EEG-Xplain: Decoding Neural Black-Boxes of EEG Foundation Models

arXiv:2609.15687 · cs.AI · Submitted 2026-09-14 · Read on arXiv

cs.AI

Submitted: 2026-09-14

Updated: 2026-09-14

Code: https://github.com/gzhu-hcai/EEG-Xplain

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

Terminology

• Foundation Model — 27× in this paper · explained in 3 episodes
• Foundation Models — 22× in this paper · explained in 7 episodes
• Fidelity — 20× in this paper · explained in 2 episodes
• Interpretability — 16× in this paper · explained in 15 episodes
• Consistency — 15× in this paper · explained in 3 episodes
• Transformer — 14× in this paper · explained in 2 episodes
• alpha — 13× in this paper · explained in 1 episode
• Core — 9× in this paper · explained in 2 episodes
• Healthy Control — 7× in this paper
• Major Depressive Disorder — 7× in this paper
• Adaptation — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Large Language Model — 5× in this paper
• Latent Space — 5× in this paper · explained in 7 episodes
• Explainability — 4× in this paper · explained in 3 episodes
• Integrated Gradients — 4× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Robustness — 3× in this paper · explained in 5 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Convolutional Neural Networks (CNNs) — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Area Over the Perturbation Curve — 1× in this paper
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Brain-Computer Interfaces — 1× in this paper
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Explainable AI (XAI) — 1× in this paper · explained in 7 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Models (FMs) — 1× in this paper · explained in 2 episodes
• Frontal Alpha Asymmetry — 1× in this paper
• Generalization — 1× in this paper · explained in 13 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Layer-wise Relevance Propagation (LRP) — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Semantic Gap — 1× in this paper · explained in 2 episodes
• State Space Models (SSMs) — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification
• On the Opportunities and Risks of Foundation Models
• EEG-GPT: Exploring Capabilities of Large Language Models for EEG Classification and Interpretation

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