neural-encoding-evaluation-ground-truth

name

neural-encoding-evaluation-ground-truth

description

Systematic audit methodology for EEG foundation model interpretability. Decomposes what models learn, what they use, and how much can be explained using layer-wise ridge probing, LEACE cross-covariance erasure, and transparent classifiers. Use when: EEG foundation model analysis, neural encoding evaluation, interpretability audit, feature causality analysis, brain signal representation analysis, EEG feature lexicon, LEACE analysis.

Neural Encoding Evaluation: Ground-Truth Approximation

Systematic methodology for auditing what EEG foundation models capture from human brain signals.

Paper

• Title: What Do EEG Foundation Models Capture from Human Brain Signals?
• arXiv: 2605.11410
• Authors: Ling Tang, Qian Chen, Jilin Mei, Houshi Xu, Quanshi Zhang et al.
• Date: 2026-05-12
• Categories: cs.AI

Overview

Clinical EEG analysis uses hand-crafted features (band power, connectivity, complexity). Modern EEG foundation models learn directly from raw signals via self-supervised pretraining, matching or outperforming feature-engineered baselines. This paper systematically audits what these models learn, what they use, and how much can be explained.

Key Research Questions

The audit decomposes into three sub-questions:

Question	Method
What does the model learn?	Layer-wise ridge probing
What does the model use?	LEACE-style cross-covariance subspace erasure
How much can be explained?	Transparent classifier vs. random-feature baseline

Methodology

1. Layer-Wise Ridge Probing

• Train linear probes on each layer's representations
• Measure how well each known EEG feature can be decoded
• Identifies which features are encoded at which depth

2. LEACE-Style Cross-Covariance Erasure

• Linear Erasure of All Covariance Evidences
• Removes feature-related information from representations
• Tests whether the feature is causal to model predictions

3. Feature Classification

Features are classified as:

• Representation-Causal (RC): Feature is both encoded AND used for predictions
• Encoded-Only: Feature is encoded but not causally used

Audit Scope

Dimension	Details
Models	CSBrain, CBraMod, LaBraM (3 foundation models)
Tasks	MDD, Stress, ISRUC-Sleep, TUSL, Siena (5 clinical tasks)
Feature Lexicon	6 families, 63 features

Total units audited: 945 (model × task × feature)

Key Results

Causality Breakdown

• 648/945 (68.6%): Representation-causal features
• 199/945 (21.1%): Encoded-only features

Universal Features

• 50 features qualify as universal candidates (strong RC support across all 3 architectures in 2+ tasks)
• Frequency-domain features dominate
• Other 5 feature families each contribute substantial causal mass

Recovery Analysis

• Confirmed features recover 79.3% of foundation model's advantage over random baseline
• Task gradient of recovery:
  • MDD ≈ 99% (near ceiling — almost fully explained by lexicon)
  • Stress ≈ 56% (harder task — significant residual for future concept discovery)

Implementation Pattern

[EEG Foundation Model] → [Layer Representations]
                              ↓
              ┌───────────────┼───────────────┐
              ↓               ↓               ↓
    [Ridge Probing]    [LEACE Erasure]   [Classification]
              ↓               ↓               ↓
    [Encoded Features] [Causal Features] [Recovery Score]
              ↓               ↓               ↓
              └───────────────┼───────────────┘
                              ↓
                    [Unified Audit Report]

Applications

• Interpretable EEG foundation model development
• Clinical validation of brain signal AI models
• Feature engineering for brain-computer interfaces
• Understanding representational alignment in neural AI
• Identifying gaps in current EEG feature lexicons

Key Insights

1. Majority Causal: Most learned features are causally used (68.6% RC)
2. Task Difficulty Gradient: Easy tasks are well-explained; hard tasks reveal unknown representations
3. Frequency Dominance: Frequency features dominate but other families matter significantly
4. Residual Discovery: The unexplained portion of hard tasks (e.g., Stress at 44%) is a concrete target for future concept discovery
5. Architecture Convergence: 50 universal features show convergence across different architectures

Related Concepts

• EEG foundation models (CSBrain, CBraMod, LaBraM)
• LEACE (Linear Erasure of All Covariance Evidences)
• Ridge regression probing
• Neural encoding models
• Clinical EEG features (band power, connectivity, complexity)
• Representational alignment

Pitfalls

• Hand-crafted features may not capture all model-learned representations
• LEACE only removes linear relationships — nonlinear encodings may persist
• Recovery analysis depends on classifier choice and baseline
• Task-dependent: what's explained varies significantly by clinical domain

References

• arXiv: 2605.11410
• Related: neural encoding evaluation, EEG interpretability, foundation model audit

Notes for Agents

When using this methodology:

1. Always run all three methods (probing, erasure, classification) together
2. The task gradient is important — don't generalize from one task to all
3. The 50 universal features are a validated starting point for new EEG analysis
4. The residual (unexplained portion) of hard tasks is where new discoveries live
5. Compare against random-feature baseline to avoid overclaiming