Journal articles: 'EEG, electroencephalogram'

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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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Duarte-Celada, Walter, Samathorn Thakolwiboon, Jie Pan, Tulio Bueso, and Jannatul Ferdous. "Cefepime-induced non-convulsive status epilepticus." Southwest Respiratory and Critical Care Chronicles 12, no. 50 (January 29, 2024): 38–40. http://dx.doi.org/10.12746/swrccc.v12i50.1277.

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Cefepime-induced non-convulsive status epilepticus (NCSE) can develop in patients with advanced age, renal impairment, and previous central nervous system disorders. Its clinical presentation varies from confusion, mutism, and decreased level of consciousness to coma. The typical electroencephalogram (EEG) findings are generalized spike and wave discharges of 1-3 Hz. We present a case series of 4 patients with cefepime-induced NCSE, including the clinical presentation and EEG findings. Electroencephalograms should be part of the workup of acute confusional state in patients on this antibiotic, and physicians should be aware of this uncommon complication. Keywords: Non-convulsive status epilepticus, cefepime, confusion, mutism, electroencephalogram.

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Golomolzina, Diana Rashidovna, Maxim Alexandrovich Gorodnichev, Evgeny Andreevich Levin, Alexander Nikolaevich Savostyanov, Ekaterina Pavlovna Yablokova, Arthur C. Tsai, Mikhail Sergeevich Zaleshin, et al. "Advanced Electroencephalogram Processing." International Journal of E-Health and Medical Communications 5, no. 2 (April 2014): 49–69. http://dx.doi.org/10.4018/ijehmc.2014040103.

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The study of electroencephalography (EEG) data can involve independent component analysis and further clustering of the components according to relation of the components to certain processes in a brain or to external sources of electricity such as muscular motion impulses, electrical fields inducted by power mains, electrostatic discharges, etc. At present, known methods for clustering of components are costly because require additional measurements with magnetic-resonance imaging (MRI), for example, or have accuracy restrictions if only EEG data is analyzed. A new method and algorithm for automatic clustering of physiologically similar but statistically independent EEG components is described in this paper. Developed clustering algorithm has been compared with algorithms implemented in the EEGLab toolbox. The paper contains results of algorithms testing on real EEG data obtained under two experimental tasks: voluntary movement control under conditions of stop-signal paradigm and syntactical error recognition in written sentences. The experimental evaluation demonstrated more than 90% correspondence between the results of automatic clustering and clustering made by an expert physiologist.

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Beer, Nicole A. M. de, Willem L. van Meurs, Marco B. M. Grit, Michael L. Good, and Dietrich Gravenstein. "Educational simulation of the electroencephalogram (EEG)." Technology and Health Care 9, no. 3 (April 1, 2001): 237–56. http://dx.doi.org/10.3233/thc-2001-9302.

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TuerxunWaili, Yousif Sa’ad Alshebly, Khairul Azami Sidek, and Md Gapar Md Johar. "Stress recognition using Electroencephalogram (EEG) signal." Journal of Physics: Conference Series 1502 (March 2020): 012052. http://dx.doi.org/10.1088/1742-6596/1502/1/012052.

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Eklöf-Areskog, G., J. P. Aronsson, and L. Petersson. "P24.12 Melatonin for sleep-electroencephalogram (EEG)." Clinical Neurophysiology 122 (June 2011): S172. http://dx.doi.org/10.1016/s1388-2457(11)60619-6.

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Rubinos, Clio, Ayham Alkhachroum, Caroline Der-Nigoghossian, and Jan Claassen. "Electroencephalogram Monitoring in Critical Care." Seminars in Neurology 40, no. 06 (November 11, 2020): 675–80. http://dx.doi.org/10.1055/s-0040-1719073.

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AbstractSeizures are common in critically ill patients. Electroencephalogram (EEG) is a tool that enables clinicians to provide continuous brain monitoring and to guide treatment decisions—brain telemetry. EEG monitoring has particular utility in the intensive care unit as most seizures in this setting are nonconvulsive. Despite the increased use of EEG monitoring in the critical care unit, it remains underutilized. In this review, we summarize the utility of EEG and different EEG modalities to monitor patients in the critical care setting.

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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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Cao, Zehong, Kuan-Lin Lai, Chin-Teng Lin, Chun-Hsiang Chuang, Chien-Chen Chou, and Shuu-Jiun Wang. "Exploring resting-state EEG complexity before migraine attacks." Cephalalgia 38, no. 7 (September 29, 2017): 1296–306. http://dx.doi.org/10.1177/0333102417733953.

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Objective Entropy-based approaches to understanding the temporal dynamics of complexity have revealed novel insights into various brain activities. Herein, electroencephalogram complexity before migraine attacks was examined using an inherent fuzzy entropy approach, allowing the development of an electroencephalogram-based classification model to recognize the difference between interictal and preictal phases. Methods Forty patients with migraine without aura and 40 age-matched normal control subjects were recruited, and the resting-state electroencephalogram signals of their prefrontal and occipital areas were prospectively collected. The migraine phases were defined based on the headache diary, and the preictal phase was defined as within 72 hours before a migraine attack. Results The electroencephalogram complexity of patients in the preictal phase, which resembled that of normal control subjects, was significantly higher than that of patients in the interictal phase in the prefrontal area (FDR-adjusted p < 0.05) but not in the occipital area. The measurement of test-retest reliability (n = 8) using the intra-class correlation coefficient was good with r1 = 0.73 ( p = 0.01). Furthermore, the classification model, support vector machine, showed the highest accuracy (76 ± 4%) for classifying interictal and preictal phases using the prefrontal electroencephalogram complexity. Conclusion Entropy-based analytical methods identified enhancement or “normalization” of frontal electroencephalogram complexity during the preictal phase compared with the interictal phase. This classification model, using this complexity feature, may have the potential to provide a preictal alert to migraine without aura patients.

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Cappellari, Alberto M. "Normal Neonatal Electroencephalogram at a Glance." Journal of Neonatology 34, no. 4 (December 2020): 236–40. http://dx.doi.org/10.1177/0973217920977532.

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Interpreting neonatal electroencephalogram (EEG) presents a challenge owing to rapid evolution of EEG patterns occurring during brain maturation in the neonatal period and rich variety of normal patterns of EEG activity, which is difficult to categorize completely. Furthermore, the description of some aspects during maturation varies in different studies. Neonatal EEG is unfamiliar to most neurologists, and its interpretation requires knowledge of the physiological markers of electrogenesis maturation. The purpose of this review was to provide health-care professionals in the neonatal intensive care unit with guidance on the more common normal maturational features of the neonatal EEG. A simplified layout with the essential elements of normal neonatal EEG is included.

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Lai, Chi Qin, Haidi Ibrahim, Mohd Zaid Abdullah, Jafri Malin Abdullah, Shahrel Azmin Suandi, and Azlinda Azman. "Current Practical Applications of Electroencephalography (EEG)." Journal of Computational and Theoretical Nanoscience 16, no. 12 (December 1, 2019): 4943–53. http://dx.doi.org/10.1166/jctn.2019.8546.

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Electroencephalogram (EEG) is used to study the activities of human brain using instrument named electroencephalograph. The usage of EEG is now widened to many fields due to its great temporal resolution and other advantages. In this paper, a literature survey has been carried out to explore and categorize applications that have been invented from EEG. The literature survey is done on works from year 2011 up to the present. Three main research areas have been explored, which are medical applications, brain–computer interface and neuromarketing. In medical applications, EEG is used to detect brain abnormality, such as seizures or brain injury. As for BCI, many applications have been proposed for object control, object recognition, rehabilitation and human assistance. In neuromarketing, EEG is used to recognize consumers’ preference such as their preferable products or movies. This literature review shows that the research on EEG is still growing, and the area of applications are expanding.

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Xu, Jin, Erqiang Zhou, Zhen Qin, Ting Bi, and Zhiguang Qin. "Electroencephalogram-Based Subject Matching Learning (ESML): A Deep Learning Framework on Electroencephalogram-Based Biometrics and Task Identification." Behavioral Sciences 13, no. 9 (September 14, 2023): 765. http://dx.doi.org/10.3390/bs13090765.

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An EEG signal (Electroencephalogram) is a bioelectric phenomenon reflecting human brain activities. In this paper, we propose a novel deep learning framework ESML (EEG-based Subject Matching Learning) using raw EEG signals to learn latent representations for EEG-based user identification and tack classification. ESML consists of two parts: one is the ESML1 model via an LSTM-based method for EEG-user linking, and one is the ESML2 model via a CNN-based method for EEG-task linking. The new model ESML is simple, but effective and efficient. It does not require any restrictions for EEG data collection on motions and thinking for users, and it does not need any EEG preprocessing operations, such as EEG denoising and feature extraction. The experiments were conducted on three public datasets and the results show that ESML performs the best and achieves significant performance improvement when compared to baseline methods (i.e., SVM, LDA, NN, DTS, Bayesian, AdaBoost and MLP). The ESML1 model provided the best precision at 96% with 109 users and the ESML2 model achieved 99% precision at 3-Class task classification. These experimental results provide direct evidence that EEG signals can be used for user identification and task classification.

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Kim, Woongsik. "A Multi-Staged EEG (Electroencephalogram) Recognition Method." Advanced Science Letters 23, no. 4 (April 1, 2017): 3725–29. http://dx.doi.org/10.1166/asl.2017.9002.

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Banoczi, Walt R. "How Some Drugs Affect the Electroencephalogram (EEG)." American Journal of Electroneurodiagnostic Technology 45, no. 2 (June 2005): 118–29. http://dx.doi.org/10.1080/1086508x.2005.11079518.

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Mason, K., N. Lubisch, F. Robinson, R. Roskos, and M. Epstein. "Intramuscular dexmedetomidine for pediatric electroencephalogram (EEG) sedation." European Journal of Anaesthesiology 29 (June 2012): 161. http://dx.doi.org/10.1097/00003643-201206001-00531.

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UPADHYAY, R., P. K. PADHY, and P. K. KANKAR. "APPLICATION OF S-TRANSFORM FOR AUTOMATED DETECTION OF VIGILANCE LEVEL USING EEG SIGNALS." Journal of Biological Systems 24, no. 01 (March 2016): 1–27. http://dx.doi.org/10.1142/s0218339016500017.

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This paper presents an S-transform-based Electroencephalogram channel optimization and feature extraction methodology for monitoring mental vigilance level of humans. Vigilance level detection methodology consists of four steps. In the first stage, two types of Electroencephalogram signals (alert and drowsy) are acquired from 30 healthy subjects and decomposed into sub-bands using the S-transform. In the second stage, permutation entropy of the S-transform coefficients is calculated and Electroencephalogram channel optimization is performed. S-transform-based statistical features are computed from the optimized Electroencephalogram channels, in the third stage. In the fourth stage, artificial intelligence techniques such as Least Square-Support Vector Machine, Artificial Neural Network and Naive Bayes Classifier are used for the classification of Electroencephalogram signals using extracted features. The performance of the feature extraction methodology is tested on the Electroencephalogram data of 30 healthy subjects. Experimental results ensured the effectiveness of proposed methodology for the estimation of mental vigilance level by using Electroencephalogram signals. It is observed that the Artificial Neural Network classifier is a good candidate for pre-emptive automatic vigilance level detection system for Brain-Computer Interface applications.

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Ahammed, Kawser, and Mosabber Uddin Ahmed. "QUANTIFICATION OF MENTAL STRESS USING COMPLEXITY ANALYSIS OF EEG SIGNALS." Biomedical Engineering: Applications, Basis and Communications 32, no. 02 (April 2020): 2050011. http://dx.doi.org/10.4015/s1016237220500118.

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Detection of mental stress has been receiving great attention from the researchers for many years. Many studies have analyzed electroencephalogram signals in order to estimate mental stress using linear methods. In this paper, a novel nonlinear stress assessment method based on multivariate multiscale entropy has been introduced. Since the multivariate multiscale entropy method characterizes the complexity of nonlinear time series, this research determines the mental stress of human during cognitive workload using complexity of electroencephalogram (EEG) signals. To perform this work, 36 subjects including 9 men and 27 women were participated in the cognitive workload experiment. Multivariate multiscale entropy method has been applied to electroencephalogram data collected from those subjects for estimating mental stress in terms of complexity. The complexity feature of brain electroencephalogram signals collected during resting and cognitive workload has shown statistically significant ([Formula: see text]) differences across brain regions and mental tasks which can be implemented practically for building stress detection system. In addition, the complexity profile of electroencephalogram signals has shown that higher stress is reflected in good counting compared to bad counting. Moreover, the support vector machine (SVM) has shown promising classification between resting and mental counting states by providing 80% sensitivity, 100% specificity and 90% classification accuracy.

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Szu, Harold, Charles Hsu, Gyu Moon, Takeshi Yamakawa, Binh Q. Tran, Tzyy Ping Jung, and Joseph Landa. "Smartphone Household Wireless Electroencephalogram Hat." Applied Computational Intelligence and Soft Computing 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/241489.

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Rudimentarybrain machine interfacehas existed for the gaming industry. Here, we propose a wireless, real-time, and smartphone-based electroencephalogram (EEG) system for homecare applications. The system uses high-density dry electrodes and compressive sensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal throughput rate.Spatial sparsenessis addressed by close proximity between active electrodes and desired source locations and using an adaptive selection ofNactive among10Npassive electrodes to formm-organized random linear combinations of readouts,m≪N≪10N.Temporal sparsenessis addressed via parallel frame differences in hardware. During the design phase, we took tethered laboratory EEG dataset and applied fuzzy logic to compute (a) spatiotemporal average of larger magnitude EEG data centers in 0.3 second intervals and (b) inside brainwave sources by Independent Component Analysis blind deconvolution without knowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original tethered data and the speed of compressive image recovery. We have compared our recovery of ill-posed inverse data against results using Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e., facial muscle-related events and wireless environmental electromagnetic interferences).

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Krivonogova, E. V., L. V. Poskotinova, D. B. Demin, and O. A. Stavinskaya. "SEROTONIN LEVEL IN PERIPHERAL BLOOD AND THE BRAIN’S BIOELECTRICAL ACTIVITY IN YOUNG PEOPLE 15-17 YEARS." Medical academic journal 19, no. 1S (December 15, 2019): 137–38. http://dx.doi.org/10.17816/maj191s1137-138.

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The purpose of the work is to evaluate the features of the organization of the bioelectrical activity of the brain with different levels of serotonin in the serum of peripheral blood in young people 15-17 years old. The study involved 93 healthy girls and boys (15-17 years) of the Arkhangelsk region and the Nenets autonomous okrug. A serotonin level is determined in serum by enzyme immunoassay using a set of “Serotonin ELISA”. The electroencephalogram (EEG) power spectrum (PS) in the alpha, beta and theta frequencies ranges was recorded using an electroencephalograph “Encephalan” (Medicom, Taganrog). Age-dependent electroencephalogram (EEG) patterns is associated with the level of serotonin in peripheral blood in adolescents. On the background of a higher level of serotonin in the blood, compared to girls, boys have localized associations of theta and beta1 activity of EEG and serotonin levels, mainly in the right frontal-temporal region. In girls, the spectral power level of the EEG theta activity is more dependent on the level of serotonin in the blood, and a greater number of brain areas are involved in correlation interactions in comparison with young men (temporal regions on the left and frontal, central, parietal regions of both hemispheres of the brain).

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Nguyen, Tien Dat, Van Toi Vo, and Thi Thanh Huong Ha. "Bipolar disorder traits: An electroencephalogram systematic review." Ministry of Science and Technology, Vietnam 64, no. 4 (December 15, 2022): 84–90. http://dx.doi.org/10.31276/vjste.64(4).84-90.

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Bipolar disorder (BD) is a serious mental disorder that globally affected 40 million people in 2019. According to the National Alliance on Mental Illness (NAMI), the present state of scientific knowledge only permits psychiatrists to diagnose BD using subjective and imprecise questionnaires. Therefore, developing a diagnostic tool with objective and precise biomarkers should be a major focus of research in this field. Among the potential biomarkers for BD, electroencephalogram (EEG)-based signatures of BD are considered to be the most optimal marker due to their strong links with behavioural symptoms and also their non-invasiveness. The goal of this review is to give a detailed summary of current techniques for investigating the traces of BD through EEG abnormalities. In this review, 13 studies from databases such as ScienceDirect and PubMed seeking to utilize EEG characteristics to diagnose BD were selected. The search keywords were “EEG in BD diagnosis”, “EEG microstates in BD”, and “EEG features for BD patients”. The publication date was set from 2007 to 2021. From these studies, we synthesize the effects of BD on each EEG feature, as well as detail the pros and cons when using each feature as a biomarker for BD. Results showed that EEG microstates demonstrate their potential among the seven EEG properties discussed in this article, as shown by several studies. By definition, EEG microstates are a dynamic representation of the spatial distribution of the scalp's electric potential as it varies over time. Specifically, four microstate classes recorded in different brain regions are classified into A (right-frontal left-posterior), B (left-frontal right-posterior), C (midline frontal-occipital), and D (midline frontal topographies). Greater presence of microstate class B in BD patients during task-free resting states are a distinctive characteristic of BD patients from which BD can be differentiated from other psychiatric illnesses. Besides microstates, EEG resting states are also considered to have a bright future in BD diagnosis. Specifically, by investigating brain frequency bands, researchers have discovered that BD patients exhibit abnormal delta and alpha signals as compared to healthy controls (HCs). The abnormalities of microstate B in EEG microstate characteristics would be the most promising biomarker for detecting BD. In addition, anomalies in delta and alpha signals during resting EEG states are possible BD diagnostic indicators.

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Onder, Halil, Irsel Tezer, Vedat Hekimsoy, and Serap Saygi. "Simultaneous electrocardiogram during routine electroencephalogram: arrhythmia rates through the eyes of the cardiologist." Arquivos de Neuro-Psiquiatria 79, no. 1 (January 2021): 15–21. http://dx.doi.org/10.1590/0004-282x20200105.

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ABSTRACT Background: The importance of simultaneous 2-lead electrocardiogram (ECG) recording during routine electroencephalogram (EEG) has been reported several times on clinical grounds. Objective: To investigate arrhythmia rates detected by simultaneous 2-lead ECG in our patient sample undergoing routine EEG. Remarkably, we sought to assess the possible expansion of results with a more experienced interpretation of simultaneous ECG. Methods: Simultaneous 2-lead ECG recordings during routine EEG, performed between January and March, 2016, have been retrospectively analyzed by a cardiology specialist. In addition, EEG reports were screened with the keywords ‘arrhythmia, tachycardia, bradycardia, atrial fibrillation, extrasystole’ to evaluate the neurologist interpretation. Results: Overall, 478 routine EEG recordings were scanned. The mean age of the patients was 42.8±19.8 (16–95), with a sex ratio of 264/214 (F/M). In 80 (17%) patients, findings compatible with arrhythmia were identified on simultaneous ECG after a cardiologist's evaluation. The detected arrhythmia subtypes were: ventricular extrasystole (n=27; 5.6%), supraventricular extrasystole (n=23; 4.8%), tachycardia (n=9; 1.8%), prolonged QRS duration (n=7; 8.7%), atrial fibrillation (n=6; 1.2%), and block (n=6; 1.2%). On the other hand, keywords related to arrhythmia were present in 45 (9.4%) of EEG reports. The reported statements were tachycardia (3.3%), arrhythmia (2.5%), bradycardia (2.1%), and extrasystole (1.5%). Conclusions: A considerably high rate of arrhythmia cases was determined on simultaneous ECG during routine EEG after being interpreted by a cardiologist. However, the screening results of EEG reports revealed relatively low arrhythmia rates. These results suggest that the detection rates of ECG abnormalities during routine EEG may be potentially improved.

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Kaundal, Amardeep, V. Hegde, H. Khan, and Holger Allroggen. "Home video EEG telemetry." Practical Neurology 21, no. 3 (March 30, 2021): 212–15. http://dx.doi.org/10.1136/practneurol-2020-002910.

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Long-term electroencephalogram monitoring is often used to help distinguish epileptic from dissociative (non-epileptic) seizures. Home video telemetry now offers many of the benefits in diagnosis previously available only with inpatient video telemetry, which is usually regarded as the ‘gold standard’. Here, we describe recent developments in home video telemetry and how we undertake this procedure in our unit.

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Sinkin, M. V., E. P. Bogdanova, O. D. Elshina, and A. A. Troitskiy. "Rules for a routine electroencephalogram recording." Medical alphabet, no. 39 (December 21, 2021): 34–38. http://dx.doi.org/10.33667/2078-5631-2021-39-34-38.

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Electroencephalography (EEG) is the primary method for functional assessment of the brain bioelectrical activity. It is the most effective for epilepsy diagnosing, and also used for localization of the epileptogenic zone in presurgical evaluation for pharmaco-resistant epilepsy and in critical care medicine. In practice, the most common type is a 'routine' EEG, the informative value of which depends largely on the accuracy of its performance. The paper briefly outlines the rules for performing a routine EEG and lists the most common mistakes that can affect its interpretation.

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Babbysh, N. A. "Software Platform for Reading, Processing and Analyzing EEG Data." Programmnaya Ingeneria 14, no. 5 (May 23, 2023): 254–60. http://dx.doi.org/10.17587/prin.14.254-260.

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Electroencephalogram (EEG) data can be used in many different areas. For example, for diagnosing brain diseases, in brain computer interfaces, for conducting various studies, and much more. To apply EEG data, a large set of different algorithms for preprocessing and analyzing these data is needed. This paper describes a software platform containing a set of tools for automated processing of EEG signals and their analysis, including machine learning methods. The platform has a flexible architecture and consists of modules, which allows it to be used for various purposes. Data can be obtained both from files and directly from the electroencephalograph device in real time. The graphical interface provides a convenient way to configure the modules of the software. The software interface of client applications (API) makes it possible to use this platform to create prototypes of devices that use EEG data for their work.

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Vanadia, Arinda Putri Auna, Prastiya Indra Gunawan, Abdurachman Abdurachman, Martono Tri Utomo, and Hanik Badriyah Hidayati. "Electroencephalogram in Children who Experienced First Unprovoked Seizure." AKSONA 2, no. 2 (July 31, 2022): 52–56. http://dx.doi.org/10.20473/aksona.v2i2.35814.

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Highlight: Epiletiform abnormalities on the EEG provide additional clinical infromation about seizures The majority of patients who have a first unprovoked seizure have an abnormal EEG (Abnormal II). Using EEG as a supporting diagnostic tool in patients experiencing their first unprovoked seizures may provide more information to improve treatment ABSTRACT Introduction: The first unprovoked seizure is defined as a series of seizures that occur within 24 hours and are followed by recovery of consciousness with unknown triggering causes such as head trauma, central nervous system infections, tumors, or hypoglycemia. The first unprovoked seizure is a thing that cannot be underestimated. According to a previous study, less than half of those who experience their first unprovoked seizure will have another. An electroencephalogram (EEG) is one of the supporting examinations for the first unprovoked seizure. Objective: This study aims to determine the EEG as the first unprovoked seizure supporting examination. Methods: This is a retrospective, descriptive, observational study with sampling from the patient's medical record at Dr. Soetomo General Hospital Surabaya from January 2017 to December 2018 based on predetermined inclusion and exclusion criteria. Results: The EEG results in children who experienced their first unprovoked seizure were more abnormal (52.9%) than normal (47.1%), with an abnormal EEG breakdown of abnormal II (17.6%) and abnormal III (35.3%). There were no patients in this study who had EEG abnormal I. All patients with EEG abnormal II (17.6%) had an intermittent slow EEG waveform, while all patients with EEG abnormal III (35.3%) had a sharp waveform. The most common location of EEG wave abnormalities was temporal (55.6%). Conclusion: In the first unprovoked seizure, an EEG examination can assist clinicians as a seizure diagnostic assistant tool. It is hoped that the results of the EEG can provide better management of the first unprovoked seizure.

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Berezina, I. Yu, L. I. Sumsky, A. Yu Mikhailov, and Yu L. Arzumanov. "Electroencephalogram indices in patients undergoing cardiac arrest." Medical alphabet 1, no. 14 (September 9, 2020): 32–38. http://dx.doi.org/10.33667/2078-5631-2020-14-32-38.

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Objective: to assess the safety of indicators of electrical activity of the brain for the approach to the analysis of the basic neurophysiological mechanisms of the brain in patients after cardiac arrest.Materials and methods: 52 patients were examined (age — 54,68 ± 19,33) after cardiac arrest. At the time of recording the electroencephalogram (EEG), the level of wakefulness of the examined patients on the Glasgow coma scale was in the range of 3 to 13 points. In 35 patients, EEG recording was performed starting from the first three days from the moment of cardiac arrest, in 17 patients — from the fourth to the 18th day. EEG was registered on electroencephalographs ‘Encephalan–EEGR–19/26’ by ‘Medikom MTD’, ‘Neuron-Spectrum–5/EP’ and ‘Neuron-Spectrum–65’ by ‘Neurosoft’ in accordance with the recommendations of the International Federation of Clinical Neurophysiologists (IFCN). The duration of a single EEG recordings lasted at least 30 min. To localize equivalent dipole sources of pathological activity we used the program ‘BrainLoc 6.0’, (Russia). In 19 patients EEG was recorded in dynamics from 2 to 8 times.Results: all patients showed EEG changes of varying severity, which can be divided into three groups (according to the severity of changes in the EEG: moderate, severe and rough). In the group of patients with gross changes in EEG can be identified 4 variants: the first variant — absence of the alpha rhythm and the dominance of slow-wave fluctuations of the frequency spectrum; variant II — continuous generalized paroxysmal activity; variant III — phenomenon of ‘burst-suppression’; variant IV — a marked decrease in the amplitude of electrical activity of the brain to the level of 2–4 microvolt.Conclusions: based on the dynamics of the EEG pattern in patients after cardiac arrest, it is possible to assume with a certain degree of probability the level of violations in the basic mechanisms of the brain.

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Nadira Mohammad Yosi, Aqila Nur, Khairul Azami Sidek, Hamwira Sakti Yaacob, Marini Othman, and Ahmad Zamani Jusoh. "Emotion recognition using electroencephalogram signal." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 2 (August 1, 2019): 786. http://dx.doi.org/10.11591/ijeecs.v15.i2.pp786-793.

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<p class="Abstract">Emotion play an essential role in human’s life and it is not consciously controlled. Some of the emotion can be easily expressed by facial expressions, speech, behavior and gesture but some are not. This study investigates the emotion recognition using electroencephalogram (EEG) signal. Undoubtedly, EEG signals can detect human brain activity accurately with high resolution data acquisition device as compared to other biological signals. Changes in the human brain’s electrical activity occur very quickly, thus a high resolution device is required to determine the emotion precisely. In this study, we will prove the strength and reliability of EEG signals as an emotion recognition mechanism for four different emotions which are happy, sad, fear and calm. Data of six different subjects were collected by using BrainMarker EXG device which consist of 19 channels. The pre-processing stage was performed using second order of low pass Butterworth filter to remove the unwanted signals. Then, two ranges of frequency bands were extracted from the signals which are alpha and beta. Finally, these samples will be classified using MLP Neural Network. Classification accuracy up to 91% is achieved and the average percentage of accuracy for calm, fear, happy and sad are 83.5%, 87.3%, 85.83% and 87.6% respectively. Thus, a proof of concept, this study has been capable of proposing a system of recognizing four states of emotion which are happy, sad, fear and calm by using EEG signal.</p>

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Zhang, Dian, Bo Wang, and Qing Liang Qin. "A Portable Electroencephalogram Recording System for Rats." Applied Mechanics and Materials 577 (July 2014): 1236–40. http://dx.doi.org/10.4028/www.scientific.net/amm.577.1236.

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A wireless portable electroencephalogram (EEG) recording system for animals was designed, manufactured and then tested in rats. The system basically consisted of four modules: 1) EEG collecting module with the wireless transmitter and receiver (designed by NRF24LE1), 2) filter bank consisting of pre-amplifier, band pass filter and 50Hz trapper, 3) power management module and 4) display interface for showing EEG signals. The EEG data were modulated firstly and emitted by the wireless transmitter after being amplified and filtered. The receiver demodulated and displayed the signals in voltage through serial port. The system was designed as surface mount devices (SMD) with small size (20mm×25mm×3mm) and light weight (4g), and was fabricated of electronic components that were commercially available. The test results indicated that in given environment the system could stably record more than 8 hours and transmit EEG signals over a distance of 20m. Our system showed the features of small size, low power consumption and high accuracy which were suitable for EEG telemetry in rats.

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Lekatompessy, Jerome Constantine, Bertha Jean Que, and Laura Bianca Sylvia Huwae. "THE RELATIONSHIP OF EPILEPTIFORM WAVES IN ELECTROENCEPHALOGRAM WITH EPILEPSY TYPE OF SCHOOL-AGE EPILEPSY PATIENTS." MNJ (Malang Neurology Journal) 9, no. 2 (July 1, 2023): 99–101. http://dx.doi.org/10.21776/ub.mnj.2023.009.02.5.

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Background: Epilepsy in children causes memory problems in the learning process, so an early diagnosis of epilepsy is needed. The modality for determining the diagnosis of epilepsy is an electroencephalogram (EEG) examination. EEG recording results in epilepsy patients are epileptiform waves that can vary according to the type of epilepsy suffered. Objective: This study aims to determine the relationship of epileptiform waves on an electroencephalogram (EEG) with the type of epilepsy in school-age epilepsy patients. Methods: This study is an analytical study that uses secondary data in the form of medical records with cross sectional design. The research subjects were 106 patients taken with total sampling technique. Data collection is done by recording medical record data on the data collection form made by researchers. Correlation analysis between variables in this study used the Fisher test. Results: The results of this study indicate there is a relationship between epileptiform waves on the electroencephalogram (EEG) with the type of epilepsy in school-age epilepsy patients, with p = 0.018 in 0.050 significance value. Conclusion: It can be concluded that there is a correlation between epileptiform waves on an electroencephalogram (EEG) with the type of epilepsy in school-age epilepsy patients.

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Dalal, Virupaxi, and Satish Bhairannawar. "Efficient de-noising technique for electroencephalogram signal processing." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (June 1, 2022): 603. http://dx.doi.org/10.11591/ijai.v11.i2.pp603-612.

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An electroencephalogram (EEG) is a recording of various frequencies of electrical activity in the brain. EEG signal is very useful for diagnosis of various brain related diseases at early stage to prevent severe issues which may lead to loss of life. The raw EEG signal captured through the leads contain different type of noises which is not susceptible for diagnosis. In this paper, an efficient algorithm is proposed to process the raw EEG signal to combat the noise. To obtain noiseless EEG data, the likelihood test ratio is applied to interference computation block. The likelihood ratio test converts EEG data signal into segmented data with nearly constant noise characteristics. This will aid in detecting the noise present in a tiny segment which ensures proper signal denoising. The processed signal is compared with the database of noiseless EEG of the same person using principal component analysis (PCA) classifier. The proposed algorithm is 99.01% efficient to identify and combat noise in the EEG signal.

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Hakem, Ekram, Dhiah Al-Shammary, and Ahmed M. Mahdi. "Survey analysis for optimization algorithms applied to electroencephalogram." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (December 1, 2023): 6891. http://dx.doi.org/10.11591/ijece.v13i6.pp6891-6903.

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<span lang="EN-US">This paper presents a survey for optimization approaches that analyze and classify Electroencephalogram (EEG) signals. The automatic analysis of EEG presents a significant challenge due to the high-dimensional data volume. Optimization algorithms seek to achieve better accuracy by selecting practical features and reducing unwanted features. Forty-seven reputable research papers are provided in this work, emphasizing the developed and executed techniques divided into seven groups based on the applied optimization algorithm particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), grey wolf optimizer (GWO), Bat, Firefly, and other optimizer approaches). The main measures to analyze this paper are accuracy, precision, recall, and F1-score assessment. Several datasets have been utilized in the included papers like EEG Bonn University, CHB-MIT, electrocardiography (ECG) dataset, and other datasets. The results have proven that the PSO and GWO algorithms have achieved the highest accuracy rate of around 99% compared with other techniques.</span>

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Sneha Pujari. "Light weight neural network for ECG and EEG anomaly detection in IOT edge sensors." World Journal of Advanced Engineering Technology and Sciences 11, no. 2 (March 30, 2024): 269–80. http://dx.doi.org/10.30574/wjaets.2024.11.2.0111.

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Lightweight neural network designed for detecting anomalies in Electrocardiogram (ECG) and Electroencephalogram (EEG) signals at IoT edge sensors. By optimizing neural network architectures, we achieve high accuracy in anomaly detection while minimizing computational demands and memory usage. Experimental results validate the effectiveness of our approach in real-world scenarios, promising improved healthcare monitoring with early detection of abnormal ECG and EEG patterns at the edge.

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Saminu, Sani, Guizhi Xu, Zhang Shuai, Abd El Kader Isselmou, Adamu Halilu Jabire, Ibrahim Abdullahi Karaye, Isah Salim Ahmad, and Abubakar Abdulkarim. "Electroencephalogram (EEG) Based Imagined Speech Decoding and Recognition." Journal of Applied Materials and Technology 2, no. 2 (June 7, 2021): 74–84. http://dx.doi.org/10.31258/jamt.2.2.74-84.

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The recent investigations and advances in imagined speech decoding and recognition has tremendously improved the decoding of speech directly from brain activity with the help of several neuroimaging techniques that assist us in exploring the neurological processes of imagined speech. This development leads to assist people with disabilities to benefit from neuroprosthetic devices that improve the life of those suffering from neurological disorders. This paper presents the summary of recent progress in decoding imagined speech using Electroenceplography (EEG) signal, as this neuroimaging method enable us to monitor brain activity with high temporal resolution, it is very portable, low cost, and safer as compared to other methods. Therefore, it is a good candidate in investigating an imagined speech decoding from the human cortex which remains a challenging task. The paper also reviews some recent techniques, challenges, future recommendations and possible solutions to improve prosthetic devices and the development of brain computer interface system (BCI).

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Kim, Dong-Jun, and Seung-Jin Woo. "Evaluation of Waist Pressure Using Electroencephalogram(EEG) Signal." Transactions of The Korean Institute of Electrical Engineers 60, no. 6 (June 1, 2011): 1190–95. http://dx.doi.org/10.5370/kiee.2011.60.6.1190.

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Rodrick, David, and Hafiz Malik. "Can Electroencephalogram (EEG) Signals Predict Postural Balance Performance?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 57, no. 1 (September 2013): 938–42. http://dx.doi.org/10.1177/1541931213571208.

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Yao, Dezhong, ZhongKe Yin, XiangHong Tang, Lars Arendt-Nielsen, and Andrew C. N. Chen. "High-resolution electroencephalogram (EEG) mapping: scalp charge layer." Physics in Medicine and Biology 49, no. 22 (October 25, 2004): 5073–86. http://dx.doi.org/10.1088/0031-9155/49/22/004.

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Gatzonis, S. D., S. Roupakiotis, E. Kambayianni, A. Politi, N. Triantafyllou, V. Mantouvalos, A. Chioni, Ch Zournas, and A. Siafakas. "Hemispheric predominance of abnormal findings in electroencephalogram (EEG)." Seizure 11, no. 7 (October 2002): 442–44. http://dx.doi.org/10.1053/seiz.2001.0642.

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Chen, Guangyi. "Are electroencephalogram (EEG) signals pseudo-random number generators?" Journal of Computational and Applied Mathematics 268 (October 2014): 1–4. http://dx.doi.org/10.1016/j.cam.2014.02.028.

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Chaovalitwongse, Wanpracha Art, Oleg A. Prokopyev, and Panos M. Pardalos. "Electroencephalogram (EEG) time series classification: Applications in epilepsy." Annals of Operations Research 148, no. 1 (August 18, 2006): 227–50. http://dx.doi.org/10.1007/s10479-006-0076-x.

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Saman Shahid and Muhammad Anwar Chaudary. "Application of Digital EEG (Electroencephalogram) Sensors in Neurosciences." Pakistan Journal Of Neurological Surgery 27, no. 3 (September 30, 2023): i—ii. http://dx.doi.org/10.36552/pjns.v27i3.919.

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Kit, G. V. "ANALYSIS OF PEAK-WAVE DISCHARGES OF EEG WITH THE USE OF WAVELET TRANSFORMATIONS." Visnyk Universytetu “Ukraina”, no. 1 (28) 2020 (2020): 224–34. http://dx.doi.org/10.36994/2707-4110-2020-1-28-19.

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The method of analysis of electroencephalograms (EEG) on the basis of wavelet transformations is offered. Electroencephalogram (EEG) analysis is widely used in clinical practice for diagnosing such neurological diseases as epilepsy, Parkinson's disease and others. Traditional approaches to EEG analysis, generally accepted in the clinical diagnosis of diseases, are due to the fact that for a certain time after the stimulus, the EEG amplitudes are calculated at time intervals that depend on the frequency of signal quantization. Therefore, it is important to develop algorithms for classifying EEG signals using wavelet transforms. The analysis of peak-wave EEG discharges, which are indicators of the presence or absence of absence epilepsy, was performed. The EEG recording areas were decomposed into the main EEG frequency bands. Wavelet transform in combination with artificial neural networks makes it possible to implement a classifier based on the energy distribution of the components of the EEG signal. Determining the activity of individual components of EEG signals, as well as the materiality of the processes that take place in the sources of these waves, may be the subject of further research.

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KC, R., and R. Poudel. "Atypical Presentation Of Paediatric Absence Seizure: EEG As A Diagnostic Tool." Journal of Psychiatrists' Association of Nepal 10, no. 1 (October 14, 2021): 76–78. http://dx.doi.org/10.3126/jpan.v10i1.40366.

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Absence seizures involve brief, sudden lapses of consciousness and are more common in children than in adults. We report a case of absence seizure in a girl with atypical presentation which was diagnosed by electroencephalogram. She responded well to sodium valproate. Detailed history, clinical examination and use of electroencephalogram for diagnosis is necessary especially when such atypical presentations are encountered.

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Magomedova, Z. A., and S. S. Abumuslimov. "Interhemisphere asymmetry of the dominant frequency of electroencephalogram rhythms with age." BIO Web of Conferences 76 (2023): 01012. http://dx.doi.org/10.1051/bioconf/20237601012.

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The work investigated the asymmetry of the dominant frequency of electroencephalography (EEG) rhythms in the two hemispheres of the brain. Three age periods were studied: 16-20, 21-35 and 35-60 years. The study of the dominant frequency was carried out in general groups and separately in males and females. Students, additional education students and university staff were recruited as subjects. The dominant frequency of EEG rhythms was studied using a Neuron-Spectrum 1 electroencephalograph in eight monopolar leads. Electrodes were applied to the scalp according to the international “10-20%” system. The subjects’ electroencephalograms were recorded in a state of wakefulness with their eyes closed. The dominant frequency of five EEG rhythms was studied: alpha, beta1, beta2, theta and delta rhythms. It was revealed that at different age periods in general groups there is asymmetry in individual EEG rhythms. In addition, when studying males and females separately, asymmetry in the dominant frequency is observed in them at different age periods. Our data indicate a possible asymmetry in the electrical activity of the cerebral hemispheres in humans aged 16 to 60 years.

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Dewangga, Sandy Akbar, Handayani Tjandrasa, and Darlis Herumurti. "Robot Motion Control Using the Emotiv EPOC EEG System." Bulletin of Electrical Engineering and Informatics 7, no. 2 (June 1, 2018): 279–85. http://dx.doi.org/10.11591/eei.v7i2.678.

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Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.

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Amran, Annis Shafika, Sharifah Aida Sheikh Ibrahim, Nurul Hashimah Ahamed Hassain Malim, Nurfaten Hamzah, Putra Sumari, Syaheerah Lebai Lufti, and Jafri Malin Abdullah. "Data Acquisition and Data Processing using Electroencephalogram in Neuromarketing: A Review." Pertanika Journal of Science and Technology 30, no. 1 (December 6, 2021): 19–33. http://dx.doi.org/10.47836/pjst.30.1.02.

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Electroencephalogram (EEG) is a neurotechnology used to measure brain activity via brain impulses. Throughout the years, EEG has contributed tremendously to data-driven research models (e.g., Generalised Linear Models, Bayesian Generative Models, and Latent Space Models) in Neuroscience Technology and Neuroinformatic. Due to versatility, portability, cost feasibility, and non-invasiveness. It contributed to various Neuroscientific data that led to advancement in medical, education, management, and even the marketing field. In the past years, the extensive uses of EEG have been inclined towards medical healthcare studies such as in disease detection and as an intervention in mental disorders, but not fully explored for uses in neuromarketing. Hence, this study construes the data acquisition technique in neuroscience studies using electroencephalogram and outlines the trend of revolution of this technique in aspects of its technology and databases by focusing on neuromarketing uses.

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Fernandes, J. A., P. L. Lutz, A. Tannenbaum, A. T. Todorov, L. Liebovitch, and R. Vertes. "Electroencephalogram activity in the anoxic turtle brain." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 273, no. 3 (September 1, 1997): R911—R919. http://dx.doi.org/10.1152/ajpregu.1997.273.3.r911.

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The anoxia-tolerant turtle brain slowly undergoes a complex sequence of changes in electroencephalogram (EEG) activity as the brain systematically downregulates its energy demands. Following N2 respiration, the root mean square voltage rapidly fell, reaching approximately 20% of normoxic levels after approximately 100 min of anoxia. During the first 20- to 40-min transition period, the power of the EEG decreased substantially, particularly in the 12- to 24-Hz band, with low-amplitude slow wave activity predominating (3-12 Hz). Bursts of high voltage rhythmic slow (approximately 3-8 Hz) waves were seen during the 20- to 100-min period of anoxia, accompanied by large sharp waves. During the next 400 min of N2 respiration, two distinct patterns of electrical activity characterized the anoxic turtle brain: 1) a sustained but depressed activity level, with an EEG amplitude approximately 20% of the normoxic control and with total EEG power reduced by one order of magnitude at all frequencies, and 2) short (3-15 s) periodic (0.5-2/min) bursts of mixed-frequency activity that interrupted the depressed activity state. We speculate that the EEG patterns seen during sustained anoxia represent the minimal or basic electrical activities that are compatible with the survival of the anoxic turtle brain as an integrated unit, which allow the brain to return to normal functioning when air respiration resumed.

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Chen, Yi-Feng, Shou-Zen Fan, Maysam F. Abbod, Jiann-Shing Shieh, and Mingming Zhang. "Electroencephalogram variability analysis for monitoring depth of anesthesia." Journal of Neural Engineering 18, no. 6 (November 17, 2021): 066015. http://dx.doi.org/10.1088/1741-2552/ac3316.

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Abstract Objective. In this paper, a new approach of extracting and measuring the variability in electroencephalogram (EEG) was proposed to assess the depth of anesthesia (DOA) under general anesthesia. Approach. The EEG variability (EEGV) was extracted as a fluctuation in time interval that occurs between two local maxima of EEG. Eight parameters related to EEGV were measured in time and frequency domains, and compared with state-of-the-art DOA estimation parameters, including sample entropy, permutation entropy, median frequency and spectral edge frequency of EEG. The area under the receiver-operator characteristics curve (AUC) and Pearson correlation coefficient were used to validate its performance on 56 patients. Main results. Our proposed EEGV-derived parameters yield significant difference for discriminating between awake and anesthesia stages at a significance level of 0.05, as well as improvement in AUC and correlation coefficient on average, which surpasses the conventional features of EEG in detection accuracy of unconscious state and tracking the level of consciousness. Significance. To sum up, EEGV analysis provides a new perspective in quantifying EEG and corresponding parameters are powerful and promising for monitoring DOA under clinical situations.

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Fujimoto, Ayataka, Yuji Matsumaru, Yosuke Masuda, Aiki Marushima, Hisayuki Hosoo, Kota Araki, and Eiichi Ishikawa. "Endovascular Electroencephalogram Records Simultaneous Subdural Electrode-Detectable, Scalp Electrode-Undetectable Interictal Epileptiform Discharges." Brain Sciences 12, no. 3 (February 24, 2022): 309. http://dx.doi.org/10.3390/brainsci12030309.

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Introduction: We hypothesized that an endovascular electroencephalogram (eEEG) can detect subdural electrode (SDE)-detectable, scalp EEG-undetectable epileptiform discharges. The purpose of this study is, therefore, to measure SDE-detectable, scalp EEG-undetectable epileptiform discharges by an eEEG on a pig. Methods: A pig under general anesthesia was utilized to measure an artificially generated epileptic field by an eEEG that was able to be detected by an SDE, but not a scalp EEG as a primary outcome. We also compared the phase lag of each epileptiform discharge that was detected by the eEEG and SDE as a secondary outcome. Results: The eEEG electrode detected 113 (97%) epileptiform discharges (97% sensitivity). Epileptiform discharges that were localized within the three contacts (contacts two, three and four), but not spread to other parts, were detected by the eEEG with a 92% sensitivity. The latency between peaks of the eEEG and right SDE earliest epileptiform discharge ranged from 0 to 48 ms (mean, 13.3 ms; median, 11 ms; standard deviation, 9.0 ms). Conclusion: In a pig, an eEEG could detect epileptiform discharges that an SDE could detect, but that a scalp EEG could not.

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Siddiqui, Mohd Maroof, and Ruchin Jain. "Prediction of REM (Rapid Eye Movement) Sleep Behaviour Disorder using EEG Signal applied EMG1 and EMG2 Channel." Biomedical and Pharmacology Journal 14, no. 1 (March 30, 2021): 519–24. http://dx.doi.org/10.13005/bpj/2153.

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This sleep disorder is reflected as the changes in the electrical activities and chemical activities in the brain that can be observed by capturing the brain signals and the images. In this research, Short Time-frequency analysis of Power Spectrum Density (STFAPSD) approach applied on Electroencephalogram (EEG) Signals for prediction of RBD sleep disorder. Collection of Electroencephalogram (EEG) of normal subjects & different type of sleep disordered subjects & application of signal processing on EEG data for development the algorithm for detection of sleep disorder and implementation in MATLAB.

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Tsolaki, Anthoula, Dimitrios Kazis, Ioannis Kompatsiaris, Vasiliki Kosmidou, and Magda Tsolaki. "Electroencephalogram and Alzheimer’s Disease: Clinical and Research Approaches." International Journal of Alzheimer's Disease 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/349249.

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Alzheimer’s disease (AD) is a neurodegenerative disorder that is characterized by cognitive deficits, problems in activities of daily living, and behavioral disturbances. Electroencephalogram (EEG) has been demonstrated as a reliable tool in dementia research and diagnosis. The application of EEG in AD has a wide range of interest. EEG contributes to the differential diagnosis and the prognosis of the disease progression. Additionally such recordings can add important information related to the drug effectiveness. This review is prepared to form a knowledge platform for the project entitled “Cognitive Signal Processing Lab,” which is in progress in Information Technology Institute in Thessaloniki. The team tried to focus on the main research fields of AD via EEG and recent published studies.

Journal articles on the topic 'EEG, electroencephalogram'

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