Taming foundation model with invariance-oriented pre-training for broad-spectrum EEG analysis across signal-level, brain-state, and brain-health tasks

arXiv:2608.24597 · cs.LG, cs.AI · Submitted 2026-08-25 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/vlawhern/arl-eegmodels

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fine-Tuning — 159× in this paper · explained in 5 episodes
• Linear Probing — 135× in this paper · explained in 2 episodes
• Accuracy — 88× in this paper · explained in 1 episode
• Foundation Model — 87× in this paper · explained in 3 episodes
• Foundation Models — 65× in this paper · explained in 7 episodes
• AUROC — 63× in this paper · explained in 2 episodes
• AUC — 55× in this paper · explained in 2 episodes
• Embeddings — 36× in this paper · explained in 2 episodes
• Transformer — 32× in this paper · explained in 2 episodes
• Benchmark — 29× in this paper · explained in 2 episodes
• Adaptation — 20× in this paper · explained in 1 episode
• Ablation — 19× in this paper · explained in 1 episode
• F1 Score — 12× in this paper · explained in 3 episodes
• Representation Learning — 9× in this paper · explained in 2 episodes
• Adaptability — 4× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Regularization — 4× in this paper · explained in 3 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Fast Fourier Transform — 3× in this paper
• Latent Space — 3× in this paper · explained in 7 episodes
• Transferability — 3× in this paper · explained in 4 episodes
• alpha — 3× in this paper · explained in 1 episode
• Affective Computing — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Exponential Moving Average (EMA) — 2× in this paper · explained in 2 episodes
• Finer-grained Affective Computing EEG Dataset — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fingerprinting — 1× in this paper · explained in 2 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Principal Component Analysis (PCA) — 1× in this paper · explained in 4 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes

Sources

• Transformer-based Spatial-Temporal Feature Learning for EEG Decoding
• EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks