Separating personal from population gains when calibrating EEG foundation models for new users
cs.LG, eess.SP, q-bio.NC
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/gaivrt/eeg-personal-population-benefit
Terminology
Sources
- REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects
- LoRA: Low-Rank Adaptation of Large Language Models
- Subject-Adaptive Transfer Learning Using Resting State EEG Signals for Cross-Subject EEG Motor Imagery Classification
- NeuroTTT: Bridging Pretraining-Downstream Task Misalignment in EEG Foundation Models via Test-Time Training
- Evaluating Fast Adaptability of Neural Networks for Brain-Computer Interface
- The Identity Trap in EEG Foundation Models: A Diagnostic Audit
- EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models
- A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding
- Learning aligned EEG representations with subject-specific encoders
- EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIs
- Stacked LoRA for Subject-Adaptive EEG Foundation Models in Motor Imagery Decoding
- Lightweight Adaptation of EEG Foundation Models for Stroke Motor Imagery Decoding: Domain Shift and Subject-Level Robustness
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