Relevant bibliographies by topics / EEG signal pattern

Contents

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

Academic literature on the topic 'EEG signal pattern'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "EEG signal pattern"

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Ferber, G. "Syntactic Pattern Recognition of Intermittent EEG Activity." Methods of Information in Medicine 24, no. 02 (1985): 79–84. http://dx.doi.org/10.1055/s-0038-1635362.

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SummaryUp to now, computerised processing of EEG signals has entered the domain of clinical application at most with respect to background activity and. the recognition of some intermittent basic patterns.Although the EEG is a multichannel signal, this recognition is performed separately for each channel, taking into account at most the immediate past and future. The result is a set of intermittent basic patterns. They are to be looked at as constituents of “complex patterns” which correspond to the entities used in the visual assessment.In this paper we present a method of uniting these basic
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Sheikh, Md. Rabiul Islam, and Shakibul Islam Md. "Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink." Indian Journal of Signal Processing (IJSP) 1, no. 3` (2021): 1–7. https://doi.org/10.54105/ijsp.C1008.081321.

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The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and anal
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Esther Rani, Dr P., and B. V. V. S. R. K. K. Pavan. "Multi-class EEG signal classification with statistical binary pattern synergic network for schizophrenia severity diagnosis." AIMS Biophysics 10, no. 3 (2023): 347–71. http://dx.doi.org/10.3934/biophy.2023021.

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<abstract> <p>Electroencephalography (EEG) is a widely used medical procedure that helps to identify abnormalities in brain wave patterns and measures the electrical activity of the brain. The EEG signal comprises different features that need to be distinguished based on a specified property to exhibit recognizable measures and functional components that are then used to evaluate the pattern in the EEG signal. Through extraction, feature loss is minimized with the embedded signal information. Additionally, resources are minimized to compute the vast range of data accurately. It is
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Lv, Chao, and Bo Song. "Classification of epileptic EEG based on improved empirical wavelet transform." Journal of Physics: Conference Series 2400, no. 1 (2022): 012010. http://dx.doi.org/10.1088/1742-6596/2400/1/012010.

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Abstract Electroencephalography (EEG) is the most commonly used method in the diagnosis of epilepsy diseases. In order to identify epilepsy EEG signals more effectively, an automatic identification method of epilepsy EEG signals based on improved empirical wavelet transform (EWT) is proposed. Firstly, in view of the difficulty of spectral division in the EEG signal processing of epilepsy by empirical wavelet transform, an improvement measure is proposed, that is, the average difference spectrum of the signal is obtained to replace the signal spectrum in the empirical wavelet transform, and the
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Tanko, Dahiru, Prabal Datta Barua, Sengul Dogan, et al. "EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals." Physiological Measurement 43, no. 3 (2022): 035002. http://dx.doi.org/10.1088/1361-6579/ac59dc.

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Abstract Objective. The main objective of this work is to present a hand-modelled one-dimensional signal classification system to detect Attention-Deficit Hyperactivity Disorder (ADHD) disorder using electroencephalography (EEG) signals. Approach. A novel handcrafted feature extraction method is presented in this research. Our proposed method uses a directed graph and an eight-pointed star pattern (EPSPat). Also, tunable q wavelet transforms (TQWT), wavelet packet decomposition (WPD), statistical extractor, iterative Chi2 (IChi2) selector, and the k-nearest neighbors (kNN) classifier have been
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Chang, Won-Du, and Chang-Hwan Im. "Enhanced Template Matching Using Dynamic Positional Warping for Identification of Specific Patterns in Electroencephalogram." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/528071.

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Template matching is an approach for signal pattern recognition, often used for biomedical signals including electroencephalogram (EEG). Since EEG is often severely contaminated by various physiological or pathological artifacts, identification and rejection of these artifacts with improved template matching algorithms would enhance the overall quality of EEG signals. In this paper, we propose a novel approach to improve the accuracy of conventional template matching methods by adopting the dynamic positional warping (DPW) technique, developed recently for handwriting pattern analysis. To vali
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S, Aravinth Raj, and Veeramuthu Venkatesh. "Local Pattern Transformation Technique for Brain Signal EEG." International Journal of Security and Its Applications 13, no. 4 (2019): 67–74. http://dx.doi.org/10.33832/ijsia.2019.13.4.07.

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Miranda, Michael Gabriel, Renato Alberto Salinas, Ulrich Raff, and Oscar Magna. "Wavelet Design for Automatic Real-Time Eye Blink Detection and Recognition in EEG Signals." International Journal of Computers Communications & Control 14, no. 3 (2019): 375–87. http://dx.doi.org/10.15837/ijccc.2019.3.3516.

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The blinking of an eye can be detected in electroencephalographic (EEG) recordings and can be understood as a useful control signal in some information processing tasks. The detection of a specific pattern associated with the blinking of an eye in real time using EEG signals of a single channel has been analyzed. This study considers both theoretical and practical principles enabling the design and implementation of a system capable of precise real-time detection of eye blinks within the EEG signal. This signal or pattern is subject to considerable scale changes and multiple incidences. In our
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Lew, Kai Liang, Kok Swee Sim, and Zehong Ting. "Deep Learning Approach EEG Signal Classification." JOIV : International Journal on Informatics Visualization 8, no. 3-2 (2024): 1693. https://doi.org/10.62527/joiv.8.3-2.2959.

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The introduction of deep learning technology has greatly benefited the neuroscience field by improving the electroencephalogram (EEG) signal analysis. These technologies have greatly improved the understanding of complex brain activity by interpreting the signal as normal or abnormal. The EEG signal requires expertise to interpret the pattern, and only then can the EEG signal be differentiated as normal or abnormal. However, some variations always complicate the analysis of the EEG signal by creating noise in the signal. This paper introduces a deep learning model, NeuroNetFlex (NFF), to class
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Pratibha, Rana* Ms. Jyotsna Singh. "A REVIEW ON CLASSIFICATION OF EEG SIGNAL DATA." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 188–93. https://doi.org/10.5281/zenodo.51018.

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A Brain computing system is a communication channel between the human or animal brain and external environment; it’s a collaboration in which brain controls a mechanical device as a natural part of its representation of the body. It is a type of communication which practically uses both software and hardware systems for the communication. It’s the type of system which provides a new way of communication between non-muscular channels with the external hardware. Basically Brain computing system is broadly divided into two major categories 1) EEG data signal based pattern recognition
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Dissertations / Theses on the topic "EEG signal pattern"

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Giovanini, Renato de Macedo [UNESP]. "SSVEP-EEG signal pattern recognition system for real-time brain-computer interfaces applications." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/151710.

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Submitted by Renato de Macedo Giovanini null (renato81243@aluno.feis.unesp.br) on 2017-09-25T14:52:54Z No. of bitstreams: 1 dissertacao_renato_de_macedo_giovanini_2017_final.pdf: 10453769 bytes, checksum: 7f7e2415a0912fae282affadea2685b8 (MD5)<br>Approved for entry into archive by Monique Sasaki (sayumi_sasaki@hotmail.com) on 2017-09-27T20:24:55Z (GMT) No. of bitstreams: 1 giovanini_rm_me_ilha.pdf: 10453769 bytes, checksum: 7f7e2415a0912fae282affadea2685b8 (MD5)<br>Made available in DSpace on 2017-09-27T20:24:55Z (GMT). No. of bitstreams: 1 giovanini_rm_me_ilha.pdf: 10453769 bytes, checksum: 7
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Giovanini, Renato de Macedo. "SSVEP-EEG signal pattern recognition system for real-time brain-computer interfaces applications /." Ilha Solteira, 2017. http://hdl.handle.net/11449/151710.

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Orientador: Aparecido Augusto de Carvalho<br>Resumo: There are, nowadays, about 110 million people in the world who live with some type of severe motor disability. Specifically in Brazil, about 2.2% of the population are estimated to live with a condition of difficult locomotion. Aiming to help these people, a vast variety of devices, techniques and services are currently being developed. Among those, one of the most complex and challenging techniques is the study and development of Brain-Computer Interfaces (BCIs). BCIs are systems that allow the user to communicate with the external world co
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Kortelainen, J. (Jukka). "EEG-based depth of anesthesia measurement:separating the effects of propofol and remifentanil." Doctoral thesis, Oulun yliopisto, 2011. http://urn.fi/urn:isbn:9789514294853.

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Abstract Within the last few decades, electroencephalogram (EEG) has become a widely used tool for the automatic assessment of depth of anesthesia. The EEG-based depth of anesthesia measurement has been associated with several advantages, such as a decreased incidence of intraoperative awareness and recall, faster recovery, and reduced consumption of anesthetics. However, the measurement is challenged by simultaneous administration of different types of anesthetics, which is the common practice in the operating rooms today. Especially, the assessment of depth of anesthesia induced by supplemen
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Radüntz, Thea. "Kontinuierliche Bewertung psychischer Beanspruchung an informationsintensiven Arbeitsplätzen auf Basis des Elektroenzephalogramms." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17417.

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Die Informations- und Kommunikationstechnologien haben die Arbeitswelt grundlegend verändert. Durch den Einsatz komplexer, hochautomatisierter Systeme werden an die kognitive Leistungsfähigkeit und Belastbarkeit von Arbeitnehmern hohe Anforderungen gestellt. Über die Ermittlung der psychischen Beanspruchung des Menschen an Arbeitsplätzen mit hohen kognitiven Anforderungen wird es möglich, eine Über- oder Unterbeanspruchung zu vermeiden. Gegenstand der Dissertation ist deshalb die Entwicklung, Implementierung und der Test eines neuen Systems zur kontinuierlichen Bewertung psychischer Beanspruch
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Perera, Biyagama. "Identification of EEG signal patterns between adults with dyslexia and normal controls." Thesis, Perera, Biyagama (2017) Identification of EEG signal patterns between adults with dyslexia and normal controls. PhD thesis, Murdoch University, 2017. https://researchrepository.murdoch.edu.au/id/eprint/39939/.

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Electroencephalography (EEG) is one of the most useful techniques used to represent behaviours of the brain and helps explore valuable insights through the measurement of brain electrical activity. Hence, it plays a vital role in detecting neurological disorders such as epilepsy. Dyslexia is a hidden learning disability with a neurological origin affecting a significant amount of the world population. Studies show unique brain structures and behaviours in individuals with dyslexia and these variations have become more evident with the use of techniques such as EEG, Functional Magnetic Resonanc
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Derner, Marlene [Verfasser]. "Pattern Recognition in Intracranial EEG Signals and Prediction of Memory / Marlene Derner." Bonn : Universitäts- und Landesbibliothek Bonn, 2021. http://d-nb.info/1229989153/34.

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Vezard, Laurent. "Réduction de dimension en apprentissage supervisé. Application à l'étude de l'activité cérébrale." Phd thesis, Université Sciences et Technologies - Bordeaux I, 2013. http://tel.archives-ouvertes.fr/tel-00926845.

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L'objectif de ce travail est de développer une méthode capable de déterminer automatiquement l'état de vigilance chez l'humain. Les applications envisageables sont multiples. Une telle méthode permettrait par exemple de détecter automatiquement toute modification de l'état de vigilance chez des personnes qui doivent rester dans un état de vigilance élevée (par exemple, les pilotes ou les personnels médicaux). Dans ce travail, les signaux électroencéphalographiques (EEG) de 58 sujets dans deux états de vigilance distincts (état de vigilance haut et bas) ont été recueillis à l'aide d'un casque à
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Dornhege, Guido. "Increasing information transfer rates for brain-computer interfacing." Phd thesis, [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=98051276X.

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Tobón, Cardona M. (Marcela). "Automatic detection of early repolarization pattern in ECG signals with waveform prototype-based learning." Master's thesis, University of Oulu, 2018. http://jultika.oulu.fi/Record/nbnfioulu-201809062750.

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Abstract. Early repolarization (ER) pattern was considered a benign finding until 2008, when it was associated with sudden cardiac arrest (SCA). Since then, the interest of the medical community on the topic has grown, stating the need to develop methods to detect the pattern and analyze the risk of SCA. This thesis presents an automatic detection method of ER using supervised classification. The novelty of the method lies in the features used to construct the classification models. The features consist of prototypes that are composed by fragments of the ECG signal where the ER pattern is loca
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Acar, Erman. "Classification Of Motor Imagery Tasks In Eeg Signal And Its Application To A Brain-computer Interface For Controlling Assistive Environmental Devices." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612994/index.pdf.

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This study focuses on realization of a Brain Computer Interface (BCI)for the paralyzed to control assistive environmental devices. For this purpose, different motor imagery tasks are classified using different signal processing methods. Specifically, band-pass filtering, Laplacian filtering, and common average reference (CAR) filtering areused to enhance the EEG signal. For feature extraction<br>Common Spatial Pattern (CSP), Power Spectral Density (PSD), and Principal Component Analysis (PCA) are tested. Linear Feature Normalization (LFN), Gaussian Feature Normalization (GFN), and Unit-norm F
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Books on the topic "EEG signal pattern"

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Tatum, William O., Claus Reinsberger, and Barbara A. Dworetzky. Artifacts of Recording and Common Errors in Interpretation. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0011.

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This chapter examines the fundamental neurophysiological principles involved in determining electroencephalographic (EEG) artifact and provides general instructions for minimizing the risk of error during clinical interpretation. Examples from EEG recordings are given to illustrate common artifacts that may be challenging to the reader because they mimic epileptiform pattern associated to people with epilepsy. Emerging techniques used to detect and reduce artifact without altering the electrocerebral signal are being developed to limit the contamination. While many artifacts are easy to recogn
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Joao Paulo do Vale Madeiro, Paulo Cesar Cortez, José Maria Da Silva Monteiro Filho, and Angelo Roncalli Alencar Brayner. Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition. Elsevier Science & Technology Books, 2018.

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Joao Paulo do Vale Madeiro, Paulo Cesar Cortez, José Maria Da Silva Monteiro Filho, and Angelo Roncalli Alencar Brayner. Developments and Applications for ECG Signal Processing: Modeling, Segmentation, and Pattern Recognition. Elsevier Science & Technology, 2018.

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Drislane, Frank W., Susan T. Herman, and Peter W. Kaplan. Nonconvulsive Status Epilepticus. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0021.

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The clinical presentation and encephalographic (EEG) findings of nonconvulsive status epilepticus (NCSE) can be complicated, making diagnosis difficult. There are generalized (e.g., absence status) and focal (e.g., aphasic status, complex partial status) forms. Some patients are responsive but have cognitive or other neurologic deficits; others are less responsive or even comatose. Increasingly, the diagnosis of NCSE is considered in intensive care unit patients. Here, without clinical signs of seizures such as convulsions, EEG is critical in diagnosis, but there is uncertainty about which EEG
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Sutter, Raoul, Peter W. Kaplan, and Donald L. Schomer. Historical Aspects of Electroencephalography. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0001.

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Electroencephalography (EEG), a dynamic real-time recording of electrical neocortical brain activity, began in the 1600s with the discovery of electrical phenomena and the concept of an “action current.” The galvanometer was introduced in the 1800s and the first bioelectrical observations of human brain signals were made in the 1900s. Certain EEG patterns were associated with brain disorders, increasing the clinical and scientific use of EEG. In the 1980s, technical advances allowed EEGs to be digitized and linked with videotape recording. In the 1990s, digital data storage increased and compu
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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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Pitt, Matthew. Motor unit anatomy and physiology. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198754596.003.0006.

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This chapter focuses on the signals recorded with needle electromyography (EMG) and the measurement of their specific parameters. These parameters include duration, amplitude, number of phases, and stability. The concept of the electrophysiologic biopsy and the explanation of unusual findings seen on EMG are introduced. In relation to the interference pattern, discussions of the firing rate, recruitment order, and interference pattern are given. Moving from the theoretical explanation of the findings, the problems of the accurate quantitative analysis of the motor unit potential are discussed
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Celesia, Gastone G., and Neal S. Peachey. Visual Evoked Potentials and Electroretinograms. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0041.

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Electrophysiological testing of vision permits the objective assessment of the function of the retina, visual pathways, and cortices. This chapter covers visual evoked potentials (VEPs) and electroretinography (ERG). Flash ERG is useful in evaluating the outer retinal function and specifically helping in the diagnosis of retinal degeneration, monitoring the progress of retinal diseases, monitoring the retinal toxicity of drugs, and understanding the pathophysiology of retinal disorders. VEPs to various stimuli are useful in evaluating macular disorders, diagnosing optic neuropathies, detecting
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Busuioc, Aristita, and Alexandru Dumitrescu. Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.770.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art
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Book chapters on the topic "EEG signal pattern"

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Patrone, Martín, Federico Lecumberry, Álvaro Martín, Ignacio Ramirez, and Gadiel Seroussi. "EEG Signal Pre-Processing for the P300 Speller." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25751-8_67.

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Sergejew, Alex A., and Ah Chung Tsoi. "Markovian Analysis of EEG Signal Dynamics in Obsessive-Compulsive Disorder." In Advances in Processing and Pattern Analysis of Biological Signals. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-9098-6_3.

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Altahat, Salahiddin, Xu Huang, Dat Tran, and Dharmendra Sharma. "People Identification with RMS-Based Spatial Pattern of EEG Signal." In Algorithms and Architectures for Parallel Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33065-0_33.

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Das, Khakon, and Ashish Khare. "α and β-Testing of an Epileptic Seizure Detection Algorithm on Pre-ictal, Ictal, and Inter-ictal Part of EEG Signal." In Computational Intelligence in Pattern Recognition. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3734-9_21.

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Lopes da Silva, Fernando H., Jan-Pieter Pijn, and Demetrios N. Velis. "Signal Processing of EEG: Evidence for Chaos or Noise. An Application to Seizure Activity in Epilepsy." In Advances in Processing and Pattern Analysis of Biological Signals. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-9098-6_2.

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Zhang, Xiaodong, Weifeng Diao, and Zhiqiang Cheng. "Wavelet Transform and Singular Value Decomposition of EEG Signal for Pattern Recognition of Complicated Hand Activities." In Digital Human Modeling. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73321-8_35.

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Kumar, Shiu, Alok Sharma, and Tatsuhiko Tsunoda. "Subject-Specific-Frequency-Band for Motor Imagery EEG Signal Recognition Based on Common Spatial Spectral Pattern." In PRICAI 2019: Trends in Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29911-8_55.

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Benni, Rashmi, Kishan Joshi, and Samarth Patil. "Enhancing Accuracy in Predicting Personalized Sleep Pattern Disorders Through Ensemble Learning Techniques Using EEG Signal Analysis." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2694-6_39.

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Mutasim, Aunnoy K., Rayhan Sardar Tipu, M. Raihanul Bashar, Md Kafiul Islam, and M. Ashraful Amin. "Computational Intelligence for Pattern Recognition in EEG Signals." In Computational Intelligence for Pattern Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89629-8_11.

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Gath, Isak, Bernard Harris, Yoram Salant, Claude Feuerstein, Olaf Henriksen, and Gérard Rondouin. "Processing of Epileptic EEG." In Advances in Processing and Pattern Analysis of Biological Signals. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-9098-6_5.

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Conference papers on the topic "EEG signal pattern"

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Jin, Bu, Shuning Xue, and Jing Liu. "CSANet: enhancing cross-subject EEG classification with contrastive subject adaptation." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2025), edited by Haiquan Zhao and Xinhua Tang. SPIE, 2025. https://doi.org/10.1117/12.3071292.

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Hu, Yuhang, and Deli Fu. "Research on EEG signal decomposition and classification based on grey wolf optimized MVMD." In International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2025), edited by Haiquan Zhao and Xinhua Tang. SPIE, 2025. https://doi.org/10.1117/12.3071263.

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Wang, Yuxin, Bao Liu, and Yuqi Tang. "An EEG Signal Analysis Method Based on Common Spatial Pattern and Support Vector Machine Algorithm." In 2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2024. https://doi.org/10.1109/prai62207.2024.10826935.

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Arnesano, Marco. "Development and Application of EEG Signal Pattern Analysis and Artificial Neural Network for Indoor Comfort Measurement." In 2024 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv). IEEE, 2024. http://dx.doi.org/10.1109/metrolivenv60384.2024.10615866.

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Singh, Anmol Rattan, Gurjinder Singh, G. Sunil, and Mohhamied Husaein Fallaah. "Deciphering Neural Patterns using LSTM from EEG Signals." In 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI). IEEE, 2025. https://doi.org/10.1109/iatmsi64286.2025.10985203.

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Reilly, Rosemarie, Xiaoshu Xu, and Jerald Jones. "Neural Network Application to Acoustic Emission Signal Processing." In CORROSION 1992. NACE International, 1992. https://doi.org/10.5006/c1992-92242.

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Abstract Artificial neural systems, also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, non-linear, mathematical relationship or transform. These constructs have two significant properties that have proven usefu
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Rock, Alan, Xiaoshu Xu, and Jerald E. Jones. "Typical Neural Network Applications in Signal Processing and Process Modelling." In CORROSION 1992. NACE International, 1992. https://doi.org/10.5006/c1992-92265.

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Abstract Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, non-linear, mathematical relationship or transform. These constructs have two significant properties that have prove
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Ghonchi, Hamidreza, Tom Foulsham, and Saideh Ferdowsi. "Assessing Neural Patterns of Anxiety Using Deep Learning: An EEG Study." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715099.

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Liang, Jiahui, Guangming Wang, Shanshan Jia, Dong Wang, and Gang Wang. "Application of EEG and EMG Signals in Multi-Classification of Epilepsy." In 2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML). IEEE, 2024. https://doi.org/10.1109/prml62565.2024.10779807.

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Alzubaidi, Laith H., Priyadharshini C, Vasanthakumar G U, Srinivas Samala, and S. Ananthi. "Signal Processing Techniques for Classifying Heartbeat Patterns in ECG Signals for Cardiac Disease Detection Using Graph Convolutional Networks." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872147.

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Reports on the topic "EEG signal pattern"

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Martinez, Kimberly D., and Gaojian Huang. Exploring the Effects of Meaningful Tactile Display on Perception and Preference in Automated Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2164.

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There is an existing issue in human-machine interaction, such that drivers of semi-autonomous vehicles are still required to take over control of the vehicle during system limitations. A possible solution may lie in tactile displays, which can present status, direction, and position information while avoiding sensory (e.g., visual and auditory) channels overload to reliably help drivers make timely decisions and execute actions to successfully take over. However, limited work has investigated the effects of meaningful tactile signals on takeover performance. This study synthesizes literature i
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Bobashev, Georgiy, John Holloway, Eric Solano, and Boris Gutkin. A Control Theory Model of Smoking. RTI Press, 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.

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We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term (“loading up” after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdra
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Farahbod, A. M., and J. F. Cassidy. An overview of seismic attenuation in the Eastern Canadian Arctic and the Hudson Bay Complex, Manitoba, Newfoundland and Labrador, Nunavut, Ontario, and Quebec. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330396.

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In this study we investigated coda-wave attenuation (QC) from the eastern Canadian Arctic in Nunavut and the Hudson Bay complex including portions of northern Manitoba, Ontario, Quebec and Labrador. We used earthquake recordings from 15 broadband and 3 short period seismograph stations of the Canadian National Seismic Network (CNSN) and 29 broadband stations of the POLARIS network across the region. Our dataset is comprised of 637 earthquakes recorded between 1985 and 2021 with magnitudes ranging from 1.3 to 6.1, depths from 0 to 20 km and epicentral distances of 5 to 100 km. This gives a tota
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Farahbod, A. M., and J. F. Cassidy. An overview of seismic attenuation in the Charlevoix Seismic Zone, southern Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/332158.

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We investigate seismic attenuation characteristics of the Charlevoix Seismic Zone. This zone is located ~100 km downstream from Quebec City and is the most seismically active region of eastern Canada. We used earthquake recordings from 8 seismograph stations of the Canadian National Seismic Network (CNSN) across the region. Our dataset is comprised of 584 earthquakes recorded between 1992 and 2022 with magnitudes ranging from 2.0 to 5.4, depths from 0 to 30 km and epicentral distances of 5 to 100 km. This gives a total of 1490 high signal-to-noise (S/N) traces (S/N?5.0) useful for QC calculati
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Farahbod, A., and J. F. Cassidy. Spatial and temporal variations in seismic coda Q attenuation in the lower St. Lawrence region, southeastern Quebec. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/332027.

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We investigate seismic attenuation characteristics of the Lower St. Lawrence seismic zone in southeastern Quebec. This zone is located ~400 km downstream from Quebec City and is between the Quebec North Shore and the Lower St. Lawrence. We used earthquake recordings from 5 broadband and 5 short period seismograph stations of the Canadian National Seismic Network (CNSN) across the region. Our dataset is comprised of 847 earthquakes recorded between 1985 and 2022 with magnitudes ranging from 2.0 to 5.1, depths from 0 to 30 km and epicentral distances of 5 to 100 km. This gives a total of 446 hig
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Farahbod, A. M., and J. F. Cassidy. An overview of seismic attenuation in the Northern Appalachians Seismic Zone, New Brunswick and Nova Scotia. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329702.

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In this study we investigated coda-wave attenuation (QC) from the northern Appalachian region of eastern Canada in the two provinces of New Brunswick and Nova Scotia. We used earthquake recordings from 8 broadband and 2 short period seismograph stations of the Canadian National Seismograph Network (CNSN) across the region. Our dataset is comprised of 476 earthquakes recorded between 1983 and 2021 with magnitudes ranging from 1.5 to 4.1, depths from 0 to 20 km (with the vast majority being &amp;amp;lt;10 km) and epicentral distances of 5 to 100 km. This gives a total of 261 high signalto- noise
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Hefetz, Abraham, and Justin O. Schmidt. Use of Bee-Borne Attractants for Pollination of Nonrewarding Flowers: Model System of Male-Sterile Tomato Flowers. United States Department of Agriculture, 2003. http://dx.doi.org/10.32747/2003.7586462.bard.

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The use of bee natural product for enhancing pollination is especially valuable in problematic crops that are generally avoided by bees. In the present research we attempted to enhance bee visitation to Male Sterile (M-S) tomato flowers generally used in the production of hybrid seeds. These flowers that lack both pollen and nectar are unattractive to bees that learn rapidly to avoid them. The specific objects were to elucidate the chemical composition of the exocrine products of two bumble bee species the North American Bombus impatiens and the Israeli B. terrestris. Of these, to isolate and
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Dickman, Martin B., and Oded Yarden. Regulation of Early Events in Hyphal Elongation, Branching and Differentiation of Filamentous Fungi. United States Department of Agriculture, 2000. http://dx.doi.org/10.32747/2000.7580674.bard.

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In filamentous fungi, hyphal elongation, branching and morphogenesis are in many cases the key to successful saprophytic and pathogenic fungal proliferation. The understanding of the fungal morphogenetic response to environmental cues is in its infancy. Studies concerning the regulation of fungal growth and development (some of which have been obtained by the participating collaborators in this project) point to the fact that ser/thr protein kinases and phosphatases are (i) involved in the regulation of such processes and (ii) share common structural and functional features between saprophytes
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