Machine learning techniques to improve the value of neurophysiological measurements for individual patients

Scriptie/masterproef: Doctoral Thesis


The main topic in this PhD thesis was applying machine learning techniques
in neurological disorders, in order to individually distinguish patients from
healthy controls, patients with different diseases or patients with different
disease severity. This thesis is intended to recapitulate a PhD in which a
broad range of subjects was covered. To start with, three different diseases
were investigated: schizophrenia, dementia and multiple sclerosis. Two
different measurement techniques were used in these studies:
electroencephalography and magnetoencephalography. Finally, different
analysis methods were applied, such as peak extraction, frequency spectrum
analysis, network analysis, group difference analysis and classification.
Datum Prijs18 sep 2017
Toekennende instantie
  • Vrije Universiteit Brussel
BegeleiderGuy Nagels (Promotor), Jeroen Van Schependom (Promotor), Johan De Mey (Jury), Jan Versijpt (Jury), Hichem Sahli (Jury), Xavier De Tiège (Jury) & Natasha Maurits (Jury)

Citeer dit