Electroencephalography (EEG) provides a non-invasive window into dynamic brain activity, yet modeling long-horizon EEG sequences remains challenging due to their high temporal complexity, substantial...
arXiv:2607. 21402v1 Announce Type: new Abstract: Self-supervised foundation models have recently shown strong potential for electroencephalogram (EEG)-based analysis.
NeuroDETR is a novel multiscale discrete spatiotemporal tokenization framework designed for asynchronous EEG event detection. It combines structured tokenization, event-aware feature refinement, and a sparse attention module called FOCAL to capture both brief and sustained neural responses while preserving spatial hierarchy across EEG channels. Experiments on eight diverse EEG datasets show that NeuroDETR achieves state‑of‑the‑art performance in jointly classifying events and estimating their onsets and offsets.
arXiv:2608.29304v1 Announce Type: new Abstract: Translating electroencephalography (EEG) into functional magnetic resonance imaging (fMRI) is important for medical neuroimaging, clinical brain-state...
The paper investigates how EEG embeddings from signal processing and foundation models encode trait and state information. Using a hierarchy metric and kNN purity on a BCI dataset, it finds that most embeddings form a macro‑trait hierarchy with micro‑states inside, which hampers state discrimination. Normalizing embeddings reduces trait leakage and improves state classification accuracy by about 10%, and fine‑tuning shifts the hierarchy toward macro‑state organization, enhancing decoding performance.
BrainCodes is a lightweight EEG foundation model that uses a quantization-based neural tokenizer and a spatiotemporal encoder to learn transferable EEG features from over 800 hours of data across more than 1,000 participants. The model is pretrained with a self‑supervised objective that encourages spatial connectivity and sensitivity to narrowband neural oscillations, enabling it to match or surpass state‑of‑the‑art performance on tasks such as sleep staging, attention assessment, and cognitive load estimation. It achieves these results while using up to 97% fewer trainable parameters, making it suitable for low‑resource wearable devices, and its evaluation framework analyzes the geometry and invariances of the learned representations.
arXiv:2607. 24126v1 Announce Type: cross Abstract: Brain-machine interfaces provide a link between neural activity and external devices, enabling restoration of motor function and advancing human-machine interaction using non-invasive electroencephalography (EEG).
arXiv:2606. 00884v1 Announce Type: cross Abstract: We study cross-subject emotion recognition from EEG, a practically important yet challenging problem in brain-computer interfaces.
LEAD is a gated temporal‑spatial Transformer foundation model designed for EEG‑based Alzheimer's disease detection. It was trained on the world’s largest EEG‑AD corpus of 2,238 subjects and uses a subject‑regularized strategy and medical contrastive learning across 13 datasets. LEAD outperforms existing methods on five downstream AD datasets, achieving the best average ranking across 20 evaluations.
arXiv:2607. 09543v1 Announce Type: new Abstract: Self-supervised pretrained foundation models (FM) have shown early promise for non-invasive electroencephalogram (EEG) decoding applications.
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.
arXiv:2509. 17920v2 Announce Type: replace Abstract: Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability.