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Evidence

Figures and tables below come from two exploratory manuscripts:
  1. Resource ladder — When Should a Deployed BCI Decoder Spend Adaptation Resources? (Paper 1)
  2. Universal Personalizer — One Personalizer Across Many Trunks (Paper 1b)
All numbers are exploratory, protocol-local, and primarily locked to NimbusBench CSVs. Do not treat them as suite-wide constants.

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Cost bake-off (BNCI primary)

Under severe feature shift with n=40 labeled stream trials, a Bayesian head recovers most of the full fine-tune accuracy gain at a fraction of the adaptation fit wall. BNCI2014-004 cost bake-off: Bayesian head accuracy vs adaptation fit time compared to frozen, linear probe, LoRA, last-layer FT, and full FT Paper 1 — BNCI2014-004, severity 0.75, n=40 (mean over 9 subjects). Recovery = Δacc / Δacc_fullFT. On Zhou2016 (supporting, 4 subjects) the same cell is softer (~46% recovery at ~5.5× lower fit time).

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Head vs fine-tune (same API)

EEGNet Personalizer versus fine-tune arms: accuracy and adaptation fit time on BNCI and Zhou Paper 1b — EEGNet severe n=40; Bayesian head vs full FT. Adaptation fit time under the same Personalizer API across trunks Paper 1b — adaptation fit wall under the shared Personalizer API.

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Transfer across trunks

The same Personalizer ladder is evaluated over an 18-cell trunk×dataset map. Soft clean→L0 transfer is common; the full ordinal principle is rarer — honesty beats overclaim. Transfer map for the Personalizer adaptation ladder across 18 trunk×dataset cells Paper 1b — transfer map (exploratory). Soft clean→L0 holds on 14/18 cells; fuller ordinal structure on 9/18.

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Product claim (honest boundaries)

Not claimed: general cross-session magic, physical electrode robustness, “improves all EEG,” SOTA over EEGNet, suite-wide 30× ratios from other protocols, or shipped “Active Inference for EEG.”

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