RPT: Resultant-Power Transport for Data-Free Continual EEG Model Merging

Electroencephalography (EEG) supports diverse brain–computer interface and monitoring applications, requiring models that adapt to new subjects while retaining previously acquired knowledge. However, inter-subject variability can undermine knowledge retention during adaptation, and replay-based remedies require storing sensitive EEG recordings, raising privacy concerns and increasing storage demands. Continual model merging (CMM) offers a scalable alternative by incrementally merging subject-specific fine-tuned models without storing or replaying historical EEG. Nevertheless, conflicting parameter updates and differing normalization states across subjects can disrupt accumulated knowledge during merging. We introduce Resultant-Power Transport (RPT), which recursively merges subject models without accessing EEG or calibration samples. RPT uses task-vector agreement to scale layer-specific targets and selects incoming contributions through a closed-form, history-weighted projection that preserves the relative weights of historical contributions. Affine-map BN merging (AM-BN) complements this update by merging the inference transformations jointly defined by batch normalization statistics and affine parameters. Experiments across three EEG tasks demonstrate that RPT outperforms competing CMM methods, enabling data-free continual adaptation to new subjects while sustaining performance on previously encountered subjects.