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Статті в журналах з теми "Brain electrical signals"
Zhuang, Qiu Hui, Guo Jun Liu, Xiu Hua Fu, and San Qiang Wang. "Brain Electrical Signal Digital Processing System Design." Applied Mechanics and Materials 278-280 (January 2013): 958–61. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.958.
Повний текст джерелаThiagarajan, T. "Interpreting Electrical Signals from the Brain." Acta Physica Polonica B 49, no. 12 (2018): 2095. http://dx.doi.org/10.5506/aphyspolb.49.2095.
Повний текст джерелаDurka, P. J., J. Z. ygierewicz, and K. J. Blinowska. "Time-Frequency Analysis of Brain Electrical Activity – Adaptive Approximations." Methods of Information in Medicine 43, no. 01 (2004): 70–73. http://dx.doi.org/10.1055/s-0038-1633838.
Повний текст джерелаCharchekhandra, Barbara. "The Reading and Analyzing Of The Brain Electrical Signals To Execute a Control Command and Move an Automatic Arm." Pure Mathematics for Theoretical Computer Science 1, no. 1 (2023): 08–16. http://dx.doi.org/10.54216/pmtcs.010101.
Повний текст джерелаGarg, Malika. "Methods for the Analysis of EEG signals: A Review." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 873–76. http://dx.doi.org/10.22214/ijraset.2021.38072.
Повний текст джерелаBashashati, Ali, Mehrdad Fatourechi, Rabab K. Ward, and Gary E. Birch. "A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals." Journal of Neural Engineering 4, no. 2 (March 27, 2007): R32—R57. http://dx.doi.org/10.1088/1741-2560/4/2/r03.
Повний текст джерелаNaresh, B., S. Rambabu, and D. Khalandar Basha. "ARM Controller and EEG based Drowsiness Tracking and Controlling during Driving." International Journal of Reconfigurable and Embedded Systems (IJRES) 6, no. 3 (May 28, 2018): 127. http://dx.doi.org/10.11591/ijres.v6.i3.pp127-132.
Повний текст джерелаMarkovinović, Ivan, Miroslav Vrankić, and Saša Vlahinić. "Removal of eye-blink artifacts from EEG signal." Engineering review 40, no. 2 (April 1, 2020): 101–11. http://dx.doi.org/10.30765/er.40.2.11.
Повний текст джерелаChandran, Kalyana Sundaram, and T. Kiruba Angeline. "Identification of Disease Symptoms Using Taste Disorders in Electroencephalogram Signal." Journal of Computational and Theoretical Nanoscience 17, no. 5 (May 1, 2020): 2051–56. http://dx.doi.org/10.1166/jctn.2020.8848.
Повний текст джерелаHirai, Yasuharu, Eri Nishino, and Harunori Ohmori. "Simultaneous recording of fluorescence and electrical signals by photometric patch electrode in deep brain regions in vivo." Journal of Neurophysiology 113, no. 10 (June 2015): 3930–42. http://dx.doi.org/10.1152/jn.00005.2015.
Повний текст джерелаДисертації з теми "Brain electrical signals"
Khodam, Hazrati Mehrnaz [Verfasser]. "On human-machine interfaces based on electrical brain signals / Mehrnaz Khodam Hazrati." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2014. http://d-nb.info/1054365644/34.
Повний текст джерелаYao, Bing. "ANALYSIS OF ELECTRICAL AND MAGNETIC BIO-SIGNALS ASSOCIATED WITH MOTOR PERFORMANCE AND FATIGUE." Case Western Reserve University School of Graduate Studies / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=case1140813534.
Повний текст джерелаMouradi, Rand. "Wireless Signals and Male Fertility." Cleveland State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=csu1318571631.
Повний текст джерелаWheland, David Stanford. "Signal processing methods for brain connectivity." Thesis, University of Southern California, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3610033.
Повний текст джерелаAlthough the human brain has been studied for centuries, and the advent of non-invasive brain imaging modalities in the last century in particular has led to significant advances, there is much left to discover. Current neuroscientific theory likens the brain to a highly interconnected network whose behavior can be better understood by determining its network connections. Correlation, coherence, Granger causality, and blind source separation (BSS) are frequently used to infer this connectivity. Here I propose novel methods to improve their inference from neuroimaging data. Correlation and coherence suffer from being unable to differentiate between direct and indirect connectivity. While partial correlation and partial coherence can mitigate this problem, standard methods for calculating these measures result in significantly reduced statistical inference power and require greater numbers of samples. To address these drawbacks I propose novel methods based on a graph pruning algorithm that leverage the connectivity sparsity of the brain to improve the inference of partial correlation and partial coherence. These methods are demonstrated in applications. In particular, partial correlation is explored in both cortical thickness data from structural MR images and resting state data from functional MR images, and partial coherence is explored in invasive electrophysiological measurements in non-human primates. Granger causality is able to differentiate between direct and indirect connectivity by default and like partial coherence is readily applicable to time series. However unlike partial coherence, it uses the temporal ordering implied by the time series to infer a type of causality on the connectivity. Despite its differences, the inference of Granger causality can also be improved using a similar graph pruning algorithm, and I describe such an extension here. The method is also applied to explore electrophysiological interactions in non-human primate data. BSS methods seek to decompose a dataset into a linear mixture of sources such that the sources best match some target property, such as independence. The second order blind identification (SOBI) BSS method has a number of properties particularly well-suited for data on the cerebral cortex and relies on the calculation of lagged covariance matrices. However while these lagged covariance matrices are readily available in one-dimensional data, they are not straightforward to calculate on the two-dimensional cortical manifold on which certain types of neuroimaging data lie. To address this, I propose a method for calculating the covariance matrices on the cortical manifold and demonstrate its application to cortical gray matter thickness and curvature data on the cerebral cortex.
Purdon, Patrick L. (Patrick Lee) 1974. "Signal processing in functional magnetic resonance imaging (fMRI) of the brain." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50032.
Повний текст джерелаLi, Kun. "Advanced Signal Processing Techniques for Single Trial Electroencephalography Signal Classification for Brain Computer Interface Applications." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3484.
Повний текст джерелаDemanuele, Charmaine. "Analysis of very low frequency oscillations in electromagnetic brain signal recordings." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/159351/.
Повний текст джерелаRenfrew, Mark E. "A Comparison of Signal Processing and Classification Methods for Brain-Computer Interface." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1246474708.
Повний текст джерелаMountney, John M. "Particle Filtering Programmable Gate Array Architecture for Brain Machine Interfaces." Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/140741.
Повний текст джерелаPh.D.
Decoding algorithms for brain machine interfaces map neural firing times to the underlying biological output signal through dynamic tuning functions. In order to maintain an accurate estimate of the biological signal, the state of the tuning function parameters must be tracked simultaneously. The evolution of this system state is often estimated by an adaptive filter. Recent work demonstrates that the Bayesian auxiliary particle filter (BAPF) offers improved estimates of the system state and underlying output signal over existing techniques. Performance of the BAPF is evaluated under both ideal conditions and commonly encountered spike detection errors such as missed and false detections and missorted spikes. However, this increase in neuronal signal decoding accuracy is at the expense of an increase in computational complexity. Real-time execution of the BAPF algorithm for neural signals using a sequential processor becomes prohibitive as the number of particles and neurons in the obs
Temple University--Theses
Dharwarkar, Gireesh. "Using Temporal Evidence and Fusion of Time-Frequency Features for Brain-Computer Interfacing." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/830.
Повний текст джерелаКниги з теми "Brain electrical signals"
S, Gevins A., and Rémond Antoine, eds. Methods of analysis of brain electrical and magnetic signals. Amsterdam: Elsevier, 1987.
Знайти повний текст джерела1962-, Laguna Pablo, ed. Bioelectrical signal processing in cardiac and neurological applications. Amsterdam: Elsevier Academic Press, 2005.
Знайти повний текст джерелаGevins, A. S. Methods of Analysis of Brain Electrical and Magnetic Signals. Elsevier Publishing Company, 1987.
Знайти повний текст джерелаSeeck, Margitta, L. Spinelli, Jean Gotman, and Fernando H. Lopes da Silva. Combination of Brain Functional Imaging Techniques. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0046.
Повний текст джерелаGage, Greg, and Tim Marzullo. How Your Brain Works. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/12429.001.0001.
Повний текст джерелаCampagnola, Luke, and Paul Manis. Patch Clamp Recording in Brain Slices. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199939800.003.0001.
Повний текст джерелаAmzica, 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.
Повний текст джерелаWadman, Wytse J., and Fernando H. Lopes da Silva. Biophysical Aspects of EEG and MEG Generation. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0004.
Повний текст джерелаSutter, 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.
Повний текст джерелаЧастини книг з теми "Brain electrical signals"
Başar, Erol. "Electrical Signals from the Brain." In Springer Series in Synergetics, 21–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72192-2_3.
Повний текст джерелаRamón, Fidel, Jesús Hernández-Falcón, and Theodore H. Bullock. "Brain Electrical Signals in Unrestrained Crayfish." In Modern Approaches to the Study of Crustacea, 7–13. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0761-1_2.
Повний текст джерелаDhiman, Rohtash, Priyanka, and J. S. Saini. "Wavelet Analysis of Electrical Signals from Brain: The Electroencephalogram." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 283–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37949-9_24.
Повний текст джерелаGotham, Solomon, and G. Sasibushana Rao. "A Suitable Approach in Extracting Brain Source Signals from Disabled Patients." In Lecture Notes in Electrical Engineering, 721–31. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2728-1_69.
Повний текст джерелаSamarpita, Soumya, and Rabinarayan Satpathy. "Impact of EEG Signals on Human Brain Before and After Meditation." In Lecture Notes in Electrical Engineering, 331–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9090-8_29.
Повний текст джерелаBaşar, Erol. "The Brain of the Sleeping Cat: Dynamics of Electrical Signals." In Springer Series in Synergetics, 75–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-59893-7_6.
Повний текст джерелаChoong, Wen Yean, Wan Khairunizam, Murugappan Murugappan, Mohammad Iqbal Omar, Siao Zheng Bong, Ahmad Kadri Junoh, Zuradzman Mohamad Razlan, A. B. Shahriman, and Wan Azani Wan Mustafa. "Hurst Exponent Based Brain Behavior Analysis of Stroke Patients Using EEG Signals." In Lecture Notes in Electrical Engineering, 925–33. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5281-6_66.
Повний текст джерелаOrjuela-Cañón, Alvaro D., Osvaldo Renteria-Meza, Luis G. Hernández, Andrés F. Ruíz-Olaya, Alexander Cerquera, and Javier M. Antelis. "Self-organizing Maps for Motor Tasks Recognition from Electrical Brain Signals." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 458–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75193-1_55.
Повний текст джерелаZhang, Wulin, Yuqiang Chen, and Jianfeng Ma. "Method of Extracting Audio-Visual Induced Brain Signals Based on Deep Neural Network." In Lecture Notes in Electrical Engineering, 1201–7. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0115-6_137.
Повний текст джерелаMousavi, Seyed Aliakbar, Muhammad Rafie Hj Mohd Arshad, Hasimah Hj Mohamed, Putra Sumari, and Saeed Panahian Fard. "P300 Detection in Electroencephalographic Signals for Brain–Computer Interface Systems: A Neural Networks Approach." In Lecture Notes in Electrical Engineering, 355–63. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01766-2_41.
Повний текст джерелаТези доповідей конференцій з теми "Brain electrical signals"
Wagh, Kalyani P., and K. Vasanth. "Review on Various Emotional Disorders by Analyzing Human Brain Signal Patterns (EEG Signals)." In 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). IEEE, 2019. http://dx.doi.org/10.1109/icecct.2019.8869453.
Повний текст джерелаMatsuno, Kevin, and Vidya K. Nandikolla. "Machine Learning Using Brain Computer Interface System." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23394.
Повний текст джерелаNicolae, Irina E., and Mihai Ivanovici. "Complexity of EEG Brain Signals Triggered by Fractal Visual Stimuli." In 2021 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP) & 2021 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). IEEE, 2021. http://dx.doi.org/10.1109/optim-acemp50812.2021.9590052.
Повний текст джерелаMeghdadi, Amir H., Witold Kinsner, and Reza Fazel-Rezai. "Characterization of healthy and epileptic brain EEG signals by monofractal and multifractal analysis." In 2008 Canadian Conference on Electrical and Computer Engineering - CCECE. IEEE, 2008. http://dx.doi.org/10.1109/ccece.2008.4564773.
Повний текст джерелаJiang, Huaiguang, Jun Jason Zhang, Adam Hebb, and Mohammad H. Mahoor. "Time-frequency analysis of brain electrical Signals for behvior recognition in patients with Parkinson's disease." In 2013 Asilomar Conference on Signals, Systems and Computers. IEEE, 2013. http://dx.doi.org/10.1109/acssc.2013.6810621.
Повний текст джерелаAlrajeh, N. A., K. W. Divine, T. P. Sullivan, and N. M. Bukhari. "Controlling a Valve Actuator and the Flow of Fluids with Interpreted Brain Signals." In SPE/IADC Middle East Drilling Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214559-ms.
Повний текст джерелаKawala-Sterniuk, Aleksandra, Jaroslaw Zygarlicki, Adam Lysiak, Barbara Grochowicz, Mariusz Pelc, Waldemar Bauer, Dawid Baczkowicz, et al. "Influence of the variables describing brain signals on the performance of the Naive Bayesian Classifier." In 2022 Progress in Applied Electrical Engineering (PAEE). IEEE, 2022. http://dx.doi.org/10.1109/paee56795.2022.9966567.
Повний текст джерелаNguyen, Thanh An, and Yong Zeng. "Analysis of Design Activities Using EEG Signals." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28477.
Повний текст джерелаContreras, Stewart, and V. Sundararajan. "Visual Imagery Classification Using Shapelets of EEG Signals." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71291.
Повний текст джерелаGonal, Jayalaxmi S., and Vinayadatt V. Kohir. "Classification of brain MR images using wavelets texture features and k-Means classfier." In 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO). IEEE, 2015. http://dx.doi.org/10.1109/eesco.2015.7253749.
Повний текст джерелаЗвіти організацій з теми "Brain electrical signals"
Research, Gratis. Green Light: A New Preventive Therapy for Migraine. Gratis Research, November 2020. http://dx.doi.org/10.47496/gr.blog.03.
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