Limits of Trial-Adaptive Neural Language Fusion Across Large Language Models in P300 Brain Computer Interfaces
- 1BIDMC
Abstract
ObjectivePublished P300-speller fusion schemes fix prior trust regardless of trial reliability; we tested whether a reliability estimate improves on it.
MethodsWe reanalyzed 3,373 archived P300-speller selections from 47 people with ALS (BigP3BCI). A fair, matched-search-space comparison, tuning both a fixed weight and an adaptive policy out-of-fold, was evaluated across 22 evaluable language-model priors up to 46.7B parameters. Two representative priors, GPT-2 and a classical 5-gram, additionally received detailed naive and mechanistic analyses.
ResultsNo priors 95% CI favored adaptive fusion under the fair comparison, despite unexploited oracle headroom at every scale. Under GPT-2, the naive comparison was significantly worse for adaptive fusion; both anchors converged to a degenerate or near-degenerate fair-comparison solution. For the representative anchors, three further controllers failed to convert that headroom into benefit; the fixed-fused posteriors output probability outperformed the best controller for flagging errors (2.8- to 3.8-fold enrichment).
ConclusionA tuned fixed weight is a difficult-to-beat default across the tested scale range; reliability estimation gave no deployable adaptive advantage.
SignificanceAdaptive weighting should be validated against a fairly tuned baseline across model families and scales; in this dataset, the fused outputs confidence identified high-risk selections better than the tested purpose-built ranker.
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BibTeX
@misc{gorenshtein2026limits,
title = {{Limits of Trial-Adaptive Neural Language Fusion Across Large Language Models in P300 Brain Computer Interfaces}},
author = {A. Gorenshtein and M. Omar and E. L. Jia and Y. Adiniaev and O. Daniel and J. Kruskal and M. Ahmed and O. R. Brook and E. Klang and Y. Barash},
howpublished = {medRxiv},
year = {2026},
month = aug,
doi = {10.64898/2026.08.30.26361777},
url = {https://www.medrxiv.org/content/10.64898/2026.08.30.26361777v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Limits of Trial-Adaptive Neural Language Fusion Across Large Language Models in P300 Brain Computer Interfaces
AU - A. Gorenshtein
AU - M. Omar
AU - E. L. Jia
AU - Y. Adiniaev
AU - O. Daniel
AU - J. Kruskal
AU - M. Ahmed
AU - O. R. Brook
AU - E. Klang
AU - Y. Barash
PY - 2026
DA - 2026/08/30
PB - medRxiv
DO - 10.64898/2026.08.30.26361777
UR - https://www.medrxiv.org/content/10.64898/2026.08.30.26361777v1
AB - ObjectivePublished P300-speller fusion schemes fix prior trust regardless of trial reliability; we tested whether a reliability estimate improves on it. MethodsWe reanalyzed 3,373 archived P300-speller selections from 47 people with ALS (BigP3BCI). A fair, matched-search-space comparison, tuning both a fixed weight and an adaptive policy out-of-fold, was evaluated across 22 evaluable language-model priors up to 46.7B parameters. Two representative priors, GPT-2 and a classical 5-gram, additionally received detailed naive and mechanistic analyses. ResultsNo priors 95% CI favored adaptive fusion under the fair comparison, despite unexploited oracle headroom at every scale. Under GPT-2, the naive comparison was significantly worse for adaptive fusion; both anchors converged to a degenerate or near-degenerate fair-comparison solution. For the representative anchors, three further controllers failed to convert that headroom into benefit; the fixed-fused posteriors output probability outperformed the best controller for flagging errors (2.8- to 3.8-fold enrichment). ConclusionA tuned fixed weight is a difficult-to-beat default across the tested scale range; reliability estimation gave no deployable adaptive advantage. SignificanceAdaptive weighting should be validated against a fairly tuned baseline across model families and scales; in this dataset, the fused outputs confidence identified high-risk selections better than the tested purpose-built ranker.
ER -