Academic literature on the topic 'Electroencephalographic (EEG)'
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Journal articles on the topic "Electroencephalographic (EEG)"
Al-Sulaiman, Abdulsalam. "Electroencephalographic (EEG) patterns in hydrocephalus." Electroencephalography and Clinical Neurophysiology 87, no. 2 (August 1993): S79. http://dx.doi.org/10.1016/0013-4694(93)91212-j.
Full textBruhn, Jörgen, Thomas W. Bouillon, Andreas Hoeft, and Steven L. Shafer. "Artifact Robustness, Inter- and Intraindividual Baseline Stability, and Rational EEG Parameter Selection." Anesthesiology 96, no. 1 (January 1, 2002): 54–59. http://dx.doi.org/10.1097/00000542-200201000-00015.
Full textIvanov, A. A. "The structure of modern EEG recorder." Epilepsy and paroxysmal conditions 14, no. 4 (January 18, 2023): 362–78. http://dx.doi.org/10.17749/2077-8333/epi.par.con.2022.138.
Full textPoliti, Keren, Sara Kivity, Hadassa Goldberg-Stern, Ayelet Halevi, and Avinoam Shuper. "Selective Mutism and Abnormal Electroencephalography (EEG) Tracings." Journal of Child Neurology 26, no. 11 (May 18, 2011): 1377–82. http://dx.doi.org/10.1177/0883073811406731.
Full textD'Souza, Delon, Gosala R. K. Sarma, and Elizabeth V. T. "Teaching Electroencephalography: Persistent Altered Sensorium with Ominous Appearing Electroencephalographic Activity." International Journal of Epilepsy 05, no. 02 (October 2018): 110–11. http://dx.doi.org/10.1055/s-0038-1676560.
Full textStankevich, Lev A., Sabina S. Amanbaeva, and Aleksandr V. Samochadin. "User Authentication by Electroencephalographic Signals when Blinkin." Computer tools in education, no. 3 (September 30, 2019): 52–69. http://dx.doi.org/10.32603/2071-2340-2019-3-52-69.
Full textQuesney, L. F. "Preoperative Electroencephalographic Investigation in Frontal Lobe Epilepsy: Electroencephalographic and Electrocorticographic Recordings." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 18, S4 (November 1991): 559–63. http://dx.doi.org/10.1017/s0317167100032698.
Full textMcFarland, Dennis J., William A. Sarnacki, and Jonathan R. Wolpaw. "Electroencephalographic (EEG) control of three-dimensional movement." Journal of Neural Engineering 7, no. 3 (May 11, 2010): 036007. http://dx.doi.org/10.1088/1741-2560/7/3/036007.
Full textYeh, Ta-Chuan, Cathy Chia-Yu Huang, Yong-An Chung, Jooyeon Jamie Im, Yen-Yue Lin, Chin-Chao Ma, Nian-Sheng Tzeng, Chuan-Chia Chang, and Hsin-An Chang. "High-Frequency Transcranial Random Noise Stimulation over the Left Prefrontal Cortex Increases Resting-State EEG Frontal Alpha Asymmetry in Patients with Schizophrenia." Journal of Personalized Medicine 12, no. 10 (October 7, 2022): 1667. http://dx.doi.org/10.3390/jpm12101667.
Full textSheikh, Hesham, Dennis J. McFarland, William A. Sarnacki, and Jonathan R. Wolpaw. "Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans." Neuroscience Letters 345, no. 2 (July 2003): 89–92. http://dx.doi.org/10.1016/s0304-3940(03)00470-1.
Full textDissertations / Theses on the topic "Electroencephalographic (EEG)"
Gasparini, John M. "An Electroencephalographic (EEG) Study of Hypofrontality during Music Induced Flow Experiences." Thesis, Northcentral University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10830810.
Full textSince Csikszentmihalyi identified the psychological experience of flow over 40 years ago, the experiences have been heralded as the optimum human function and prescriptive to high levels of well-being and quality of life. Csikszentmihalyi theorized that flow represented an autonomous reality that represented an altered state unlike any other human experience. Flow states emerged from intrinsically motivated behavior that represented a fragile balance between the level of enjoyment from novel task stimulation and a sense of self-efficacy required to meet the specific task demands. However, flow is not well understood and research is skewed toward to phenomenological investigations that described the nature of the experience and many of the significant variables of interest across a diverse range of activities. The lack of experimental exploration of flow has created fundamental research gaps. The general problem is that flow is predictive and related to positive psychological outcomes; however, current assessment methodologies and research have not provided the functional neuroanatomy involved. The purpose of this quantitative experimental study was to examine the hypofrontality theory that a flow state occurs concurrently with decreased cognitive activation in the frontal cortex (hypofrontality) during the flow phenomena. Participants consisted of expert piano players that were assessed for changes in alpha activity in the frontal cortex during a flow and non-flow condition. Results from the paired samples paired t-test conducted revealed there were statistically significant differences in alpha power in the experimental conditions (DV) versus the control conditions (IV; M = 93, SD = 105, N = 14), t(13) = 3.29, p = .006. These results supported the main hypothesis that there is increased alpha power in the frontal cortex during flow states. This finding provides the first empirically validated biomarker for a flow. These results will assist future research to understand flow experiences as a conceptually unambiguous variable.
Lahr, Jacob [Verfasser], and Andreas [Akademischer Betreuer] Schulze-Bonhage. "Electromyographic signals in intracranial electroencephalographic recordings = Elektromyographische Signale in intrakraniellen EEG-Aufnahmen." Freiburg : Universität, 2012. http://d-nb.info/1123473927/34.
Full textATTARD, TREVISAN ADRIAN. "NOVEL COMPUTATIONAL ELECTROENCEPHALOGRAPHIC (EEG) METHODOLOGIES FOR AUTISM MANAGEMENT AND EPILEPTIC SEIZURE PREDICTION." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/333759.
Full textSAIBENE, AURORA. "A Flexible Pipeline for Electroencephalographic Signal Processing and Management." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/360550.
Full textThe electroencephalogram (EEG) provides the non-invasive recording of brain activities and functions as time-series, characterized by a temporal and spatial (sensor-dependent) resolution, and by brain condition-bounded frequency bands. Moreover, it presents some cost-effective device solutions. However, the resulting EEG signals are non-stationary, time-varying, and heterogeneous, being recorded from different subjects and being influenced by specific experimental paradigms, environmental conditions, and devices. Moreover, they are easily affected by noise and they can be recorded for a limited time, thus they provide a restricted number of brain conditions to work with. Therefore, in this thesis a flexible pipeline for signal processing and management is proposed to have a better understanding of the EEG signals and exploit them for a variety of applications. Moreover, the proposed flexible pipeline is divided in 4 modules concerning signal pre-processing, normalization, feature computation and management, and EEG data classification. The EEG signal pre-processing exploits the multivariate empirical mode decomposition (MEMD) to decompose the signal in oscillatory modes, called intrinsic mode functions (IMFs), and uses an entropy criterion to select the most relevant IMFs that should maintain the natural brain dynamics, while discarding uninformative components. The resulting relevant IMFs are then exploited for signal substitution and data augmentation. Even though MEMD is adapt to the EEG signal non-stationarity, further processing steps should be undertaken to mitigate these data heterogeneity. Therefore, a normalization step is introduced to obtain comparable data inter- and intra-subject and between different experimental conditions, allowing the extraction of general features in the time, frequency, and time-frequency domain for EEG signal characterization. Even though the use of a variety of feature types may provide new data patterns, they may also present some redundancies and increase the risk of incurring in classification problems like curse of dimensionality and overfitting. Therefore, a feature selection based on evolutionary algorithms is proposed to have a completely data-driven approach, exploiting both supervised and unsupervised learning models, and suggesting new stopping criteria for a modified genetic algorithm implementation. Moreover, the use of different learning models may affect the discrimination of different brain conditions. The introduction of deep learning models may provide a strategy to learn directly from the available data. By suggesting a proper input formulation it could be possible to maintain the EEG data time, frequency, and spatial information, while avoiding too complex architectures. Therefore, using different processing steps and approaches may provide general or experimental specific strategies to manage the EEG signal, while maintaining its natural characteristics.
Barne, Louise Catheryne. "Electroencephalographic correlates of temporal learning." reponame:Repositório Institucional da UFABC, 2016.
Find full textDissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Neurociência e Cognição, 2016.
We constantly learn and update our predictions about when events we cause will occur. This flexibility is important to program motor actions and to estimate when errors have been made. However, the mechanisms that govern learning and updating in temporal domain are largely unknown. In order to clarify these mechanisms we had three mains objectives: 1. To describe how we learn a new temporal relation between two events and how expectation is updated based on new information; 2. To describe the neural correlates underlying temporal learning and temporal updating; 3. To investigate temporal learning in two different sensory modalities: vision and audition, in order to verify whether such processes occur independently of sensory modality. In order to achieve the objectives, we developed two different experiments with electroencephalography recordings. In the first experiment, we aimed to answer the first two objectives by developing a behavioral task in which participants had to monitor whether a temporal error had been made. Results evidenced a rapid temporal adjustment by the participants to a new temporal relation. Temporal errors evoked electrophysiological markers classically related to error coding as frontal theta oscillations and feedback-related negativity. Delta phase was modulated by behavioral adjustments, suggesting its importance in temporal prediction updating. In conclusion, low frequency oscillations appear to be modulated in error coding and temporal learning. The second experiment investigated temporal learning in two different sensory modalities. Results indicated that time perception is biased differently depending on temporal marker sensory modality. Besides, we found that intertrial phase coherence of theta oscillations was modulated by expectation on both sensory conditions. However, such result occurs on central electrodes analysis, but not on sensory electrodes analysis, indicating a supramodal mechanism of temporal prediction.
Lorensen, Tamara Dawn. "Defining anterior posterior dissociation patterns in electroencephalographic comodulation in Chronic Fatigue Syndrome and depression." Queensland University of Technology, 2004. http://eprints.qut.edu.au/16552/.
Full textHajipour, Sardouie Sepideh. "Signal subspace identification for epileptic source localization from electroencephalographic data." Thesis, Rennes 1, 2014. http://www.theses.fr/2014REN1S185/document.
Full textIn the process of recording electrical activity of the brain, the signal of interest is usually contaminated with different activities arising from various sources of noise and artifact such as muscle activity. This renders denoising as an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications such as source localization. In this thesis, we propose six methods for noise cancelation of epileptic signals. The first two methods, which are based on Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) frameworks, are used to denoise interictal data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing stages including spike peak detection, extraction of exact time support of spikes and clustering of spikes involved in each source of interest. Two other methods, called Time Frequency (TF)-GEVD and TF-DSS, are also proposed in order to denoise ictal EEG signals for which the time-frequency signature is extracted using the Canonical Correlation Analysis method. We also propose a deflationary Independent Component Analysis (ICA) method, called JDICA, that is based on Jacobi-like iterations. Moreover, we propose a new direct algorithm, called SSD-CP, to compute the Canonical Polyadic (CP) decomposition of complex-valued multi-way arrays. The proposed algorithm is based on the Simultaneous Schur Decomposition (SSD) of particular matrices derived from the array to process. We also propose a new Jacobi-like algorithm to calculate the SSD of several complex-valued matrices. The last two algorithms are used to denoise both interictal and ictal data. We evaluate the performance of the proposed methods to denoise both simulated and real epileptic EEG data with interictal or ictal activity contaminated with muscular activity. In the case of simulated data, the effectiveness of the proposed algorithms is evaluated in terms of Relative Root Mean Square Error between the original noise-free signals and the denoised ones, number of required ops and the location of the original and denoised epileptic sources. For both interictal and ictal data, we present some examples on real data recorded in patients with a drug-resistant partial epilepsy
Mosse, Leah Kathryn. "Electroencephalographic (EEG) biofeedback treatment for children with attention deficit disorders in a school setting." Thesis, University of North Texas, 2001. https://digital.library.unt.edu/ark:/67531/metadc3005/.
Full textGirão, Leonor Lopes Ribeiro da Silva. "Neural correlations during brain activation in arithmetical tasks – an approach using electroencephalographic data." Master's thesis, Faculdade de Ciências e Tecnologia, 2010. http://hdl.handle.net/10362/4257.
Full textThe present study aims at examining the correlation among different brain areas while the subjects performed an arithmetical task, and how these differ from the mental relations in the same subjects during a resting state. In order to this, both linear and nonlinear methods were used, i.e., both algorithms capable of detecting linear relations and algorithms capable of detecting correlations without assuming any type of parametric relationship between the signals were implemented. The first algorithm that was implemented was the cross-correlation function, which gives an estimate of how much two signals are linearly correlated, and estimates the delay between them, thus permitting to make inferences on causality. Furthermore, this algorithm was validated using the statistic method called surrogation, in order to test for the applicability of the algorithm on the signals that were to be processed. The next part of the study consisted on implementing two analogous algorithms, the coefficient of determination and the nonlinear regression coefficient. These coefficients both measure the fraction of reduction of variance that can be obtained by estimating the relationship between two signals according to a fitted line, the difference being that the former assumes a linear relation between both sets of samples and the latter doesn‟t previously assume any type of relationship between the signals. The main differences in correlation that were observed between the state of mental rest and between the arithmetic task performance were that in the former more brain sites were correlated, whereas during the task this synchrony was mainly verified between frontal and parietal areas, showing a decrease in the other locations. Furthermore, the estimates provided by the linear and nonlinear algorithms were very similar, suggesting that in this case the relationships among different neural networks were mainly linear, and thus validating the application of linear methods in this type of analysis in particular cases. Regarding the estimation of delays between signals and inferences on causality, no conclusive results were attained.
Martínez, Cristina G. B. "Nonlinear signal analysis of micro and macro electroencephalographic recordings from epilepsy patients." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/670397.
Full textEl uso de medidas de análisis no lineales de señales para caracterizar registros electroencefalográficos (EEG) puede ser clave para una mejor comprensión de las dinámicas cerebrales subyacentes. En trastornos neurológicos como la epilepsia, estas dinámicas están alteradas a consecuencia de una coordinación perturbada entrepoblaciones neuronales. El objetivo de esta tesis es caracterizarel intervalo de registros de EEG libre de crisis epilépticas de pacientes con epilepsia mediante técnicas de análisis no lineales de señales para investigar si este tipo de análisis puede contribuir ala localización del SOZ (en inglés, Seizure onset zone), la región del cerebro donde se pueden registrar las descargas iniciales de las crisis epilépticas. Con este propósito, utilizamos una puntuación de predictibilidad no lineal corregida por sustitutos y una medida de interdependencia no lineal corregida por sustitutos para analizar registros EEG de pacientes con epilepsia grabados durante noches completas implantados con electrodos híbridos equipados con macro- y microcontactos. Nuestros resultados demuestran que el análisis combinado de macro- y micro-registros de EEG puede ayudar a aumentar el grado en el que el análisis cuantitativo de EEG puede contribuir al diagnóstico de pacientes con epilepsia.
L’ús de mesures d’anàlisi de senyals no lineals per la caracterització de registres encefalogràfics (EEG) pot ser clau per una millor comprensió de les dinàmiques cerebrals subjacents. En trastorns neurològics com l’epilèpsia, aquestes dinàmiques estan alterades a conseqüència d’una coordinació pertorbada entre poblacions neuronals. L’objectiu d’aquesta tesi doctoral és caracteritzar l’interval de registres EEG lliures de crisis epilèptiques en pacients amb epilèpsia mitjançant tècniques d’anàlisi de senyals no lineals, per tal d’investigar si aquest tipus d’anàlisi pot contribuir a la localització de la SOZ (en anglès, Seizure onset zone), la regió del cervell on es poden registrar les primeres descàrregues de la crisi. Amb aquesta finalitat, utilitzem una puntuació de previsibilitat no lineal corregida mitjançant substituts i una mesura d’interdependència no lineal corregida per substituts per analitzar registres EEG de pacients amb epilèpsia. Aquests han sigut enregistrats durant nits completes amb elèctrodes híbrids equipats amb macro- i microcontactes. Els resultats obtinguts demostren que l’anàlisi combinat de macro- i microregistres en l’EEG pot ajudar a augmentar el grau de contribució de l’anàlisi quantitatiu de l’EEG dins el diagnòstic de pacients amb epilèpsia.
Books on the topic "Electroencephalographic (EEG)"
Erlichman, Martin. Electroencephalographic (EEG) video monitoring. Rockville, MD: U.S. Dept. of Health and Human Services, Public Health Service, Agency for Health Care Policy and Research, 1990.
Find full textFreeman, Walter J. Imaging Brain Function With EEG: Advanced Temporal and Spatial Analysis of Electroencephalographic Signals. New York, NY: Springer New York, 2013.
Find full text1931-, Spehlmann Rainer, ed. Spehlmann's EEG primer. 2nd ed. Amsterdam: Elsevier, 1991.
Find full textR, Hughes John. EEG in clinical practice. 2nd ed. Boston: Butterworth-Heinemann, 1994.
Find full textFisch, Bruce J. Fisch and Spehlmann's EEG primer: Basic principles of digital and analog EEG. 3rd ed. Amsterdam: Elsevier, 1999.
Find full textS, Ebersole John, ed. Ambulatory EEG monitoring. New York: Raven Press, 1989.
Find full text1933-, Zschocke S., and Speckmann Erwin-Josef, eds. Basic mechanisms of the EEG. Boston: Birkhäuser, 1993.
Find full textH, Chiappa Keith, ed. The EEG of drowsiness. New York: DEMOS Publications, 1987.
Find full textRajna, P. The EEG atlas of adulthood epilepsy. [Budapest]: Innomark, 1990.
Find full textEugene, Tolunsky, ed. A primer of EEG: With a mini-atlas. Philadelphia, PA: Butterworth-Heinemann, 2003.
Find full textBook chapters on the topic "Electroencephalographic (EEG)"
Michalopoulos, K., M. Zervakis, and N. Bourbakis. "Current Trends in ERP Analysis Using EEG and EEG/fMRI Synergistic Methods." In Modern Electroencephalographic Assessment Techniques, 323–50. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/7657_2013_67.
Full textFingelkurts, Andrew A., and Alexander A. Fingelkurts. "Operational Architectonics Methodology for EEG Analysis: Theory and Results." In Modern Electroencephalographic Assessment Techniques, 1–59. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/7657_2013_60.
Full textTripoliti, Evanthia E., Michalis Zervakis, and Dimitrios I. Fotiadis. "Computer-Based Assessment of Alzheimer’s Disease Employing fMRI and/or EEG: A Comprehensive Review." In Modern Electroencephalographic Assessment Techniques, 351–83. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/7657_2014_70.
Full textVarotto, Giulia, Laura Tassi, Fabio Rotondi, Roberto Spreafico, Silvana Franceschetti, and Ferruccio Panzica. "Effective Brain Connectivity from Intracranial EEG Recordings: Identification of Epileptogenic Zone in Human Focal Epilepsies." In Modern Electroencephalographic Assessment Techniques, 87–101. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/7657_2013_61.
Full textKlonowski, Wlodzimierz. "Fractal Analysis of Electroencephalographic Time Series (EEG Signals)." In Springer Series in Computational Neuroscience, 413–29. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3995-4_25.
Full textHarsha, Shivangi Madhavi, and Jayashri Vajpai. "Fuzzy Inference System for Classification of Electroencephalographic (EEG) Data." In Intelligent Human Computer Interaction, 35–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44689-5_4.
Full textFan, Miaolin, Vladimir Miskovic, Chun-An Chou, Sina Khanmohammadi, Hiroki Sayama, and Brandon E. Gibb. "Classification Analysis of Chronological Age Using Brief Resting Electroencephalographic (EEG) Recordings." In Brain Informatics and Health, 96–104. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23344-4_10.
Full textKemperman, C. J. F., S. L. H. Notermans, and R. Wevers. "Relationship Between Electroencephalographic (EEG) Synchronization and Plasma Dopamine-beta-hydroxylase (DBH) Activity in Patients." In Verhandlungen der Deutschen Gesellschaft für Neurologie, 712–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83201-7_221.
Full textBrienza, Marianna, Chiara Davassi, and Oriano Mecarelli. "Ambulatory EEG." In Clinical Electroencephalography, 297–304. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04573-9_17.
Full textTassi, Laura. "Invasive EEG." In Clinical Electroencephalography, 319–28. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04573-9_19.
Full textConference papers on the topic "Electroencephalographic (EEG)"
Fuad, N., R. Jailani, W. R. W. Omar, A. H. Jahidin, and M. N. Taib. "Three dimension 3D signal for electroencephalographic (EEG)." In 2012 IEEE Control and System Graduate Research Colloquium (ICSGRC). IEEE, 2012. http://dx.doi.org/10.1109/icsgrc.2012.6287173.
Full textRegueiro, Matthew, Bhagyashree Shirke, Mandy Chiu, Nicholas Capobianco, and Kiran George. "Electroencephalographic (EEG) Analysis of Individuals Experiencing Acute Mental Stress." In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2019. http://dx.doi.org/10.1109/uemcon47517.2019.8993062.
Full textAgashe, Harshavardhan A., and Jose L. Contreras-Vidal. "Decoding the evolving grasping gesture from electroencephalographic (EEG) activity." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6610817.
Full textMarquez L., Alejandro P., and Roberto Munoz G. "Analysis and classification of electroencephalographic signals (EEG) to identify arm movements." In 2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). IEEE, 2013. http://dx.doi.org/10.1109/iceee.2013.6676033.
Full text"Performance Evaluation of Methods for Correcting Ocular Artifacts in Electroencephalographic (EEG) Recordings." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004199101260132.
Full textYanar, Hilmi, and Yuriy Mishchenko. "A hidden Markov model of electroencephalographic brain activity for advanced EEG-based brain computer interfaces." In 2016 24th Signal Processing and Communication Application Conference (SIU). IEEE, 2016. http://dx.doi.org/10.1109/siu.2016.7495747.
Full textKorb, Sebastian, Didier Grandjean, and Klaus Scherer. "Investigating the production of emotional facial expressions: a combined electroencephalographic (EEG) and electromyographic (EMG) approach." In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813388.
Full textZilidou, Vasiliki I., Christos A. Frantzidis, Ana B. Vivas, Maria Karagianni, and Panagiotis D. Bamidis. "Towards Multi-parametric Hub Scoring of Functional Cortical Brain Networks: An Electroencephalographic (EEG) Study Across Lifespan." In 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2017. http://dx.doi.org/10.1109/cbms.2017.149.
Full textTing Li, Jun Hong, and Jinhua Zhang. "Electroencephalographic (EEG) control of cursor movement in three-dimensional scene based on Small-World neural network." In 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icicisys.2010.5658416.
Full textKongara, Kavitha, Lorna Johnson, Nikki J Kells, Craig B Johnson, Venkata SR Dukkipati, and Sheryl L Mitchinson. "Alteration of Electroencephalographic Responses to Castration in Cats by Administration of Opioids EEG responses to castration in cats." In Annual International Conference on Advances in Veterinary Science Research. Global Science & Technology Forum (GSTF), 2013. http://dx.doi.org/10.5176/2382-5685_vetsci13.58.
Full textReports on the topic "Electroencephalographic (EEG)"
Engheta, Nader, Edward N. Pugh, and Jr. Selected Electromagnetic Problems in Electroencephalography (EEG) Fields in Complex Media and Small Radiating Elements in Dissipative Media. Fort Belvoir, VA: Defense Technical Information Center, November 2004. http://dx.doi.org/10.21236/ada428876.
Full textWhitaker, Keith W., and W. D. Hairston. Assessing the Minimum Number of Synchronization Triggers Necessary for Temporal Variance Compensation in Commercial Electroencephalography (EEG) Systems. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada568650.
Full textRawal, Sandhya. Weighted Phase Lag Index (WPLI) as a Method for Identifying Task-Related Functional Networks in Electroencephalography (EEG) Recordings during a Shooting Task. Fort Belvoir, VA: Defense Technical Information Center, August 2011. http://dx.doi.org/10.21236/ada558399.
Full textHamlin, 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.), December 2021. http://dx.doi.org/10.21079/11681/42562.
Full textEEG data might help identify children at risk for social anxiety. ACAMH, March 2021. http://dx.doi.org/10.13056/acamh.15048.
Full text