← Back to all news
arXiv Machine Learning By Kunkun Zhang, Kehao Wu, Zhitao Chen, Haigen Hu, Qianwei Zhou

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

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
benchmarkssafety
More like this →
arXiv Machine Learning
Sep 22

Adaptive Forgetting for Nonstationary Optimization: Towards Robust EEG Decoding

The paper introduces AFOR, a tensor‑wise adaptive optimizer for EEG decoding that replaces the fixed second‑moment decay coefficient used in Adam/AdamW with a dynamic coefficient estimated online from local gradient state. AFOR combines a Residual‑Alignment Signal Scorer (RASS) to assess gradient quality and an Adaptive Forgetting Controller (AFC) to map this score to a bounded per‑step decay coefficient, with cumulative‑product initialization correction for consistency. In cross‑subject experiments on three EEG benchmarks, AFOR outperforms standard optimizers, improving mean test accuracy by 3.00%, 2.07%, and 4.38% over Adam.

By Hongyu Zhu, Lin Chen, Jing Chen, Yuting Zhou, Mingsheng Shang
benchmarkssafety
More like this →