Machine Learning for EEG-based biomarkers

Last updated on Sep 1, 2021

Ever since I began to work on electrophysiology and cognitive research, it has been my foremost conviction that tools like machine learning and deep learning combined with EEG data would enable a new paradigm for biomarker discovery in psychiatry and neurology.

During my PhD, I worked on different deep learning architectures applied to EEG to diagnose Attention Deficit Hyperactivity Disorder (ADHD) patients from healthy controls and to predict which patients with REM Behavior Disorder (RBD) would eventually develop Parkinson’s disease up to 6 years before they develop any symptoms.

Please consider the related publications listed below or more details.

EEG Deep Learning ADHD Parkinson's
Laura Dubreuil Vall
Laura Dubreuil Vall
Engineer and Neuroscientist

My research interests include neurotechnology, electroencephalography, brain stimulation, machine learning, product development and healthcare strategy.

Related

Publications

Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG
Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder that affects 5% of the pediatric and …
Laura Dubreuil Vall, Giulio Ruffini, Joan A Camprodon
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Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG
Deep Learning With EEG Spectrograms in Rapid Eye Movement Behavior Disorder
REM Behavior Disorder (RBD) is now recognized as the prodromal stage of α synucleinopathies such as Parkinson’s disease (PD). In …
Giulio Ruffini, David Ibáñez, Marta Castellano, Laura Dubreuil Vall, Aureli Soria-Frisch, Ron Postuma, Jean-François Gagnon, Jacques Montplaisir
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Deep Learning With EEG Spectrograms in Rapid Eye Movement Behavior Disorder