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Data processing in real-time fMRI neurofeedback
 Bečička, Martin ; Slavíček, Tomáš (oponent) ; Lamoš, Martin (vedoucí práce)
The presented thesis deals with real-time digital filtering of fMRI neurofeedback data. It analyzes currently used solution at CEITEC MU chiefly in respect to finding ways to shorten the delay at the beginning of each neurofeedback block which is introduced by digital filtering. Current solution uses extended Kalman filter mainly for its real-time and smoothing properties. Analysis of 150 individual neurofeedback blocks yielded true learning period of Kalman filter which has been found to be significantly shorter than is set in the current solution. Different options to further reduce the transient period have been explored and short moving average filter has been chosen as an optimal trade-off between transient period, filter delay and its smoothing properties.
Úplný záznam
Data processing in real-time fMRI neurofeedback
 Bečička, Martin ; Slavíček, Tomáš (oponent) ; Lamoš, Martin (vedoucí práce)
The presented thesis deals with real-time digital filtering of fMRI neurofeedback data. It analyzes currently used solution at CEITEC MU chiefly in respect to finding ways to shorten the delay at the beginning of each neurofeedback block which is introduced by digital filtering. Current solution uses extended Kalman filter mainly for its real-time and smoothing properties. Analysis of 150 individual neurofeedback blocks yielded true learning period of Kalman filter which has been found to be significantly shorter than is set in the current solution. Different options to further reduce the transient period have been explored and short moving average filter has been chosen as an optimal trade-off between transient period, filter delay and its smoothing properties.
Úplný záznam

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