arXiv Machine Learning

Towards Robust EEG Decoding Based on Riemannian Self-Attention

arXiv:2606. 25456v1 Announce Type: new Abstract: Brain-Computer Interface (BCI) based on electroencephalography (EEG) enables direct interaction between the brain and external environments and has significant applications in assistive technologies, medical rehabilitation, and entertainment.

arXiv Machine Learning

Jun 5

A Sliced-Wasserstein Framework on Correlation Matrices for EEG Decoding

arXiv:2606. 06104v1 Announce Type: new Abstract: Electroencephalography (EEG) offers noninvasive, millisecond resolution recordings of neuronal activity and is widely used in neuroscience and healthcare.

By Chen Hu, Rui Wang, Jiale Zhou, Jingjun Yi, Shaocheng Jin, Yidong Song, Yefeng Zheng

Hugging Face Trending Papers

2d ago

Multi-Feature Riemannian Hypergraph for Online Test-Time Adaptation of Motor Imagery Brain-Computer Interface

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.

fine-tuning benchmarks safety

arXiv Machine Learning

Jul 10

Geometry-Aware Deep Congruence Networks for Manifold Learning in Cross-Subject Motor Imagery

arXiv:2511. 18940v3 Announce Type: replace Abstract: Cross-subject motor imagery decoding remains a fundamental challenge in EEG-based brain-computer interfaces due to substantial inter-subject variability.

By Sanjeev Manivannan, Chandra Shekar Lakshminarayan

benchmarks safety

arXiv Machine Learning

1d ago

Multi-Feature Riemannian Hypergraph for Online Test-Time Adaptation of Motor Imagery Brain-Computer Interface

arXiv:2608. 16134v1 Announce Type: new Abstract: In clinical motor imagery brain-computer interface (MI-BCI) decoding, cross-day transferability and online operation remain two critical challenges.

By Siqi Li (Peking University, Chinese Institute for Brain Research, Beijing), Zhi Li (NeuCyber Neurotech), Tong Liu (NeuCyber Neurotech), Shuai Zhang (NeuCyber Neurotech), Yanfei Jia (Beijing Medical University), Zhiqiang Yi (Beijing Medical University), Jue Xie (NeuCyber Neurotech), Ni Ji (Chinese Academy of Medical Sciences & Peking Union Medical College, Chinese Institute for Brain Research, Beijing)

fine-tuning benchmarks safety

arXiv Machine Learning

Jul 3

I\textsuperscript{2}RiMA: Spectral Riemannian Representation with Temporal Attention for Mental Stress Detection based on EEG Signals

arXiv:2607. 01279v1 Announce Type: new Abstract: Cross-subject EEG stress detection remains challenging because discriminative stress-related patterns are both subject-dependent and frequency-specific.

By Cheng He, Kunyu Peng, Shangen Han, Jinming Ma, Jinhong Ding, Likun Xia

nlp benchmarks

arXiv Machine Learning

Aug 7

Rectifying Geometric Misalignment: Online Source-Free Adaptation for Class-Imbalanced EEG

arXiv:2608. 05315v1 Announce Type: new Abstract: Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs) often require unsupervised domain adaptation (UDA) to generalize across subjects and sessions.

By Shiwen Chu, Shanglin Li, Motoaki Kawanabe, Reinmar Kobler

fine-tuning safety

arXiv AI

Jul 24

Riemannian Deep Learning: Modules, Networks, and Geometries

arXiv:2607. 19305v2 Announce Type: replace-cross Abstract: Deep neural networks on manifold-valued representations have attracted growing interest, but many basic components remain tied to specific manifolds, rely on Euclidean approximations, or require costly and numerically fragile geometric operations.

By Chen Ziheng

arXiv Machine Learning

Jun 30

BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis

arXiv:2606. 29200v1 Announce Type: new Abstract: Multi-site functional MRI (fMRI) studies are essential for robust neuropsychiatric diagnosis yet suffer severe domain shifts from scanner heterogeneity, demographics, and site-specific acquisition protocols.

By Kunyu Zhang, Tianxiang Xu

fine-tuning benchmarks safety

arXiv AI

Jul 22

Riemannian Deep Learning:Modules, Networks, and Geometries

arXiv:2607. 19305v1 Announce Type: cross Abstract: Deep neural networks on manifold-valued representations have attracted growing interest, but many basic components remain tied to specific manifolds, rely on Euclidean approximations, or require costly and numerically fragile geometric operations.

By Chen Ziheng

arXiv AI

Jul 13

LieBN: Batch Normalization over Lie Groups

arXiv:2607. 08783v1 Announce Type: cross Abstract: Manifold-valued measurements are prevalent in various machine learning tasks.

By Ziheng Chen, Yue Song, Rui Wang, Xiao-Jun Wu, Nicu Sebe

arXiv Machine Learning

Aug 11

SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization

arXiv:2602. 22895v2 Announce Type: replace-cross Abstract: Implementations of symmetric positive definite (SPD) matrix-based neural networks for neural decoding remain fragmented across research codebases and Python packages.

By Bruno Aristimunha, Ce Ju, Antoine Collas, Florent Bouchard, Ammar Mian, Bertrand Thirion, Sylvain Chevallier, Reinmar Kobler

benchmarks

Hugging Face Trending Papers

Jul 21

1-Lipschitz Neural Networks on Hadamard Manifolds

Controlling the Lipschitz constant of a neural network is a standard way to promote robustness and stability. Most existing constraining strategies are designed for Euclidean spaces.

Related stories