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.
arXiv:2601. 07556v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computational constraints.
arXiv:2610.07420v1 Announce Type: new Abstract: Electroencephalography (EEG) signals drift over time, which can cause static brain-computer interface (BCI) models to degrade in practice. We present a...
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.
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.
arXiv:2608. 13072v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology.
arXiv:2609.20904v1 Announce Type: cross Abstract: Hybrid motor-imagery brain-computer interfaces (MI-BCIs) combining EEG and fNIRS can outperform EEG-only systems by exploiting complementary electrop...
arXiv:2608. 00048v1 Announce Type: cross Abstract: Electroencephalography (EEG) generation is essential for alleviating data scarcity and enabling large scale neural modeling in brain computer interface applications.
arXiv:2606. 00106v1 Announce Type: cross Abstract: Brain-computer interfaces (BCIs) are limited by low signal-to-noise ratio in modalities such as electroencephalography, which requires multiple trials to reliably decode user intentions.
arXiv:2607. 03094v1 Announce Type: new Abstract: Electroencephalography (EEG) decoding for brain-computer interfaces (BCIs) faces a major challenge: substantial inter-subject variability limits effective cross-subject generalization.
arXiv:2608.24727v1 Announce Type: cross Abstract: EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especia...
EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especially across diverse clinical datasets. Full fine-tun...
arXiv:2606. 01767v1 Announce Type: new Abstract: Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-specific architectures that severely limit cross-task scalability.