Deep Neural Network to Map Human Brain Activity from EEG to fMRI
Organisatsiooni nimi
Computational Neuroscience Lab
Kokkuvõte
There are different techniques to measure human brain activity. One of them, EEG, can show precisely *when* the activity occurred in time, but provides almost no information about *where* inside the brain the activity happened. Another technology, fMRI, is exactly the opposite: it shows precise 3D location of the source of activity, but cannot tell when exactly that activity took place.
We have a dataset where both EEG and fMRI data were recorded simultaneously. That allows to try to train a network (or another ML model) to predict location of fMRI activity from EEG recording.
The outcome of this work will be a model, that gets piece of EEG data as input and tells which part of brain this activity is most likely to be originating from. This would be important contribution to computational neuroscience, field of source localization and, if done well, might yield publishable result.
We have a dataset where both EEG and fMRI data were recorded simultaneously. That allows to try to train a network (or another ML model) to predict location of fMRI activity from EEG recording.
The outcome of this work will be a model, that gets piece of EEG data as input and tells which part of brain this activity is most likely to be originating from. This would be important contribution to computational neuroscience, field of source localization and, if done well, might yield publishable result.
Lõputöö kaitsmise aasta
2017-2018
Juhendaja
Ilya Kuzovkin
Suhtlemiskeel(ed)
eesti keel, inglise keel
Nõuded kandideerijale
Understanding of machine learning concepts, familiarity with neural networks, good Python/numpy programming skills, scientific attitude, ability to work independently (things will fail, you need to be able to think what to try next).
Tase
Magister
Märksõnad
Kandideerimise kontakt
Nimi
Ilya Kuzovkin
Tel
E-mail