Stimulated brain responses last twice as long as textbooks say: SafeLens builds its own null from resting data and tracks cortical evoked potentials to 1,040 ms
SafeLens pairs nonlinear machine learning with strict false-positive control for event-locked neural activity. It trains a random forest at each sliding-window position to separate event-locked trials from resting trials, then runs the same pipeline on set-aside resting recordings to build a model-matched empirical null, holding the family-wise error rate per channel. On simulated cortico-cortical evoked potentials injected into real resting intracranial EEG it resolved about 2.5 times as many polarity-resolved deflections as trial-average thresholding and the closest data-driven method, with 5 to 11 times fewer false components; in eight patients responses stayed above chance to 1,040 ms after stimulation, against 570 and 455 ms for the two benchmarks.