Let Me Change My Mind:Decision-Relevant Adaptation for Brain–AI Interfaces

Adaptive brain–computer interfaces can mistake a change in what a user wants for a change in how that intention is recorded. Restoring decoder performance can therefore conflict with preserving intent. We formulate adaptation as decision-relevant identification under latent change. Within a finite, piecewise-stationary model, we characterize when reliable action selection is possible without separately identifying goals and recording states. We derive a lower bound on additional diagnostic effort conditional on reusable evidence, and a sequence-aware probing and selective-reset policy that attains its leading order within stable segments under explicit separation and stability assumptions. The policy resolves hypotheses requiring different actions while retaining evidence about unchanged mechanisms, rather than relearning the entire interface.We evaluate 96 healthy adults across two sites, two EEG tasks, and six sessions over eight weeks, independently manipulating private-goal changes, recording shifts, and AI-prior alignment. At 90% execution coverage, conventional online adaptation reduces recording-shift errors from 23.8% to 11.9% but increases goal-shift errors from 10.4% to 22.6% relative to a frozen decoder; this reversal persists within both prior-alignment strata. Against a validation-selected, cost-aware decision-class testing baseline with change detection and full recalibration, our policy reduces goal-shift errors from 11.3% to 5.8%—a paired reduction of 5.5 percentage points (participant-bootstrap 95% CI, 3.8–7.2)—and joint-shift errors from 15.4% to 8.1%. Correct-command throughput increases from 8.1 to 9.4 per minute, including calibration, probes, confirmations, and corrections. Full-state information gain requires 1.7 times the diagnostic attention at matched error and coverage; replacing selective resets with global resets increases goal-shift errors to 9.4%.A held-out EEG site with 24 participants and an independent gaze-control cohort with 32 participants reproduce error reductions of 5.1 and 4.6 percentage points, respectively, using the same querying-and-reset rule with interface-specific observation models. These results support a transferable principle: faithful adaptation requires identifying action-changing distinctions and preserving still-valid evidence, rather than recovering every latent factor or restoring yesterday’s predictions.