The paper introduces a personalised federated learning approach for EEG decoding, applying it to both a Riemannian SPDNet and a Euclidean EEGNet. In this scheme, all subjects share a common trunk that learns a latent representation, while each subject retains a private head for classification, thereby addressing inter‑subject variability. Experiments on three motor‑imagery datasets show that personalised SPDNet outperforms standard federated learning and centralised training, converges faster, and communicates fewer parameters, and it also surpasses all EEGNet variants on two of the datasets.
By Thibault Pautrel, Florent Bouchard, Ammar Mian, Guillaume Ginolhac
arXiv:2606. 16462v1 Announce Type: cross Abstract: Cross-subject EEG decoding promises more training data, but it also exposes neural networks to strong inter-subject distribution shifts.
By Bruna J. Lopes, Gabriel Schwartz, Sylvain Chevallier, Raphael Y. de Camargo, Bruno Aristimunha
arXiv:2607. 22733v1 Announce Type: cross Abstract: We investigate whether a generative model can supply useful synthetic motor-imagery (MI) electroencephalography (EEG) trials that improve the accuracy of independent downstream classifiers.
By Matei Moldoveanu, Alain Sirois, Claire Ben Ali, Fabien Lotte, Florian Yger
arXiv:2604.22494v2 Announce Type: replace-cross
Abstract: We introduce two federated learning frameworks for the classical SPDnet model operating on symmetric positive definite (SPD) matrices with St...
By Thibault Pautrel, Florent Bouchard, Ammar Mian, Guillaume Ginolhac
The paper introduces FRIST, a two‑stage EEG decoding framework that uses fMRI data to learn spectral projections and class geometry, then refines EEG predictions. In experiments with 12 participants, FRIST improves finger‑level motor decoding accuracy across both movement execution and motor imagery tasks, outperforming EEG‑only baselines and generalizing across different EEG backbones. The method demonstrates that fMRI’s spatial resolution can enhance noninvasive EEG‑based BCI performance.
By Jintao Zhang, Yidan Ding, Joshua Kosnoff, Maxim Karrenbach, Hanwen Wang, Bin He
arXiv:2601. 17883v3 Announce Type: replace Abstract: Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain-computer interfaces, aiming to learn transferable neural representations from large-scale heterogeneous recordings.
By Dingkun Liu, Yuheng Chen, Zhu Chen, Zhenyao Cui, Yaozhi Wen, Jiayu An, Jingwei Luo, Dongrui Wu
arXiv:2509. 17920v2 Announce Type: replace Abstract: Current deep learning models for electroencephalography (EEG) are often task-specific and depend on large labeled datasets, limiting their adaptability.
By Jamiyan Sukhbaatar, Satoshi Imamura, Ibuki Inoue, Shoya Murakami, Kazi Mahmudul Hassan, Seungwoo Han, Ingon Chanpornpakdi, Toshihisa Tanaka
arXiv:2608.03176v2 Announce Type: replace
Abstract: Imagined handwriting offers a temporally rich paradigm for non-invasive neural decoding, yet reliable recognition across unseen participants remain...
By Xiao Fan, Hongbin Guo, Yubo Han, Yi Zhang
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.
By Aymen Sarhane, Fouad Lbakali, Mouad Souissi, Jonathan Lys, Giulia Lioi
BRIDGE-EEG is an efficient multi‑task EEG classification pipeline that leverages self‑supervised pretraining while dramatically reducing model size. It maps heterogeneous EEG recordings to a unified 62‑channel time‑frequency representation, pretrains an SE‑ResNet18 teacher with SimCLR, and distills it into smaller SE‑ResNet8 and SE‑ResNet4 students. The compact models achieve accuracy comparable to or better than larger foundation models on abnormality detection and emotion recognition, and they consume up to three times less energy on edge devices, enabling deployment on wearable hardware.
By Meghna Roy Chowdhury, Chengwei Zhou, Haotian Yu, Gourav Datta, Shreyas Sen
In clinical motor imagery brain-computer interface (MI-BCI) decoding, cross-day transferability and online operation remain two critical challenges. Hypergraphs can improve transferability by capturing higher-order sample relationships, yet existing hypergraph-based methods for online emotion recognition neglect the cross-day benefits of Riemannian geometry widely adopted in EEG transfer learning.
arXiv:2609.13507v1 Announce Type: new
Abstract: Brain-computer interfaces (BCIs) decode neural activity to restore lost function. Typically, training a high-performance neural decoder requires a larg...
By Ben Tang, Zachary Spalding, Gregory B. Cogan