MENDR: Manifold-Embedded Neural Data Representations for Channel-Agnostic EEG Foundation Modeling

Abstract

Foundation models for electroencephalography (EEG) have shown promise in learning transferable representations, but existing approaches treat EEG as a generic time series, ignoring the Riemannian geometry of spatial covariance structure that is fundamental to neural signal analysis. We propose MENDR (Manifold-Embedded Neural Data Representations), the first EEG foundation model that builds this geometry directly into its architecture (via the matrix logarithm and Log-Euclidean tangent space) rather than learning it from data. MENDR embeds windowed covariance matrices into the Log-Euclidean tangent space via differentiable matrix logarithm, where standard transformer operations become geometrically equivalent to Riemannian operations. A channel-agnostic spatial projection based on Perceiver-style cross-attention enables seamless transfer across electrode configurations ranging from 6 to 64 channels. Pretrained on the Temple University Hospital EEG Corpus (4,000+ hours, 14,987 subjects) using a masked autoencoder objective in the tangent space, MENDR achieves state-of-the-art results on 3 of 6 downstream benchmarks (TUAB abnormality, TUEV event, CHB-MIT seizure) with only 1.2M parameters, up to $42\times$ fewer than competing foundation models.