Relevant bibliographies by topics / Electroencephalography (EEG) signal

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Electroencephalography (EEG) signal'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Electroencephalography (EEG) signal"

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Yuan, Lixue, Yinyan Fan, Quanxi Gan, and Huibin Feng. "Clinical Diagnosis of Psychiatry Based on Electroencephalography." Journal of Medical Imaging and Health Informatics 11, no. 3 (2021): 955–63. http://dx.doi.org/10.1166/jmihi.2021.3338.

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At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain
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Tran, Yvonne. "EEG Signal Processing for Biomedical Applications." Sensors 22, no. 24 (2022): 9754. http://dx.doi.org/10.3390/s22249754.

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Zhao, Yifan, Fei He, and Yuzhu Guo. "EEG Signal Processing Techniques and Applications." Sensors 23, no. 22 (2023): 9056. http://dx.doi.org/10.3390/s23229056.

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Garg, Malika. "Methods for the Analysis of EEG signals: A Review." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 873–76. http://dx.doi.org/10.22214/ijraset.2021.38072.

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Abstract: Electroencephalography (EEG) helps to predict the state of the brain. It tells about the electrical activity going on in the brain. Difference of the surface potential evolved from various activities get recorded as EEG. The analysis of these EEG signals is of utmost importance to solve the problems related to the brain. Signal pre-processing, feature extraction and classification are the main steps of the EEG signal analysis. In this article we discussed various processing techniques of EEG signals. Keywords: EEG, analysis, signal processing, feature extraction, classification
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Chaddad, Ahmad, Yihang Wu, Reem Kateb, and Ahmed Bouridane. "Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques." Sensors 23, no. 14 (2023): 6434. http://dx.doi.org/10.3390/s23146434.

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The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisi
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Shaima, Miqdad Mohamed Najeeb, Th. Salim Al Rikabi Haider, and Mohammed Ali Shaima. "Finding the discriminative frequencies of motor electroencephalography signal using genetic algorithm." TELKOMNIKA Telecommunication, Computing, Electronics and Control 19, no. 1 (2021): pp. 285~292. https://doi.org/10.12928/TELKOMNIKA.v19i1.17884.

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A crucial part of the brain-computer interface is a classification of electroencephalography (EEG) motor tasks. Artifacts such as eye and muscle movements corrupt EEG signal and reduce the classification performance. Many studies try to extract not redundant and discriminative features from EEG signals. Therefore, this study proposed a signal preprocessing and feature extraction method for EEG classification. It consists of removing the artifacts by using discrete fourier transform (DFT) as an ideal filter for specific frequencies. It also cross-correlates the EEG channels with the effective c
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Ivaldi, Marco, Lorenzo Giacometti, and David Conversi. "Quantitative Electroencephalography: Cortical Responses under Different Postural Conditions." Signals 4, no. 4 (2023): 708–24. http://dx.doi.org/10.3390/signals4040039.

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In this study, the alpha and beta spectral frequency bands and amplitudes of EEG signals recorded from 10 healthy volunteers using an experimental cap with neoprene jacketed electrodes were analysed. Background: One of the main limitations in the analysis of EEG signals during movement is the presence of artefacts due to cranial muscle contraction; the objectives of this study therefore focused on two main aspects: (1) validating a tool capable of decreasing movement artefacts, while developing a reliable method for the quantitative analysis of EEG data; (2) using this method to analyse the EE
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Zhu, Hangyu, Cong Fu, Feng Shu, Huan Yu, Chen Chen, and Wei Chen. "The Effect of Coupled Electroencephalography Signals in Electrooculography Signals on Sleep Staging Based on Deep Learning Methods." Bioengineering 10, no. 5 (2023): 573. http://dx.doi.org/10.3390/bioengineering10050573.

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The influence of the coupled electroencephalography (EEG) signal in electrooculography (EOG) on EOG-based automatic sleep staging has been ignored. Since the EOG and prefrontal EEG are collected at close range, it is not clear whether EEG couples in EOG or not, and whether or not the EOG signal can achieve good sleep staging results due to its intrinsic characteristics. In this paper, the effect of a coupled EEG signal in an EOG signal on automatic sleep staging is explored. The blind source separation algorithm was used to extract a clean prefrontal EEG signal. Then the raw EOG signal and cle
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Sema, Yildirim. "An Overview of ECG Artifact Detection in EEG Signals." Journal of Cardiovascular Medicine and Cardiology 12, no. 2 (2025): 017–21. https://doi.org/10.17352/2455-2976.000222.

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Electroencephalography (EEG) is an important technique for recording brain signals and is particularly used in the diagnosis and treatment of neurological diseases such as epilepsy. However, due to the complex nature of EEG signals, their interpretation is difficult and time-consuming. In EEG recordings, physiological noises such as eye movements (EOG) and electrocardiography (ECG) can affect the signals and hinder accurate diagnosis. This study emphasizes the importance of removing noise from EEG signals, with a focus on the impact of ECG-induced noise. The detection of QRS complexes in the E
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Stuart, N., J. Manners, B. Lechat, et al. "O010 Capturing Localised Electroencephalography Signals During Sleep using Tripolar Concentric Ring Electrodes." Sleep Advances 4, Supplement_1 (2023): A4. http://dx.doi.org/10.1093/sleepadvances/zpad035.010.

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Abstract Introduction Tri -concentric ring electrodes (TCRE) evaluate the current density underlying each electrode and provide improved signal-to-noise compared to conventional electroencephalography (EEG) electrodes. This pilot study used TCRE for the first time to compare TCRE versus more conventional EEG signals during sleep . Materials and Method Twenty healthy sleepers (8 males, mean±SD age 27.8±9.6 y) completed a 9-hr sleep opportunity. Eighteen TCRE electrodes were placed based on the 10-20 system, along with more conventional EEG recorded from the outer rings of paired TCRE electrodes
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Dissertations / Theses on the topic "Electroencephalography (EEG) signal"

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Birch, Gary Edward. "Single trial EEG signal analysis using outlier information." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/28626.

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The goal of this thesis work was to study the characteristics of the EEG signal and then, based on the insights gained from these studies, pursue an initial investigation into a processing method that would extract useful event related information from single trial EEG. The fundamental tool used to study the EEG signal characteristics was autoregressive modeling. Early investigations pointed to the need to employ robust techniques in both model parameter estimation and signal estimation applications. Pursuing robust techniques ultimately led to the development of a single trial processing meth
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Sellergren, Albin, Tobias Andersson, and Jonathan Toft. "Signal processing through electroencephalography : Independent project in electrical engineering." Thesis, Uppsala universitet, Elektricitetslära, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-298771.

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This report is about a project where electroencephalography (EEG) wasused to control a two player game. The signals from the EEG-electrodeswere amplified, filtered and processed. Then the signals from the playerswere compared and an algorithm decided what would happen in the gamedepending on which signal was largest. The controls and the gaming mechanismworked as intended, however it was not possible to gather a signal fromthe brain with the method used in this project. So ultimately the goal wasnot reached.<br>electroencephalography, EEG
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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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Salma, Nabila. "EEG Signal Analysis in Decision Making." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984237/.

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Decision making can be a complicated process involving perception of the present situation, past experience and knowledge necessary to foresee a better future. This cognitive process is one of the essential human ability that is required from everyday walk of life to making major life choices. Although it may seem ambiguous to translate such a primitive process into quantifiable science, the goal of this thesis is to break it down to signal processing and quantifying the thought process with prominence of EEG signal power variance. This paper will discuss the cognitive science, the signal proc
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Hodulíková, Tereza. "Analýza EEG během anestezie." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-220369.

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This master's thesis deals with the method of functional examination of brain electric activity. In the first part is description of central nervous system, method of electroencephalography and possible connections. Furthermor the project involves characteristic of EEG signal and its artifacts. It also includes signal processing and list of symptoms, which will be used for an analysis of the EEG during anesthesia. The second part of thesis involves development of application, which allow viewing and proccesing of EEG signal. In conclusion of thesis is carried out unequal segmentation and stati
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Mulyana, Ridwan S. "A Low Voltage, Low Power 4th Order Continuous-time Butterworth Filter for Electroencephalography Signal Recognition." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1281981810.

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Esteller, Rosana. "Detection of seizure onset in epileptic patients from intracranial EEG signals." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/15620.

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Shahriari, Sheyda. "Electroencephalography (EEG) profile and sense of body ownership : a study of signal processing, proprioception and tactile illusion." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/16299.

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With the ability to feel through artificial limbs, users regain more function and increasingly see the prosthetics as parts of their own bodies. So, main focus of this project was dedicated to recuperating sensation by deception both in sighted and unsighted patients, started with illusionary experiments on healthy volunteers, brain signals were captured with medical EEG headsets during these tests to have a better understanding of how the brain works during body ownership illusions. EEG results suggest that gender difference exists in the perception of body transfer illusion. Visual input can
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Bendoukha, Hocine. "Détection automatique des évènements paroxystiques dans le signal EEG." Rouen, 1989. http://www.theses.fr/1989ROUES029.

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Etude des méthodes de détection automatiques des évènements paroxystiques électroencéphalographiques chez des malades épileptiques. Les signaux EEG proviennent d'enregistrements ambulatoires de longue durée. L'approche consiste à définir des descripteurs quantitatifs et qualitatifs du signal EEG
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Courtellemont, Pierre. "Architecture multi-processeurs pour le traitement du signal EEG." Rouen, 1989. http://www.theses.fr/1989ROUES003.

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L'algorithme proposé repose sur le principe des moindres carrés récursifs. Il diffère des méthodes usuelles par une adaptation des paramètres qui se fait globalement sur une fenêtre d'observation et non à chaque nouvel échantillon. Cette technique a permis la mise au point d'un algorithme de détection à deux seuils successifs
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Books on the topic "Electroencephalography (EEG) signal"

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Sanei, Saeid. EEG signal processing. John Wiley & Sons, 2007.

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Genquan, Feng. EKG and EEG multiphase information analysis. American Medical Publishers, 1992.

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Freeman, Walter J. Imaging Brain Function With EEG: Advanced Temporal and Spatial Analysis of Electroencephalographic Signals. Springer New York, 2013.

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Lopes da Silva, F. H., 1935-, Storm van Leeuwen W, and Rémond Antoine, eds. Clinical applications of computer analysis of EEG and other neurophysiological signals. Elsevier, 1986.

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O, Quadens, and European Space Agency, eds. Analysis of EEG signals recorded in microgravity during parabolic flight using the method of strange attractor dimensions. ESA, 1999.

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Sanei, Saeid, and Jonathon A. Chambers. EEG Signal Processing. Wiley & Sons, Incorporated, John, 2013.

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Chambers, Jonathon, and Saeid Sanei. Eeg Signal Processing. John Wiley & Sons Inc, 2007.

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Zhang, Yanchun, Yan Li, and Siuly Siuly. EEG Signal Analysis and Classification: Techniques and Applications. Springer, 2018.

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Zhang, Yanchun, Yan Li, and Siuly Siuly. EEG Signal Analysis and Classification: Techniques and Applications. Springer, 2017.

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Zhang, Yanchun, Yan Li, and Siuly Siuly. EEG Signal Analysis and Classification: Techniques and Applications. Springer International Publishing AG, 2017.

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Book chapters on the topic "Electroencephalography (EEG) signal"

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Rizzo, Cristiano. "EEG Signal Acquisition." In Clinical Electroencephalography. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04573-9_5.

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Rizzo, Cristiano. "EEG Signal Analysis." In Clinical Electroencephalography. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04573-9_6.

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Bucci, Paola, and Silvana Galderisi. "Physiologic Basis of the EEG Signal." In Standard Electroencephalography in Clinical Psychiatry. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470974612.ch2.

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Sasidharan, Arun, Sumit Sharma, M. Vrinda, Kusumika Krora Dutta, and Chetan S. Mukundan. "Diagnostic Applications of EEG Signal Patterns in Neuroscience." In Advanced Electroencephalography Analytical Methods. CRC Press, 2024. http://dx.doi.org/10.1201/9781003252092-2.

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Lu, Xuejing, and Li Hu. "Electroencephalography, Evoked Potentials, and Event-Related Potentials." In EEG Signal Processing and Feature Extraction. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2_3.

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Al-Fraiji, Safaa S., and Dhiah Al-Shammary. "Survey for Electroencephalography EEG Signal Classification Approaches." In Mobile Computing and Sustainable Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1866-6_14.

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Khadka, Rabindra, Poushali Sengupta, Pedro G. Lind, and Anis Yazidi. "Quantization of Vision Transformer-Based Model for Real-Time EEG Classification." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86240-3_2.

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Abstract Electroencephalography (EEG) is a non-invasive and cost-effective tool for capturing brain signals. However, existing approaches based on deep learning models for classifying EEG signals are primarily trained on large EEG datasets and require extensive computational resources. This poses significant challenges for real-time processing, particularly in resource-constrained environments such as wearable devices and edge computing. Our work addresses these challenges by exploring quantization techniques to optimize a vision transformer model for real-time EEG classification. We apply pos
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Khang, Alex, Vladimir Hahanov, Eugenia Litvinova, et al. "Medical and Biomedical Signal Processing and Prediction Using the EEG Machine and Electroencephalography." In Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. CRC Press, 2024. http://dx.doi.org/10.1201/9781003429609-7.

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Li, Jingjing, Ye Yang, Zhexin Zhang, Yinan Zhao, Vargas Meza Xanat, and Yoichi Ochiai. "Visualizing the Electroencephalography Signal Discrepancy When Maintaining Social Distancing: EEG-Based Interactive Moiré Patterns." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05900-1_12.

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Yedukondalu, Jammisetty, M. Krishna Chaitanya, and Lakhan Dev Sharma. "Artifacts Removal in Electroencephalogram (EEG) Signals." In Advanced Electroencephalography Analytical Methods. CRC Press, 2024. http://dx.doi.org/10.1201/9781003252092-9.

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Conference papers on the topic "Electroencephalography (EEG) signal"

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Prasad, Himayavardhini Jagath, and G. Ramkumar. "A Robust Deep Learning Model to Predict Epileptic Seizures based on Electroencephalographic (EEG) Signals." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859429.

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Salahuddin Morsalin, S. M., and Shin-Chi Lai. "Front-end circuit design for electroencephalography (EEG) signal." In 2020 Indo-Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN). IEEE, 2020. http://dx.doi.org/10.1109/indo-taiwanican48429.2020.9181346.

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Mumtazi, Abdunnafi Naufal, Pringgo Widyo Laksono, Lobes Herdiman, and Susy Susmartini. "UAV pilot stress assessment based-on electroencephalography (EEG) signal." In PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON ENGINEERING, TECHNOLOGY, AND INDUSTRIAL APPLICATIONS 2021 (8th ICETIA 2021): Engineering, Environment, and Health: Exploring the Opportunities for the Future. AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0199489.

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Fatharani, Afif Firdaus, Khoerun Nisa Syaja’Ah, Mada Sanjaya Ws, Muhammad Insan Al-Amin, Rin Rin Nurmalasari, and Andang Saehu. "Electroencephalography (EEG) Signal Identification for Epilepsy Using Support Vector Machine (SVM)." In 2023 17th International Conference on Telecommunication Systems, Services, and Applications (TSSA). IEEE, 2023. http://dx.doi.org/10.1109/tssa59948.2023.10366941.

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Song, Yufan, ZhiHong Zhang, Te Hu, Xiaoliang Gong, and Asoke K. Nandi. "Identify of Spatial Similarity of Electroencephalography (EEG) during Working-Memory Maintenance." In 2019 27th European Signal Processing Conference (EUSIPCO). IEEE, 2019. http://dx.doi.org/10.23919/eusipco.2019.8902595.

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Farizal, Ahmad, Adhi Dharma Wibawa, Yuri Pamungkas, Monica Pratiwi, and Arbintoro Mas. "Classifying Known/Unknown Information in The Brain using Electroencephalography (EEG) Signal Analysis." In 2022 11th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS). IEEE, 2022. http://dx.doi.org/10.1109/eeccis54468.2022.9902928.

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Fu, Li-Min. "Knowledge-directed electroencephalography (EEG) signal analysis with recurrent context-learning neural networks." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Su-Shing Chen. SPIE, 1994. http://dx.doi.org/10.1117/12.179237.

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Ghorbanian, Parham, Subramanian Ramakrishnan, and Hashem Ashrafiuon. "Stochastic Oscillator Model of EEG Based on Information Content and Complexity." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-5929.

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In this study, a stochastic Duffing - van der Pol coupled two oscillator system is designed to produce output matching the information content, complexity measure, and frequency content of actual electroencephalography (EEG) signals. This is achieved by deriving the oscillator model parameters and noise intensity using an optimization scheme whose objective is to minimize a weighed average of errors in sample entropy, Shannon entropy, and powers of the major brain frequency bands. The signals produced by the optimal model are then compared with the EEG signal using phase portrait reconstructio
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Iapascurta, Victor, and Ion Fiodorov. "Kolmogorov-Chaitin Algorithmic Complexity for EEG Analysis." In 12th International Conference on Electronics, Communications and Computing. Technical University of Moldova, 2022. http://dx.doi.org/10.52326/ic-ecco.2022/cs.14.

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Electroencephalography as a generally accepted method of monitoring the electrical activity of brain neurons is widely used both in diseases and in healthy conditions. The recorded electrical signal is usually obtained from several electrodes located on the scalp. While EEG recording techniques are largely standardized, the interpretation of some aspects is still an open question. There is hardly questionable progress in detecting abnormal EEG signals known as seizures. A less explored field is the detection and classification of non-pathological conditions such as emotional and other function
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Alam, MD Erfanul, and Biswanath Samanta. "Performance Evaluation of Empirical Mode Decomposition for EEG Artifact Removal." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71647.

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Electroencephalography measures the sum of the post-synaptic potentials generated by many neurons having the same radial orientation with respect to the scalp. The electroen-cephalographic signals (EEG) are weak and often contaminated with different artifacts that have biological and external sources. Reliable pre-processing of the noisy, non-linear, and non-stationary brain activity signals is needed for successful extraction of characteristic features in motor imagery based brain-computer interface (MI-BCI). In this work, a signal processing technique, namely, empirical mode decomposition (E
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Reports on the topic "Electroencephalography (EEG) signal"

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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from t
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