When Coarse Annotations Conceal Novel States: TC-REJECT for Auditing Time-Series Labels

Coarse interval labels can conceal changes in behavior or physiology, contaminating scientific analyses and downstream models. Auditing such labels is difficult because an unseen state may appear typical of the training distribution while conflicting with its assigned label. In controlled simulations, global atypicality and label agreement fail in opposite conditions: density scores invert for coherent lower-variance novelty, while large scale shifts reverse their relative utility. We therefore introduce Typicality-Corrected Rejection (TC-REJECT), which calibrates complementary multichannel representations and combines them using e-value summation or rank averaging without requiring examples of the target novel state. Across wearable activity, naturalistic electrocorticography, and clinical local field potential recordings from deep-brain nuclei in two participants with deep-brain stimulation, TC-REJECT detects withheld or concealed states and supports selective label review. On prespecified previously unused neural recordings, rank-based fusion is more robust than e-value summation and achieves the highest mean performance among independent comparison methods on both evaluated novelty targets. TC-REJECT provides a practical framework for auditing and conservatively refining partially labeled time series.