Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install hiyenwong-ai-collection-collection-skills-eeg-test-time-adaptation-benchmarkgit clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-eeg-test-time-adaptation-benchmark/SKILL.md--- name: eeg-test-time-adaptation-benchmark description: "NeuroAdapt-Bench: Systematic benchmark for test-time adaptation (TTA) on EEG foundation models under real-world distribution shifts. Evaluates TTA methods across multiple FMs, tasks, and datasets including extreme modality shifts (Ear-EEG). Finds gradient-based TTA degrades, optimization-free methods more stable." --- # EEG Test-Time Adaptation Benchmark (NeuroAdapt-Bench) **Paper:** Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts **arXiv:** 2604.16926 (April 2026) **Authors:** Gabriel Jason Lee, Jathurshan Pradeepkumar, Jimeng Sun **Categories:** cs.LG, cs.AI, eess.SP ## Core Contribution NeuroAdapt-Bench is the first systematic benchmark for evaluating test-time adaptation (TTA) methods on EEG foundation models under realistic distribution shifts. It reveals that standard TTA methods from other domains are unreliable for EEG. ## Problem EEG foundation models face distribution shifts across: - **Clinical settings:** Different hospitals, protocols - **Devices:** Different amplifier hardware, electrode types - **Populations:** Different age groups, conditions - **Modalities:** Scalp EEG vs. Ear-EEG TTA enables adaptation to unlabeled target data during inference without source data access — critical for healthcare privacy. ## Benchmark Design (NeuroAdapt-Bench) ### Distribution Shift Types 1. **In-distribution:** Same domain as training 2. **Out-of-distribution:** Different but related domain 3. **Extreme modality shift:** e.g., Scalp EEG → Ear-EEG ### Evaluation Dimensions - Multiple pretrained foundation models - Diverse downstream tasks - Heterogeneous datasets - Representative TTA approaches from other domains ## Key Findings ### 1. Standard TTA Methods Are Unreliable for EEG - Standard TTA approaches yield **inconsistent gains** - Often **degrade performance** compared to no adaptation - Results don't transfer across tasks or datasets ### 2. Gradient-Based TTA Fails - Gradient-based approaches **particularly prone to heavy degradation** - EEG signal characteristics make gradient estimation unstable - Distribution shifts in EEG are fundamentally different from image domain shifts ### 3. Optimization-Free Methods Are More Stable - Methods that don't require gradient computation show **greater stability** - More **reliable improvements** across settings - Suggests EEG requires fundamentally different adaptation strategies ## Implications ### For Practitioners 1. **Avoid naive TTA:** Don't apply standard TTA methods directly to EEG FMs 2. **Prefer optimization-free:** Use methods that don't rely on gradients 3. **Validate per-task:** TTA effectiveness varies by task — test before deployment 4. **Domain-specific needed:** EEG requires custom adaptation strategies ### For Researchers 1. **EEG ≠ images:** Distribution shifts in EEG have different characteristics 2. **Gradient instability:** EEG signal properties make gradient-based TTA unreliable 3. **Need domain-specific TTA:** Current TTA literature is vision-focused 4. **Ear-EEG challenge:** Extreme modality shifts remain largely unsolved ## TTA Methods Evaluated ### Gradient-Based (Found to Degrade) - Tent (entropy minimization) - EATA (entropy minimization with sample selection) - SAR (sharpness-aware regularization) - MEMO (multi-expansion for test-time adaptation) ### Optimization-Free (Found More Stable) - Feature alignment methods - Statistical normalization approaches - Non-parametric adaptation ## Comparison with TTA in Other Domains | Domain | Gradient-Based TTA | Optimization-Free TTA | |--------|-------------------|----------------------| | Vision (ImageNet-C) | Strong improvements | Moderate improvements | | EEG | **Heavy degradation** | **Stable, modest gains** | ## Application Scenarios - Clinical EEG deployment across hospitals - Cross-device model transfer - Consumer EEG headset adaptation - Ear-EEG and alternative modality deployment - Privacy-preserving model adaptation (no source data needed) ## Trigger Keywords - neuroadapt-bench, eeg test-time adaptation, tta eeg, test-time adaptation foundation model, optimization-free tta, EEG测试时自适应 ## Related Skills - eeg-channel-adaptation-benchmark - eeg-foundation-model-adapters - tta-eeg-foundation-models - laya-eeg-foundation