Electroencephalography (EEG) has a long history as a clinical tool to study brain function,
and its potential to derive biomarkers for various applications is far from exhausted. Machine�…

Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet,
when building predictive models from brain data, it is often unclear how electrophysiology�…

Electroencephalographic (EEG) recordings are thought to reflect the network-wide
operations of canonical neural computations, making them a uniquely insightful measure of�…

Relationships between neuroimaging measures and behavior provide important clues about
brain function and cognition in healthy and clinical populations. While�…

Electroencephalography (EEG) and magnetoencephalography (MEG) are the most common
noninvasive brain-imaging techniques for monitoring electrical brain activity and inferring�…

Biomarker discovery in neurological and psychiatric disorders critically depends on
reproducible and transparent methods applied to large-scale datasets�…

Population-level modeling can define quantitative measures of individual aging by applying
machine learning to large volumes of brain images. These measures of brain age, obtained�…

Despite the inherent complexity of electroencephalogram (EEG) data characterized by its
high dimensionality, artifactual noise, and biological variability, many machine learning (ML)�…

Spectral analysis using wavelets has proven useful for analyzing electroencephalographic
(EEG) signals and identifying biomarkers in a clinical context. Over the past decade�…

Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like
epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of�…