalized epilepsy. A significant difference in inflow and outflow validated by the nonparametric Kruskal-Wallis test was observed for these groups. By taking advantage of directionality of brain connectivity and by extracting the intensity of information flow, specific patterns in different brain regions of interest between each data group can be revealed. This is rather important as researchers could then associate such patterns in context to the 3D source localization where seizures are thought to emanate in focal epilepsy. This research endeavor, given its generalized construct, can extend for the study of other neurological and neurode- generative disorders such as Parkinson, depression, Alzheimers disease, and mental illness.
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Books on the topic "ELECTROENCEPHALOGRA"

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National Institutes of Health (U.S.). Office of Clinical Center Communications, ed. EEG (electroencephalogram). [Bethesda, Md.?]: Clinical Center Communications, National Institutes of Health, 1989.

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Vogel, Friedrich. Genetics and the Electroencephalogram. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57040-7.

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The electroencephalogram: Its patterns and origins. Cambridge, Mass: MIT Press, 1993.

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Garner, B. P. Spectral analysis of the electroencephalogram in young children. Manchester: UMIST, 1992.

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Empson, Jacob. Human brainwaves: The psychological significance of the electroencephalogram. Houndmills, Basingstoke, Hampshire: Macmillan, 1986.

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Parker, James N., and Philip M. Parker. Electroencephalogram: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.

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Richards, Mark. Modulation of human electroencephalogram activity by experimentally generated electromagnetic fields: A counterclockwise application of complex magnetic fields known to alter time perception. Sudbury, Ont: Laurentian University, Department of Psychology, 2001.

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The Brain's alpha rhythms and the mind: A review of classical and modern studies of the alpha rhythm component of the electroencephalogram with commentaies on associated neuroscience and neuropsychology. Amsterdam: Elsevier, 2003.

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Shaw, J. C. The Brain's alpha rhythms and the mind: A review of classical and modern studies of the alpha rhythm component of the electroencephalogram with commentaries on associated neuroscience and neuropsychology. Amsterdam: Elsevier, 2003.

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Kam, Julia W. Y., and Todd C. Handy. Electroencephalogram Recording in Humans. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0006.

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Abstract:
This chapter provides an elementary introduction to the theory and practical application of electroencephalogram (EEG) recording for the purpose of studying neurocognitive processes. It is aimed at readers who have had little or no experience in EEG data collection, and would like to gain a better understanding of scientific papers employing this methodology or start their own EEG experiment. We begin with a definition of EEG, and a summary of the strengths and limitations of EEG-based techniques. Following this is a description of the basic theory concerning the cellular mechanisms underlying EEG, as well as two types of data generated by EEG recording. We then present a brief summary of the equipment necessary for EEG data acquisition and important considerations for presentation software. Finally, we provide an overview of the protocol for data acquisition and processing, as well as methods for quantifying both EEG and event-related potentials data.
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Book chapters on the topic "ELECTROENCEPHALOGRA"

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Ellenbroek, Bart, Alfonso Abizaid, Shimon Amir, Martina de Zwaan, Sarah Parylak, Pietro Cottone, Eric P. Zorrilla, et al. "Electroencephalogram." In Encyclopedia of Psychopharmacology, 462. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_605.

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Casson, Alexander J., Mohammed Abdulaal, Meera Dulabh, Siddharth Kohli, Sammy Krachunov, and Eleanor Trimble. "Electroencephalogram." In Seamless Healthcare Monitoring, 45–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69362-0_2.

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Ellenbroek, Bart, Alfonso Abizaid, Shimon Amir, Martina de Zwaan, Sarah Parylak, Pietro Cottone, Eric P. Zorrilla, et al. "Electroencephalogy." In Encyclopedia of Psychopharmacology, 471–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_616.

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Pavelka, Lauren Connell. "Electroencephalogram (EEG)." In Encyclopedia of Child Behavior and Development, 563–64. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_970.

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Charman, Tony, Susan Hepburn, Moira Lewis, Moira Lewis, Amanda Steiner, Sally J. Rogers, Annemarie Elburg, et al. "Electroencephalogram (EEG)." In Encyclopedia of Autism Spectrum Disorders, 1067–68. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_720.

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Aaronson, Benjamin. "Electroencephalogram (EEG)." In Encyclopedia of Autism Spectrum Disorders, 1665–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-91280-6_720.

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Luhmann, Heiko J. "EEG (Electroencephalogram)." In Encyclopedia of Sciences and Religions, 696. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8265-8_200675.

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Vogel, Friedrich. "The Problem." In Genetics and the Electroencephalogram, 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57040-7_1.

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Vogel, Friedrich. "The Human EEG: General Aspects." In Genetics and the Electroencephalogram, 7–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57040-7_2.

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Vogel, Friedrich. "Genetic Studies:Twin Studies." In Genetics and the Electroencephalogram, 23–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57040-7_3.

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Conference papers on the topic "ELECTROENCEPHALOGRA"

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Keshishzadeh, Sarineh, Ali Fallah, and Saeid Rashidi. "Electroencephalogram Based Biometrics." In the 2018 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3230820.3230821.

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Mohite, Nilima, Rajveer Shastri, Shankar Deosarkar, and Arnab Das. "Epileptic electroencephalogram classification." In 2014 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2014. http://dx.doi.org/10.1109/iccsp.2014.6949885.

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Chandrasiri, M. E., R. M. T. M. Dhanapala, W. G. K. G. Kumari, and R. Ranaweera. "PC based Electroencephalogram system." In 2013 IEEE 8th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2013. http://dx.doi.org/10.1109/iciinfs.2013.6731966.

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HWA, RUDOLPH C., and THOMAS C. FERREE. "FLUCTUATIONS IN HUMAN ELECTROENCEPHALOGRAM." In Proceedings of the 10th International Workshop on Multiparticle Production. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704641_0038.

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Szu, Harold, Charles Hsu, Gyu Moon, Takeshi Yamakawa, and Binh Tran. "Household wireless electroencephalogram hat." In SPIE Defense, Security, and Sensing, edited by Harold Szu and Liyi Dai. SPIE, 2012. http://dx.doi.org/10.1117/12.923669.

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Geetha, G. "Detecting epileptic seizures using Electroencephalogram." In the Second International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2393216.2393260.

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Geetha, G., and S. N. Geethalakshmi. "Detecting epileptic seizures using electroencephalogram." In the International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2345396.2345510.

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Rodrigues, Pedro Miguel, Diamantino Freitas, and Joao Paulo Teixeira. "Alzheimer's electroencephalogram event scalp localization." In 2015 IEEE 9th International Workshop on Multidimensional (nD) Systems (nDS). IEEE, 2015. http://dx.doi.org/10.1109/nds.2015.7332640.

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He, Aijun, Xiaodong Yang, Xi Yang, and Xinbao Ning. "Phase Synchronization in Sleep Electroencephalogram." In 2007 IEEE/ICME International Conference on Complex Medical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/iccme.2007.4381979.

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Afroz, Dil, and Nafiul Hasan. "Emotion state analysis by Electroencephalogram." In 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET). IEEE, 2022. http://dx.doi.org/10.1109/iciset54810.2022.9775894.

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Reports on the topic "ELECTROENCEPHALOGRA"

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

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ABERDEEN TEST CENTER MD SOLDIER SYSTEMS DIV. Electroencephalogram-Based Measurement of Workload, Engagement, and Fatigue. Fort Belvoir, VA: Defense Technical Information Center, April 2011. http://dx.doi.org/10.21236/ada540923.

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Jokeit, H., R. Goertzl, E. Kuchleri, and S. Makeig. Event-Related Changes in the 40 Hz Electroencephalogram in Auditory and Visual Reaction Time Tasks. Fort Belvoir, VA: Defense Technical Information Center, January 1994. http://dx.doi.org/10.21236/ada379543.

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