AI RESEARCH
Adaptive Data Compression and Reconstruction for Memory-Bounded EEG Continual Learning
arXiv CS.LG
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ArXi:2605.03085v1 Announce Type: new Electroencephalography (EEG) signals provide millisecond-level temporal resolution but their analysis is limited by remarkable noise and inter-subject variability, making robust personalization difficult under limited annotations. Unsupervised Individual Continual Learning (UICL) has been proposed to address this practical challenge, where a model pretrained on a labeled cohort must adapt online to unlabeled subject streams under strict memory constraints.