Relevant bibliographies by topics / Electroencephalogram signal

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Academic literature on the topic 'Electroencephalogram signal'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Electroencephalogram signal"

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 (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 emot

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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 (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 nois

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Geetha, P., and S. Nagarani. "Novel Model for Automatic Classification of the Epileptic Seizures Using Fast Fourier Series-Haar Wavelet Transform." Journal of Medical Imaging and Health Informatics 11, no. 12 (2021): 3209–14. http://dx.doi.org/10.1166/jmihi.2021.3918.

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The disorder based on neurological can be considered as epilepsy that leads to the recurrent seizures in occurrence. The electronic characteristics of brain can be monitor by the electroencephalogram (EEG). It is most commonly used in the medical application. The function monitoring records can be non linear as well as non stationary functioning. The present work produce a novel methodology, it is depend on Fast Fourier series (FFS) and wavelet transform based on Haar. These methods are used for the various kinds of epileptic seizure the electroencephalogram based signal. The detection of boun

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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 (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 projec

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Berezovchuk, L. V., and M. E. Makarchuk. "About bioelectric buffer system of the brain." Klinicheskaia khirurgiia 87, no. 7-8 (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 mea

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Melinda, Melinda, Syahrial, Yunidar, Al Bahri, and Muhammad Irhamsyah. "Finite Impulse Response Filter for Electroencephalogram Waves Detection." Green Intelligent Systems and Applications 2, no. 1 (2022): 7–19. http://dx.doi.org/10.53623/gisa.v2i1.65.

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Electroencephalographic data signals consist of electrical signal activity with several characteristics, such as non-periodic patterns and small voltage amplitudes that can mix with noise making it difficult to recognize. This study uses several types of EEG wave signals, namely Delta, Alpha, Beta, and Gamma. The method we use in this study is the application of an impulse response filter to replace the noise obtained before and after the FIR filter is applied. In addition, we also analyzed the quality of several types of electroencephalographic signal waves by looking at the addition of the s

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Djamal, Esmeralda Contessa, and Dimas Andhika Sury. "Multi-channel of electroencephalogram signal in multivariable brain-computer interface." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 618. http://dx.doi.org/10.11591/ijai.v12.i2.pp618-626.

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Brain-computer interface (BCI) usually uses Electroencephalogram (EEG) signals as an intermediate device to drive external devices directly from the brain. The development of BCI capabilities is carried out by involving multivariable EEG signals as movement commands. EEG signals are recorded using multi-channel, enriching information if it uses the suitable method and architecture. This research proposed a two-dimensional convolutional neural networks (CNN) method to recognize multi-channel EEG signals. The vertical dimension is the channel, while the horizontal is the signal sequence. Hence,

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Esmeralda, Contessa Djamal, and Andhika Sury Dimas. "Multi-channel of electroencephalogram signal in multivariable brain-computer interface." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 618–26. https://doi.org/10.11591/ijai.v12.i2.pp618-626.

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Perumalsamy, Marichamy, and Kalyana Sundaram Chandran. "Gustatory stimulus-based electroencephalogram signal classification." International Journal of Biomedical Engineering and Technology 37, no. 3 (2021): 308. http://dx.doi.org/10.1504/ijbet.2021.10043694.

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Chandran, Kalyana Sundaram, and Marichamy Perumalsamy. "Gustatory stimulus-based electroencephalogram signal classification." International Journal of Biomedical Engineering and Technology 37, no. 3 (2021): 308. http://dx.doi.org/10.1504/ijbet.2021.119930.

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Dissertations / Theses on the topic "Electroencephalogram signal"

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 run

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Winski, R. "Adaptive techniques for signal enhancement in the human electroencephalogram." Thesis, Keele University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372829.

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Liu, Hui. "Online automatic epileptic seizure detection from electroencephalogram (EEG)." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012941.

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Mylonas, Socrates Andreou. "Signal modelling : a versatile approach for the automatic analysis of the electroencephalogram." Thesis, City University London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283270.

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Nussbaum, Paul. "Signal Processing of Electroencephalogram for the Detection of Attentiveness towards Short Training Videos." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/558.

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This research has developed a novel method which uses an easy to deploy single dry electrode wireless electroencephalogram (EEG) collection device as an input to an automated system that measures indicators of a participant’s attentiveness while they are watching a short training video. The results are promising, including 85% or better accuracy in identifying whether a participant is watching a segment of video from a boring scene or lecture, versus a segment of video from an attentiveness inducing active lesson or memory quiz. In addition, the final system produces an ensemble average of att

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Kwong, Siu-shing. "Detection of determinism of nonlinear time series with application to epileptic electroencephalogram analysis." View the Table of Contents & Abstract, 2005. http://sunzi.lib.hku.hk/hkuto/record/B35512222.

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Young, Andrew Coady. "A Consensus Model for Electroencephalogram Data Via the S-Transform." Digital Commons @ East Tennessee State University, 2012. https://dc.etsu.edu/etd/1424.

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A consensus model combines statistical methods with signal processing to create a better picture of the family of related signals. In this thesis, we will consider 32 signals produced by a single electroencephalogram (EEG) recording session. The consensus model will be produced by using the S-Transform of the individual signals and then normalized to unit energy. A bootstrapping process is used to produce a consensus spectrum. This leads to the consensus model via the inverse S-Transform of the consensus spectrum. The method will be applied to both a control and experimental EEG to show how th

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Kawaguchi, Hirokazu. "Signal Extraction and Noise Removal Methods for Multichannel Electroencephalographic Data." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/188593.

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Alhajjar, Yasser. "Prévision du risque neuro-développemental du nouveau-né prématuré par classification automatique du signal EEG." Thesis, Angers, 2017. http://www.theses.fr/2017ANGE0020/document.

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L’électroencéphalogramme (EEG), mesure de l'activité électrique du cerveau, reste une des meilleures méthodes de prévision non-invasive des résultats neurologiques. L'objectif de notre travail est de développer un système de classification automatique qui prévoit des risques sur la maturation cérébrale, se traduisant par un état pathologique à 2 ans. Les caractéristiques du signal EEG, qui sont utiles à la prévision automatisée, sont traitées via un module appelée EEGDiag, et sont appliquées sur un ensemble de données issues de 397 dossiers de nouveau-nés prématurés. Chaque dossier comprend un

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Hajipour, Sardouie Sepideh. "Signal subspace identification for epileptic source localization from electroencephalographic data." Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S185/document.

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Lorsque l'on enregistre l'activité cérébrale en électroencéphalographie (EEG) de surface, le signal d'intérêt est fréquemment bruité par des activités différentes provenant de différentes sources de bruit telles que l'activité musculaire. Le débruitage de l'EEG est donc une étape de pré-traitement important dans certaines applications, telles que la localisation de source. Dans cette thèse, nous proposons six méthodes permettant la suppression du bruit de signaux EEG dans le cas particulier des activités enregistrées chez les patients épileptiques soit en période intercritique (pointes) soit e

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Books on the topic "Electroencephalogram signal"

Hamdi, Salah. Electroencephalogram Signal Analysis: Epileptic Cerebral Activity Localization and Implementation. Cambridge Scholars Publishing, 2022.

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Butkov, Nic. Polysomnography. Edited by Sudhansu Chokroverty, Luigi Ferini-Strambi, and Christopher Kennard. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682003.003.0007.

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This chapter provides an overview of the sleep recording process, including the application of electrodes and sensors to the patient, instrumentation, signal processing, digital polysomnography (PSG), and artifact recognition. Topics discussed include indications for PSG, standard recording parameters, patient preparation, electrode placement for recording the electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG), the use of respiratory transducers, oximetry, signal processing, filters, digital data display, electrical safety, and patient monitor

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Koutroumanidis, Michalis, Dimitrios Sakellariou, and Vasiliki Tsirka. Electroencephalography. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0011.

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This chapter concentrates on essential technical aspects of the electroencephalogram (EEG) and its role in the clinical and aetiological diagnosis of people with epilepsy. The technical subsection explores important stages of the largely ‘mystifying’ process from the generation of the abnormal signals in the brain to their final visualization on the screen, including digitalization of the signal and sampling rate, montages, and derivations, focusing on their clinical relevance. The second part reviews the behavioural attributes of the interictal and ictal discharges in the different epilepsy t

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Amzica, Florin, and Fernando H. Lopes da Silva. Cellular Substrates of Brain Rhythms. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0002.

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The purpose of this chapter is to familiarize the reader with the basic electrical patterns of the electroencephalogram (EEG). Brain cells (mainly neurons and glia) are organized in multiple levels of intricate networks. The cellular membranes are semipermeable media between extracellular and intracellular solutions, populated by ions and other electrically charged molecules. This represents the basis of electrical currents flowing across cellular membranes, further generating electromagnetic fields that radiate to the scalp electrodes, which record changes in the activity of brain cells. This

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Schomer, Donald L., Charles M. Epstein, Susan T. Herman, Douglas Maus, and Bruce J. Fisch. Recording Principles. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0005.

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This chapter reviews the technical aspects of recording and reviewing clinical electroencephalograms (EEGs) and related biopotentials. While advances in engineering technology have revolutionized EEG machines, the basic principles underlying accurate representation of brain activity are largely unchanged. The first section reviews the analog EEG components, and the second section discusses analog-to-digital conversion, digital filters, and display and storage parameters. Digital EEG machines are now less expensive and their capabilities far surpass those of analog machines. The third section r

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Luginbühl, Martin, and Arvi Yli-Hankala. Assessment of the components of anaesthesia. Edited by Antony R. Wilkes and Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0026.

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In modern anaesthesia practice, hypnotic drugs, opioids, and neuromuscular blocking agents (NMBAs) are combined. The introduction of NMBAs in particular substantially increased the risk of awareness and recall during general anaesthesia. Hypnotic drugs such as propofol and volatile anaesthetics act through GABAA receptors and have typical effects on the electroencephalogram (EEG). During increasing concentrations of these pharmaceuticals, the EEG desynchronization is followed by gradual synchronization, slowing frequency, and increasing amplitude of EEG, thereafter EEG suppressions (burst supp

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Jef ferys, John G. R. Cortical activity: single cell, cell assemblages, and networks. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199688395.003.0004.

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This chapter describes how the activity of neurons produces electrical potentials that can be recorded at the levels of single cells, small groups of neurons, and larger neuronal networks. It outlines how the movement of ions across neuronal membranes produces action potentials and synaptic potentials. It considers how the spatial arrangement of specific ion channels on the neuronal surface can produce potentials that can be recorded from the extracellular space. Finally, it outlines how the layered cellular structure of the neocortex can result in summation of signals from many neurons to be

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Book chapters on the topic "Electroencephalogram signal"

Yao, Dezhong. "Signal Space-Based EEG Inverse Solution." In The Physics and Mathematics of Electroencephalogram. CRC Press, 2024. http://dx.doi.org/10.1201/9781032639260-11.

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Ladeheng, Hasnisha, and Khairul Azami Sidek. "Early Depression Detection Using Electroencephalogram Signal." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97255-4_9.

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Fasil, O. K., R. Rajesh, and T. M. Thasleema. "Fusion of Signal and Differential Signal Domain Features for Epilepsy Identification in Electroencephalogram Signals." In Advances in Data and Information Sciences. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8360-0_12.

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Sheng, Hu, YangQuan Chen, and TianShuang Qiu. "Multifractional Property Analysis of Human Sleep Electroencephalogram Signals." In Fractional Processes and Fractional-Order Signal Processing. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2233-3_13.

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Adama, V. S., and Martin Bogdan. "Consciousness Detection in Complete Locked-In State Patients Using Electroencephalogram Coherency and Artificial Neural Networks." In Sensor Networks and Signal Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4917-5_29.

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Egorova, Lyudmila, Lev Kazakovtsev, and Elena Vaitekunene. "Nonlinear Features and Hybrid Optimization Algorithm for Automated Electroencephalogram Signal Analysis." In Springer Proceedings in Mathematics & Statistics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52965-8_19.

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Khandelwal, Sarika, Harsha R. Vyawahare, and Seema B. Rathod. "Automated Electroencephalogram Temporal Lobe Signal Processing for Diagnosis of Alzheimer Disease." In Data Analysis for Neurodegenerative Disorders. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2154-6_5.

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Manoj Kumar Swain, Chaudhuri, Ashish Singh, and Indrakanti Raghu. "Electroencephalogram (EEG) Signal Denoising Using Optimized Wavelet Transform (WT) A Study." In Computational Intelligence in Medical Decision Making and Diagnosis. CRC Press, 2023. http://dx.doi.org/10.1201/9781003309451-11.

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Rajakumar, Keerthik Dhivya, Rajeswari Jayaraj, Jagannath Mohan, and Adalarasu Kanagasabai. "Determination of Effects of Instrumental Music on Brain Signal Using Electroencephalogram." In Ergonomics for Design and Innovation. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94277-9_13.

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Kulkarni, Vinay, Yashwant Joshi, and Ramchandra Manthalkar. "A Clustering Approach for Sensory-Motor Cortex Signal Classification Using Electroencephalogram Signal for Brain-Computer Interface." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2631-0_26.

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Conference papers on the topic "Electroencephalogram signal"

Siam Dip, Muhammad Sudipto. "Cognitive Workload Classification Using Electroencephalogram Signal." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022139.

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N, Sridhar, and Uma A. "Survey on Signal Processing and Classification Algorithms for Depression Using Electroencephalogram Signals." In 2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC). IEEE, 2024. http://dx.doi.org/10.1109/icsseecc61126.2024.10649522.

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Chowdhury, Aditta, Mahir Foysal, Fabliha Enam Chowdhury, Monika Chowdhury, Diba Das, and Mehdi Hasan Chowdhury. "Design of FPGA-based Digital Filter for Electroencephalogram Signal Processing." In 2024 27th International Conference on Computer and Information Technology (ICCIT). IEEE, 2024. https://doi.org/10.1109/iccit64611.2024.11022355.

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Chen, Cai, Fulai Peng, Danyang Lv, et al. "Effect of Breathing Rate Change on Electroencephalogram Signal in Awake State." In 2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE). IEEE, 2024. http://dx.doi.org/10.1109/icsece61636.2024.10729511.

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Singh, Anmol Rattan, Gurjinder Singh, and Nitin Saluja. "Visualizing various Electroencephalogram signal components in case of different brain conditions." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862204.

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Mito, Shogo, Miho Miyajima, Hirofumi Tomioka, et al. "Postoperative Delirium Prediction Based on Preoperative Electrocardiogram and Electroencephalogram." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10848992.

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Aarthi, R., V. Maadhesh, P. Rajalakshmi, and Shanen J. Thomas. "Alzheimer’s Disease Prediction using Machine Learning techniques with the help of Electroencephalogram Brain Signal." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725703.

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Govindaraj, R., S. Ranjith., K. Deepthi, K. Kishore Babu, Rajesh G. Bodkhe, and Ayman Amer. "Developing an Electroencephalogram Based Robotic Motion Control System Using Brainwaves Enabled Signal Processing Technique." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910554.

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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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Xu Huang, Salahiddin Altahat, Dat Tran, and Dharmendra Sharma. "Human identification with electroencephalogram (EEG) signal processing." In 2012 International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2012. http://dx.doi.org/10.1109/iscit.2012.6380841.

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