Exact Temporal Readouts Expose and Test a Two-Boundary Decision Pattern in Imagined-Speech EEG

Temporal aggregation can determine an imagined-speech EEG prediction, yet its role is usually opaque. We introduce an exact temporal readout framework built around a bidirectional exponential-memory mixture (BEM): each trial logit is a normalized, analytically recoverable kernel over frame-level class evidence anchored at both trial boundaries. On BCI Competition 2020 Track 3, BEM raises development accuracy from 77.07 ± 1.01% to 85.73 ± 1.57% and gains 7.11 participant-paired percentage points on a training-excluded historical audit. Its exact kernels place 89.67% of their mass in the outer fifths. Distilling this pattern into endpoint-fifths pooling yields no detected difference from BEM; applying the rule to the same frozen mean-trained evidence tensors gains 4.13 points on development and 2.98 on audit, isolating aggregation from encoder retraining. Boundary-masking and filtering controls further support reliance on broad two-edge structure. In a separate NEMAR on007591 calibration-to-online stress test, models were locally frozen before EEG or decoder-outcome inspection, although event labels and official quality-control metadata were known. The declared raw-accuracy gate passes: all three participants favor BEM, with a 4.67-point mean gain. However, balanced accuracy falls for all three, and a disclosed post-outcome same-evidence endpoint transfer loses 2.67 points. Exact readouts therefore turn a temporal preference into a distillable, intervenable, and externally falsifiable hypothesis, while the mixed stress test rules out a general two-boundary law.