Дисертації з теми "Brain electrical signals"
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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 джерелаBozorgzadeh, Bardia. "Integrated Microsystems for High-Fidelity Sensing and Manipulation of Brain Neurochemistry." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1432223568.
Повний текст джерелаJanwattanapong, Panuwat. "Connectivity Analysis of Electroencephalograms in Epilepsy." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3906.
Повний текст джерелаFoldes, Stephen Thomas. "Command of a Virtual Neuroprosthesis-Arm with Noninvasive Field Potentials." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1290109568.
Повний текст джерелаRajaei, Hoda. "Brain Connectivity Networks for the Study of Nonlinear Dynamics and Phase Synchrony in Epilepsy." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3882.
Повний текст джерелаMalý, Lukáš. "Ovládání invalidního vozíku pomocí klasifikace EEG signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221361.
Повний текст джерелаTernstedt, Andreas. "Pattern recognition with spiking neural networks and the ROLLS low-power online learning neuromorphic processor." Thesis, Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-63033.
Повний текст джерелаChandran, Subash K. S. "Analysis of Local Field Potential and Gamma Rhythm Using Matching Pursuit Algorithm." Thesis, 2016. http://etd.iisc.ernet.in/handle/2005/2771.
Повний текст джерелаBai-Ling, Shu, and 許百靈. "The effects of various pCO2, pO2, and electrical stimulation on functional Magnetic Resonance signals in rat brains." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/45920387071182632938.
Повний текст джерела國立臺灣大學
物理學研究所
87
The methodologies of applying magnetic resonance imaging (MRI) to study functional effects in live animals are widely used today. One of these methodologies is to acquire functional images in rat brains while studying the increase of cerebral blood flow with elevation of CO2 concentration. The MR signals are enhanced by increasing blood flow, which causes slight difference of proton density in cerebral vessels. Another methodology is to study MR signal changes under different levels of blood oxygenation. Since the ability of hemoglobin combining with oxygen is different at various O2 concentrations and blood flow, this produces deoxyhemoglobin. Various amounts of deoxyhemoglobin in blood vessels would have different contributions to MR signal changes. Generally, various levels of blood oxygenation causing MR signal changes are called BOLD effect. Furthermore, neuronal activation would cause local oxygenation alterations in cerebral blood vessels and result in BOLD effect. Therefore the stimulation of nerves would be studied by acquiring functional images.
Zhu, Quan. "Signal Processing for Time Series of Functional Magnetic Resonance Imaging." Diss., 2008. http://hdl.handle.net/10161/602.
Повний текст джерела"Scheduling Neural Sensors to Estimate Brain Activity." Master's thesis, 2012. http://hdl.handle.net/2286/R.I.14853.
Повний текст джерелаDissertation/Thesis
M.S. Electrical Engineering 2012
Chaturvedi, Vikram. "Low Power and Low Area Techniques for Neural Recording Application." Thesis, 2012. http://hdl.handle.net/2005/3167.
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