arXiv Machine Learning
5d ago

Memory-Augmented Online Test-Time Adaptation for EEG Motor Imagery via Perturbation Consistency and Confidence Calibration

The paper introduces MOPC, a memory‑augmented online test‑time adaptation framework for EEG motor‑imagery BCIs that operates without source data. It uses random‑mask perturbations to enforce prediction consistency, a confidence‑calibrated reweighting loss to handle pseudolabels and class imbalance, and a lightweight memory module to prevent catastrophic forgetting. Evaluated on BCI IV IIb and OpenBMI datasets, MOPC outperforms prior methods by 2.22% and 3.67% while maintaining per‑trial adaptation latency of 40–60 ms.

By Kunkun Zhang, Kehao Wu, Zhitao Chen, Haigen Hu, Qianwei Zhou
arXiv AI
Jul 7

Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts

arXiv:2604. 16926v2 Announce Type: replace-cross Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by distribution shifts across clinical settings, devices, and populations.

By Gabriel Jason Lee, Jathurshan Pradeepkumar, Jimeng Sun