Decide Before You Record: Heterogeneity-Guided Pre-Acquisition Stimulus Selection for Cross-Day Neuroprostheses

Decoding language intentions from intracranial electroencephalography signals enables direct communication for patients with severe speech impairments. However, practical deployment remains severely limited by cross-day performance degradation caused by physiological and instrumental heterogeneity, requiring patients to record extensive calibration data daily to maintain decoding performance. Here we introduce \textbf{He}terogeneity-Guided \textbf{D}ata \textbf{A}cquisition and \textbf{A}daptation (\textbf{HeDA\textsuperscript{2}}), a novel calibration paradigm that \textbf{determines which stimuli to record} for optimal calibration \textbf{before any neural data are acquired}, fundamentally departing from existing methods that passively adapt to whatever data happen to be available. HeDA\textsuperscript{2} operates through three stages: it first quantifies cross-day neural heterogeneity with a proposed \textbf{S}entence-level \textbf{H}eterogeneity via \textbf{A}lignment \textbf{P}ath \textbf{E}ntropy that captures how different phonetic phenomena exhibit varying degrees of heterogeneity; second, an estimator learns to predict this heterogeneity from historical patterns, enabling strategic selection of calibration sentences before any neural recording occurs; third, heterogeneity-aware adaptation updates the decoder using minimal acquired data. Extensive experiments on intracranial recordings across diverse recalibration settings demonstrate that, with merely 20\% of conventionally required calibration data, HeDA\textsuperscript{2} achieves comparable or superior decoding accuracy, representing a critical step toward clinically viable speech prosthesis.