Defence of doctoral thesis in the field of Biomedical Engineering, MSc Ivan Zubarev
This thesis summarizes how machine-learning methods can be used to decode non-invasive measures of the human brain activity measured with electro- (EEG) and magnetoencephalography (MEG), with a particular focus on how the patterns that these methods extract from the data can be interpreted in a way that advances our understanding of the functioning of the human brain. The methods developed in this thesis can be applied in brain research, development of brain computer-interfaces, as well as identifying functional biomarkers of various neurological conditions.
Opponent: Professor Surjo Soekadar, Charité –University Medicine Berlin, Germany
Custos: Professor Lauri Parkkonen, Aalto University School of Science, Department of Neuroscience and Biomedical Engineering
Doctoral candidate's contact information: [email protected], +358505121760
The defence will be organized via remote connection (Zoom). Link to the defence
The doctoral thesis will be publicly displayed 10 days before the defence in the publication archive of Aalto University.
Electronic doctoral thesis (aaltodoc.aalto.fi)