Thesis Details
Analýza a klasifikace dat ze snímače mozkové aktivity
This thesis aims to implement methods for recording EEG data obtained with the neural activity sensor OpenBCI Ultracortex IV headset. It also describes neurofeedback, methods of obtaining data from the motor cortex for further analysis and takes a look at the machine learning algorithms best suited for the presented problem. Multiple training and testing datasets are created, as well as a tool for recording the brain activity of a headset-wearing test subject, which is being visually presented with cognitive challenges on the screen in front of him. A neurofeedback demo app has been developed, presented and later used for calibration of new test subjects. Next part is data analysis, which aims to discriminate the left and right hand movement intention signatures in the brain motor cortex. Multiple classification methods are used and their utility reviewed.
OpenBCI, Ultracortex, BCI, mind-controlled game, EEG, biofeedback, brain, focus, atten-tion, prefrontal cortex, motor cortex, meditation, python, unity, lstreamer, gui, openvibe,10-20 system, brain waves, DWT, wavelet, gradient boosting, random forests, support vec-tor machines
Drábek Vladimír, doc. Ing., CSc. (DCSY FIT BUT), člen
Hanáček Petr, doc. Dr. Ing. (DITS FIT BUT), člen
Lengál Ondřej, Ing., Ph.D. (DITS FIT BUT), člen
Pavlík Jan, Mgr., Ph.D. (DADM FME BUT), člen
Veselý Vladimír, Ing., Ph.D. (DIFS FIT BUT), člen
@mastersthesis{FITMT21897, author = "Jan Jile\v{c}ek", type = "Master's thesis", title = "Anal\'{y}za a klasifikace dat ze sn\'{i}ma\v{c}e mozkov\'{e} aktivity", school = "Brno University of Technology, Faculty of Information Technology", year = 2019, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/21897/" }