Confidence-Gated Adaptation for Cross-Session BCI: A Decision-Oriented Framework

Abstract

The practical deployment of Brain-Computer Interfaces (BCI) is severely hampered by the inherent non-stationarity of neural signals. Sensorimotor rhythms drift across sessions due to fatigue, learning, or hardware shifts, creating a generalization gap where models trained on historical data fail on new sessions.

This research challenges the traditional “one-size-fits-all” adaptation approach. Instead, we propose a decision-oriented framework, positing that the optimal adaptation strategy must be contingent upon the statistically measured divergence between historical source data and the current target session.

Core Challenge: Uncertainty in Divergence Estimation

A critical insight of this work is that standard estimates of inter-session divergence (e.g., Maximum Mean Discrepancy or Riemannian distance) are themselves subject to high sampling variance due to limited calibration trials. Relying on noisy point estimates can lead to negative transfer.

Proposed Solution: Confidence-Interval (CI) Gating

To address this, we introduce “Confidence-Interval Gating.” This mechanism employs nonparametric bootstrapping to establish confidence intervals around divergence estimates. Adaptation decisions are then based on the statistical overlap of these intervals rather than arbitrary thresholds.

Methodological Framework

The framework dynamically selects among three canonical pipelines based on the detected drift scenario:

1. Merge & Adapt Pipeline (MAP):

   • Scenario: Minimal, random drift.

   • Strategy: Utilizes global data pooling to maximize statistical power.

2. Minimum-Distance MAP (MMP):

   • Scenario: Heterogeneous or multi-modal history.

   • Strategy: Uses CI-Gating to selectively include only historical sessions that are statistically indistinguishable from the target, effectively pruning irrelevant source data to prevent negative transfer.

3. Bridge-Domain Pipeline (BDP):

   • Scenario: Significant distributional shifts.

   • Strategy: Identifies a “bridge” set of sessions geometrically intermediate between source and target. It uniquely decouples hyperparameter optimization from final alignment by using a proxy transfer task (Far-to-Bridge), ensuring robust parameter selection before the final adaptation.

Impact

By linking measurable neural divergence—augmented with rigorous uncertainty quantification—to specific adaptation pipelines, this work offers a principled methodology for personalized BCI deployment, moving beyond ad-hoc model selection towards statistically grounded decision rules.

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