Searched for: subject%3A%22Brain%255C+modeling%22
(1 - 6 of 6)
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Murgoci, Vlad (author)
This study investigates the relationship between deep learning models and the human brain, specifically focusing on the prediction of brain activity in response to static visual stimuli using functional magnetic resonance imaging (fMRI). By leveraging intermediate outputs of pre-trained convolutional neural networks (CNNs) with feature-weighted...
bachelor thesis 2023
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Shi, S. (author), Cheng, Xiaodong (author), Van den Hof, Paul M.J. (author)
Identifiability of a single module in a network of transfer functions is determined by whether a particular transfer function in the network can be uniquely distinguished within a network model set, on the basis of data. Whereas previous research has focused on the situations that all network signals are either excited or measured, we develop...
journal article 2023
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Pandey, Pankaj (author), Rodriguez-Larios, Julio (author), Miyapuram, Krishna Prasad (author), Lomas, J.D. (author)
Electroencephalography (EEG) enables online monitoring brain activity, which can be used for neurofeedback. One of the growing applications of EEG neurofeedback is to facilitate meditation practice. Specifically, EEG neurofeedback can be used to alert participants whenever they get distracted during meditation practice based on changes in their...
conference paper 2023
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Yu, Hang (author), Wu, Songwei (author), Dauwels, J.H.G. (author)
Estimating a sequence of dynamic undirected graphical models, in which adjacent graphs share similar structures, is of paramount importance in various social, financial, biological, and engineering systems, since the evolution of such networks can be utilized for example to spot trends, detect anomalies, predict vulnerability, and evaluate...
journal article 2022
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Vlaar, M.P. (author), Birpoutsoukis, Georgios (author), Lataire, John (author), Schouten, A.C. (author), Schoukens, Johan (author), van der Helm, F.C.T. (author)
Joint manipulation elicits a response from the sensors in the periphery which, via the spinal cord, arrives in the cortex. The average evoked cortical response recorded using electroencephalography was shown to be highly nonlinear; a linear model can only explain 10% of the variance of the evoked response, and over 80% of the response is...
journal article 2018
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Hofmann, J.A. (author)
The ability to simulate brain neurons in real-time using biophysically-meaningful models is a critical pre-requisite grasping human brain behavior. By simulating neurons' behavior, it is possible, for example, to reduce the need for in-vivo experimentation, to improve artificial intelligence and to replace damaged brain parts in patients. A...
master thesis 2014
Searched for: subject%3A%22Brain%255C+modeling%22
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