eeg-fm-stress-testing-clinical-decoding

name	eeg-fm-stress-testing-clinical-decoding
title	EEG Foundation Model Stress Testing for Clinical Decoding
description	Comprehensive benchmarking framework for stress-testing EEG foundation models with dataset identity analysis and targeted negative controls to evaluate clinical decoding robustness.
trigger_words	["eeg foundation models","stress testing","clinical decoding","dataset identity","negative controls","frozen linear probes"]

EEG Foundation Model Stress Testing for Clinical Decoding

Overview

This methodology provides a comprehensive stress-testing framework for evaluating pretrained EEG foundation models in clinical decoding tasks. The approach benchmarks multiple models across diverse clinical tasks and datasets using rigorous evaluation protocols including dataset identity analysis and targeted negative controls to reveal hidden biases and limitations.

Core Contributions

• Multi-model Benchmark: Evaluation of six EEG foundation models (LaBraM, EEGMamba, CBraMod, REVE, BENDR, BIOT)
• Clinical Task Diversity: Five clinical tasks across four datasets including dementia, Alzheimer's, and ictal detection
• Evaluation Unit Analysis: Comparison of leave-one-subject-out, subject-grouped, and recording-level splits
• Targeted Negative Controls: Random initialization, random features, label permutation, scrambled-label fine-tuning, projection sensitivity
• Dataset Identity Detection: Quantification of dataset-specific biases in frozen embeddings

Key Findings

1. Performance Variability: Frozen REVE achieves 0.568 AUROC on Korean dementia vs 0.769 for classical features
2. Dataset Identity Bias: Frozen embeddings decode dataset identity with AUROC 1.000 at PCA-50 (0.9998 after preprocessing)
3. Random Initialization Superiority: Randomly initialized encoder outperforms pretrained REVE on Korean diagnosis (0.659 vs 0.570)
4. Classical Features Advantage: Classical features nominally exceed REVE on Alzheimer's disease at subject level
5. Controlled Positive Result: Cross-subject ictal detection shows clear benefit (REVE: 0.793 AUROC, +9.2% over random)

Implementation Guidelines

Benchmark Setup

# Stress-testing framework for EEG foundation models
class EEGStressTest:
    def __init__(self, models, datasets, clinical_tasks):
        self.models = models  # List of foundation models
        self.datasets = datasets  # Multiple clinical datasets
        self.tasks = clinical_tasks  # Clinical decoding tasks
        
    def run_comprehensive_benchmark(self):
        results = {}
        for model_name in self.models:
            model = load_pretrained_model(model_name)
            for dataset_name, dataset in self.datasets.items():
                for task in self.tasks[dataset_name]:
                    # Test different evaluation units
                    results[f"{model_name}_{dataset_name}_{task}"] = {
                        'subject_level': self.evaluate_subject_level(model, dataset, task),
                        'recording_level': self.evaluate_recording_level(model, dataset, task),
                        'negative_controls': self.run_negative_controls(model, dataset, task)
                    }
        return results

Evaluation Protocols

1. Frozen Linear Probes: Use pretrained encoders with frozen weights
2. Multiple Split Strategies:
   • Leave-one-subject-out (LOSO)
   • Subject-grouped splits
   • Explicitly identified recording-level splits
3. Negative Control Suite:
   • Random initialization baseline
   • Random features comparison
   • Label permutation test
   • Scrambled-label fine-tuning
   • Projection sensitivity analysis

Dataset Identity Analysis

# Detect dataset identity bias in embeddings
def analyze_dataset_identity(embeddings, dataset_labels, preprocessing=True):
    if preprocessing:
        # Apply band restriction and per-epoch z-scoring
        embeddings = apply_band_restriction(embeddings)
        embeddings = per_epoch_z_score(embeddings)
    
    # PCA dimensionality reduction
    pca_50 = PCA(n_components=50).fit_transform(embeddings)
    
    # Train classifier to detect dataset identity
    clf = LogisticRegression()
    scores = cross_val_score(clf, pca_50, dataset_labels, cv=5, scoring='roc_auc')
    return np.mean(scores)

Use Cases

• Model Validation: Rigorous evaluation of EEG foundation models before clinical deployment
• Bias Detection: Identifying dataset-specific biases that could lead to false conclusions
• Comparative Analysis: Fair comparison between foundation models and classical features
• Clinical Translation: Understanding real-world performance limitations for medical applications
• Research Reproducibility: Standardized stress-testing protocol for future studies

Pitfalls & Considerations

• Evaluation Unit Dependency: Results vary significantly based on split strategy (subject vs recording level)
• Dataset Shift Sensitivity: Performance degrades substantially under distribution shift
• Comparator Strength: Choice of baseline significantly affects conclusions
• Hidden Biases: Dataset identity can be decoded even from heavily preprocessed embeddings
• Task Specificity: Benefits may only appear in specific clinical tasks (e.g., ictal detection)

Best Practices

1. Always Include Negative Controls: Test against random initialization and random features
2. Use Multiple Evaluation Units: Report results for both subject-level and recording-level splits
3. Analyze Dataset Identity: Quantify dataset-specific biases in embeddings
4. Compare to Classical Features: Benchmark against established classical feature pipelines
5. Report Full Results: Include both positive and negative findings transparently

Activation Keywords

eeg foundation models, stress testing, clinical decoding, dataset identity, negative controls, frozen linear probes, leave-one-subject-out, recording-level splits, random initialization, classical features, ictal detection, dementia classification, alzheimer's disease

References

• Zare, M. (2026). Stress-Testing EEG Foundation Models for Clinical Decoding: Dataset Identity and Targeted Negative Controls. arXiv:2607.24519 [cs.LG]