Adaptive Data Compression and Reconstruction for Memory-Bounded EEG Continual Learning

TL;DR

This paper introduces ADaCoRe, a memory-efficient EEG continual learning method that uses morphology-aware compression and reconstruction to improve personalization under strict memory constraints.

Contribution

It proposes a novel adaptive compression and reconstruction pipeline tailored for EEG data in continual learning scenarios, outperforming existing methods.

Findings

ADaCoRe achieves at least +2.7 and +15.3 accuracy gains on ISRUC and FACED datasets.

The method maintains key EEG morphology during compression and reconstruction.

Ablation studies show the effectiveness of each component in ADaCoRe.

Abstract

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. However, existing UICL methods typically store full past samples, which undermine the continual learning goal of avoiding retraining. Observing that EEG signals exhibit well-structured morphologies to be exploited via morphology-aware selection, compression, and reconstruction, here we propose Adaptive Data Compression and Reconstruction (ADaCoRe) for UICL. This is a memory-efficient pipeline composed of saliency-driven keyframe protection, rational polyphase compression, adjoint reconstruction with verbatim overwrite on protected indices, and prototype-confidence selection for adaptive exemplar maintenance. Across three representative benchmarks, ADaCoRe consistently outperforms recent strong baselines under tight buffer regimes (eg., the performance gains are at least +2.7 and +15.3 ACC on ISRUC and FACED datasets, respectively). Ablation studies quantify compression-fidelity trade-offs and highlight the contribution of each design, while visualizations confirm the preservation of key EEG morphology during compression and reconstruction.