arXiv:2610.07824v1 Announce Type: new Abstract: Electrophysiological source imaging (ESI) aims to estimate cortical source activity from noninvasive electrophysiological measurements such as electroe...
arXiv:2607. 22733v1 Announce Type: cross Abstract: We investigate whether a generative model can supply useful synthetic motor-imagery (MI) electroencephalography (EEG) trials that improve the accuracy of independent downstream classifiers.
arXiv:2610.08355v1 Announce Type: cross Abstract: Flow-matching models start from an isotropic Gaussian source, the standard choice when the correlation structure of the data is unknown in advance. F...
arXiv:2611.06979v1 Announce Type: new Abstract: M/EEG source imaging requires accurate localization and reliable uncertainty estimates, yet posterior credible regions are rarely assessed for empirica...
arXiv:2611.06700v1 Announce Type: new Abstract: EEG extended-source imaging aims to localize active cortical regions and characterize their spatial extent, yet supervised inverse networks typically d...
arXiv:2609.13507v1 Announce Type: new Abstract: Brain-computer interfaces (BCIs) decode neural activity to restore lost function. Typically, training a high-performance neural decoder requires a larg...
SPDAlign is an interpretable framework that aligns domain-specific EEG data by applying linear transformations on the Symmetric Positive Definite manifold, correcting mean differences and global rotations across domains using Wasserstein Procrustes. It leverages a generative model of EEG to recover distribution shifts caused by factors like head geometry, and demonstrates competitive performance on extensive public EEG datasets. The method is globally linear, enabling identification of frequency ranges, spatial patterns, and cross-subject variability without requiring labeled calibration data.
BrainPro is a process‑conditioned self‑supervised pretraining framework for large EEG models that addresses two underexplored challenges: the underuse of mental‑process cues in masked pretraining and the variability of electrode montages across datasets. It maps dataset‑specific montages to a universal channel‑region template, retrieves spatial filters to create a topographically aligned basis, and learns a shared encoder along with process‑specific branches conditioned on protocol cues. Experiments on nine public BCI benchmarks show that BrainPro outperforms baselines, with ablations indicating that topographic retrieval and process‑conditioned representation learning jointly enhance EEG decoding.
arXiv:2606. 11415v1 Announce Type: cross Abstract: Neural recordings are often interpreted as local measurements, yet the signal at any one sensor can also reflect structured activity distributed across the broader network.
Neural State Prediction (NSP) is a latent‑predictive framework designed to curb shortcut learning in EEG foundation models. By using a target encoder updated with an exponential moving average, identity residualization, and topology‑separated context, NSP constrains both the prediction target and the available context. Trained on 2.2 million EEG segments, NSP outperforms baselines on 14 datasets in the EEG‑FM‑Bench, achieving 63.94 % macro balanced accuracy.
The paper introduces a spectral framework that decomposes the posterior covariance in sparse Bayesian learning for M/EEG source imaging into data‑informed and indeterminacy epistemic components, using the singular value structure of the prior‑preconditioned forward operator. It also presents an equivalent subtractive decomposition that expresses the prior covariance as the sum of posterior and data‑driven reduction terms, allowing precise accounting of uncertainty reduction through Bayesian learning. These complementary representations provide both theoretical insight and practical tools for interpretable uncertainty quantification in linear‑Gaussian Bayesian inverse solvers where the posterior covariance is available in closed form.
arXiv:2608.24597v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a widely used window into human brain function, but most EEG models remain tied to a one-dataset-one-model supervised...