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
This review discusses how neuroimaging data can be represented as symmetric positive-definite (SPD) matrices and analyzed using the Riemannian geometry of the SPD manifold. It surveys the evolution from modality-specific SPD representations to geometric shallow and deep learning methods, emphasizing how these approaches maintain structural constraints while integrating modern AI techniques. The paper frames SPD matrix learning as a bridge between classical geometric statistics and contemporary machine learning in neuroimaging and brain‑computer interface research.
By Ce Ju, Reinmar Kobler, Antoine Collas, Motoaki Kawanabe, Cuntai Guan, Bertrand Thirion
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: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
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)
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