mlsanityData to 7 Oct 2026

Cross-Cohort Spectral-Temporal Dissociation in Frozen EEG Foundation-Model Representations

arXiv 2607.24834 ↗First posted 23 Jul 2026q-bio.NC cs.AI cs.ETcited by 1

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Abstract (arXiv)

Objective. We tested whether frozen representations from five EEG foundation models support decoding of long-range temporal correlations, measured as the detrended-fluctuation-analysis (DFA) exponent of the alpha-band amplitude envelope. Approach. REVE, LaBraM, BENDR, CBraMod, and BIOT were evaluated in CAUEEG and BrainLat. A common 240 s estimator used 8-13 Hz filtering, DFA over 2-23.8 s, artifact masking, and quality control. One fixed nested-cross-validation readout predicted DFA and a fixed-mode aperiodic exponent. Controls tested pre-pool order sensitivity and aperiodic residualization. Results. CAUEEG included 764 recordings and BrainLat 79. BIOT decoded DFA in CAUEEG (R-squared = 0.232; conditional subject-bootstrap 95 percent interval, 0.121-0.310), and CBraMod was positive but imprecise (R-squared = 0.121; 0.003-0.214). Neither replicated in BrainLat, where all five point estimates were negative. In contrast, CBraMod and BIOT decoded the aperiodic exponent in both cohorts (R-squared = 0.459-0.757). BIOT remained positive after removal of the measured linear aperiodic association in matched CAUEEG data (R-squared = 0.240). The post-hoc order control was batch- and configuration-sensitive. Because chronological EEG epochs are not exchangeable, it was descriptive, not an LRTC-specific test. No revised DFA transfer direction passed source-label permutation testing. Cohort membership was near-ceiling decodable from all five embeddings, but this is not a pure site effect. Significance. CBraMod and BIOT show a replicated, model-specific spectral-temporal dissociation: aperiodic decoding is present in both cohorts, whereas alpha-envelope DFA decoding is cohort-dependent. These findings bound the evaluated readouts; they do not establish representational absence or an architectural cause. Transfer and clinical associations remain exploratory.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 23 Jul 2026.

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15 of 26 references.

References · 15

  1. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  2. 2024Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCIcited by 311
  3. 2021BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG datacited by 194
  4. 2024CBraMod: A Criss-Cross Brain Foundation Model for EEG Decodingcited by 188
  5. 2025REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjectscited by 70
  6. 2025EEG-Bench: A Benchmark for EEG Foundation Models in Clinical Applicationscited by 12
  7. 2026What Do EEG Foundation Models Capture from Human Brain Signals?cited by 3
  8. 2026Mechanistic Interpretability of EEG Foundation Models via Sparse Autoencoderscited by 3
  9. 2026Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Modelscited by 3
  10. 2025Structure-preserving contrastive learning for spatial time seriescited by 1
  11. 2026Batch Effects In Brain Foundation Model Embeddingscited by 1
  12. 2026Aperiodic and Low-Frequency Spectral Bias in Reconstruction based EEG Foundation Modelscited by 1
3 more
  1. 2026The Identity Trap in EEG Foundation Models: A Diagnostic Auditcited by 1
  2. 2026A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learningcited by 1
  3. 2026Pretrained, Frozen, Still Leaking: Auditing Cross-Encoder Attribute Transfer in EEG Foundation Modelscited by 1

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