Machine learning of brain-specific biomarkers from EEG
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�…
and its potential to derive biomarkers for various applications is far from exhausted. Machine�…
[HTML][HTML] Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers
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�…
when building predictive models from brain data, it is often unclear how electrophysiology�…
[HTML][HTML] The patient repository for EEG data+ computational tools (PRED+ CT)
JF Cavanagh, A Napolitano, C Wu…�- Frontiers in�…, 2017 - frontiersin.org
Electroencephalographic (EEG) recordings are thought to reflect the network-wide
operations of canonical neural computations, making them a uniquely insightful measure of�…
operations of canonical neural computations, making them a uniquely insightful measure of�…
[HTML][HTML] Moving beyond ERP components: a selective review of approaches to integrate EEG and behavior
Relationships between neuroimaging measures and behavior provide important clues about
brain function and cognition in healthy and clinical populations. While�…
brain function and cognition in healthy and clinical populations. While�…
Bayesian Machine Learning: EEG\/MEG signal processing measurements
Electroencephalography (EEG) and magnetoencephalography (MEG) are the most common
noninvasive brain-imaging techniques for monitoring electrical brain activity and inferring�…
noninvasive brain-imaging techniques for monitoring electrical brain activity and inferring�…
[HTML][HTML] DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience
Biomarker discovery in neurological and psychiatric disorders critically depends on
reproducible and transparent methods applied to large-scale datasets�…
reproducible and transparent methods applied to large-scale datasets�…
[HTML][HTML] A reusable benchmark of brain-age prediction from M/EEG resting-state signals
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�…
machine learning to large volumes of brain images. These measures of brain age, obtained�…
Achieving Reproducibility in EEG-Based Machine Learning
Despite the inherent complexity of electroencephalogram (EEG) data characterized by its
high dimensionality, artifactual noise, and biological variability, many machine learning (ML)�…
high dimensionality, artifactual noise, and biological variability, many machine learning (ML)�…
GREEN: a lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration
Spectral analysis using wavelets has proven useful for analyzing electroencephalographic
(EEG) signals and identifying biomarkers in a clinical context. Over the past decade�…
(EEG) signals and identifying biomarkers in a clinical context. Over the past decade�…
[HTML][HTML] The NMT scalp EEG dataset: an open-source annotated dataset of healthy and pathological EEG recordings for predictive modeling
Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like
epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of�…
epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of�…