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.
arXiv:2608. 05315v1 Announce Type: new Abstract: Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs) often require unsupervised domain adaptation (UDA) to generalize across subjects and sessions.
arXiv:2606. 25456v1 Announce Type: new Abstract: Brain-Computer Interface (BCI) based on electroencephalography (EEG) enables direct interaction between the brain and external environments and has significant applications in assistive technologies, medical rehabilitation, and entertainment.
arXiv:2511. 18940v3 Announce Type: replace Abstract: Cross-subject motor imagery decoding remains a fundamental challenge in EEG-based brain-computer interfaces due to substantial inter-subject variability.
arXiv:2606. 29200v1 Announce Type: new Abstract: Multi-site functional MRI (fMRI) studies are essential for robust neuropsychiatric diagnosis yet suffer severe domain shifts from scanner heterogeneity, demographics, and site-specific acquisition protocols.
arXiv:2604. 16926v2 Announce Type: replace-cross Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by distribution shifts across clinical settings, devices, and populations.
arXiv:2606. 06104v1 Announce Type: new Abstract: Electroencephalography (EEG) offers noninvasive, millisecond resolution recordings of neuronal activity and is widely used in neuroscience and healthcare.
SPERA is a new EEG foundation model that predicts in latent space using a joint-embedding predictive architecture. It incorporates a Legendre-polynomial spatial prior into attention to handle varying scalp electrode geometries, and adds factorized temporal and spatial attention with periodic full-attention blocks, plus a relational spectral regularizer to align latent similarity with spectral views. Trained on about 80,000 hours of EEG from 29,048 subjects across 106 datasets, SPERA achieves the highest average balanced accuracy on nine downstream tasks in clinical, cognitive, and BCI domains, while demonstrating strong parameter efficiency and robustness to different recording conditions.
arXiv:2608. 02070v2 Announce Type: replace-cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.
arXiv:2608. 02070v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios.
The paper introduces SPD-MetaFormer, an attention‑free architecture for decoding brain signals from small datasets. It shows that existing attention‑based models on the SPD manifold (MAtt and GBWAtt) learn nearly uniform weights, and that replacing learned weights with uniform ones does not degrade performance. SPD-MetaFormer uses uniformly weighted Fréchet aggregation with a geodesic residual and a shared spectral feedforward map, achieving competitive results on three EEG benchmarks while maintaining SPD‑valued token states until classification.
arXiv:2601. 07556v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints.
Brain4FMs is a unified benchmark for evaluating brain foundation models (BFMs) on both scalp EEG and intracranial EEG (iEEG). It incorporates 17 representative models and 21 public datasets spanning clinical diagnosis, sleep staging, communication, and affective computing, and offers dataset-aware preprocessing, cross‑subject evaluation, and standardized downstream workflows. The benchmark highlights that no single BFM consistently outperforms others across all tasks, modalities, and adaptation protocols, prompting further exploratory analyses of model‑specific spatial, spectral, and discrete representations.