What masking geometry works best for EEG foundation models?
cs.LG
Submitted: 2026-09-27
Updated: 2026-09-27
Project page: https://pierregtch.github.io/eeg-fm-masking
Terminology
Sources
- Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language
- data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
- LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
- NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
- Stochastic positional embeddings improve masked image modeling
- VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
- Revisiting Feature Prediction for Learning Visual Representations from Video
- Emerging Properties in Self-Supervised Vision Transformers
- LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis
- Masked Autoencoders As Spatiotemporal Learners
- RankMe: Assessing the downstream performance of pretrained self-supervised representations by their rank
- S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention
- From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Saptiotemporal Dynamics in Brain Signal Analysis
- Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI
- Using Riemannian geometry for SSVEP-based Brain Computer Interface
- NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
- EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces
- Understanding Collapse in Non-Contrastive Siamese Representation Learning
- Decoupled Weight Decay Regularization
- Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning
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