Scaling Laws for EEG Decoding: How Much Data Is Enough?

arXiv:2609.35056 · q-bio.NC · Submitted 2026-09-28 · Read on arXiv

q-bio.NC

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/braindecode/OpenEEGBench

Terminology

• Scaling Laws — 14× in this paper · explained in 4 episodes
• Deep Learning — 7× in this paper
• Transformer — 7× in this paper · explained in 2 episodes
• Computer Vision — 6× in this paper
• Foundation Model — 5× in this paper · explained in 3 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Foundation Models — 3× in this paper · explained in 7 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Additive Decomposition — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• Deep Learning Scaling is Predictable, Empirically
• Scaling Laws for Neural Language Models
• Scaling laws for decoding images from brain activity
• EEGDash: An open-source platform for machine learning on public neurophysiological data
• DINOv3

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