# Cross-session intracortical speech decoding

Across days, neural drift degrades a fixed intracortical speech decoder. One study confined adaptation to a 256×256 affine transform and bias at the neural input, updating 65,792 parameters, under 0.05% of the full model, while the recurrent encoder and phoneme classifier stayed frozen, using 40 or 80 labeled trials for supervised calibration. On the public T12 dataset, macro phoneme error rate fell 11.37% and 15.51% across six chronologically held-out sessions, with all six improving under all three seeds; on the independent T15 dataset the same frozen rule gave 2.72% and 10.05% reductions, with more session-to-session variability and neither budget below the nominal 0.05 reference threshold.

Across days, neural drift degrades a fixed intracortical speech decoder. One study confined adaptation to a 256×256 affine transform and bias at the neural input, updating 65,792 parameters, under 0.05% of the full model, while the recurrent encoder and phoneme classifier stayed frozen, using 40 or 80 labeled trials for supervised calibration. On the public T12 dataset, macro phoneme error rate fell 11.37% and 15.51% across six chronologically held-out sessions, with all six improving under all three seeds; on the independent T15 dataset the same frozen rule gave 2.72% and 10.05% reductions, with more session-to-session variability and neither budget below the nominal 0.05 reference threshold.