Article

Between Patterns and Predictions: Interpretable Latent EEG Representations for Clinical Insights

2026-06-22

Abstract excerpt

Electroencephalography (EEG) captures rich brain dynamics, yet in clinical practice this complexity is often reduced to simplified summaries or categorical labels, limiting its interpretability for decision-making. We tested the hypothesis that a pretrained latent embedding framework, the Universal Map of EEG (UM-EEG), can preserve clinically meaningful structure across heterogeneous datasets and provide a general...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 428038f9-0d22-504e-9361-da26cc247341
DOI: 10.64898/2026.06.12.26355536

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Between Patterns and Predictions: Interpretable Latent EEG Representations for Clinical InsightsDOI 10.64898/2026.06.12.26355536Read

Towards a Universal Map of EEG: A semantic, low-dimensional manifold for EEG Classification, Clustering and PrognosticationSemantic proximity Read

Reliability and disease sensitivity are dissociable properties of EEG foundation-model representationsSemantic proximity Read

Multi-modal, multi-species, and multi-task latent-space model for decoding level of consciousnessSemantic proximity Read

Toward Unified Biomarkers for Focal EpilepsySemantic proximity Read

A Foundation Model for Sleep-Based Risk Stratification and Clinical OutcomesSemantic proximity Read

Self-Supervised Data-Driven Approach Defines Pathological High-Frequency Oscillations in HumanSemantic proximity Read

Unsupervised Representation Learning Reveals Individualized Neurophysiological ProfilesSemantic proximity Read

Machine Learning Interpretability Methods to Characterize Brain Network Dynamics in EpilepsySemantic proximity Read

A Multimodal Sleep Foundation Model for Disease PredictionSemantic proximity Read

Sleep Staging Foundation Models Encode Neural Disorder-Related EEG Representations that Generalize to WakefulnessSemantic proximity Read

Temporally Continuous Automated Sleep-Wake Classification Using Deep LearningSemantic proximity Read

The Hidden Repertoire of Brain Dynamics and DysfunctionSemantic proximity Read