Aug 2026· Cognitive Neurodynamics· Vol 20· 0 citations· 32 references
Medicine
TL;DR
A dynamic re-initialization framework for EEG–fNIRS multimodal decoding achieves cross-modal balanced learning through a diagnosis–adjustment–re-initialization mechanism, which achieves cross-modal balanced learning through a diagnosis–adjustment–re-initialization mechanism.
A comprehensive taxonomy of MI EEG cross-variability decoding studies from 2020 to 2025 is presented, systematically organizing advances in deep learning and transfer learning and critically evaluate core algorithmic approaches, including Convolutional Neural Networks, transformers, feature alignment, domain adaptation...
Li-Jun Wang, Yue-Ying Zhou, Peng-Pai Wang et al.· Frontiers in Neuroscience· 0 citations
Motor imagery electroencephalography (MI-EEG) decoding remains challenging because of the low signal-to-noise ratio, non-stationarity, and inter-subject variability of EEG signals. This study proposes a dynamic multi-branch EEG decoding network (DMB-EDN) that jointly models temporal dynamics, learnable time-frequency p...
Jing-Xin Cai, Meng-Yao Gao, Guang-Yu Li et al.· Frontiers in Neuroscience· 0 citations
Despite the widespread adoption of deep learning techniques in motor imagery (MI) electroencephalogram (EEG) decoding, the limited decoding performance persists due to the low signal-to-noise ratio of EEG signals and insufficient exploration of MI-related information from temporal, frequency and spatial domains. Theref...
Yun-Feng Qin, Li Zhang, Yu Liu et al.· Behavioural Brain Research· 0 citations
Brain–computer interfaces (BCIs) enable direct communication between the brain and external devices, providing a bio-inspired link between humans and artificial systems. However, electroencephalography (EEG)-based motor imagery (MI) decoding continues to pose challenges, due to limited temporal exploitation and insuffi...
Combining complementary neurophysiological modalities offers a promising strategy for improving motor imagery (MI) brain-computer interfaces (BCIs), but learning shared representations across modalities remains largely unexplored. Here, we propose a two-phase deep learning framework for multimodal EEG–MEG decoding that...
Giovanni Messuti, Silvia Scarpetta, P. Sorrentino et al.· bioRxiv· 0 citations
Imagined speech decoding remains challenging in brain-computer interfaces (BCIs) due to the low signal-to-noise ratio and complex spatio-temporal-spectral structure of Electroencephalogram (EEG) data. Existing studies mainly rely on single-view features or simple fusion strategies, limiting their ability to capture div...
Zi-Jian Han, Zhao-Hu Liu, Honggang Liu et al.· IEEE transactions on bio-med...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
