82 citations · 240 across the 15 of their papers we have counts for
17 papers
Spiking Neural Network for Intra-cortical Brain Signal Decoding
Song Yang, Haotian Fu, Herui Zhang +3
Decoding brain signals accurately and efficiently is crucial for intra-cortical brain-computer interfaces. Traditional decoding approaches based on neural activity vector features…
Multimodal Brain-Computer Interfaces: AI-powered Decoding Methodologies
Siyang Li, Hongbin Wang, Xiaoqing Chen +1
Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs…
MVCNet: Multi-View Contrastive Network for Motor Imagery Classification
Ziwei Wang, Siyang Li, Xiaoqing Chen +1
Electroencephalography (EEG)-based brain-computer interfaces (BCIs) enable neural interaction by decoding brain activity for external communication. Motor imagery (MI) decoding has…
Effective and Efficient Intracortical Brain Signal Decoding with Spiking Neural Networks
Haotian Fu, Peng Zhang, Song Yang +3
A brain-computer interface (BCI) facilitates direct interaction between the brain and external devices. To concurrently achieve high decoding accuracy and low energy consumption in…
Privacy-Preserving Brain-Computer Interfaces: A Systematic Review
K. Xia, W. Duch, Y. Sun +8
A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, ed…
Front-end Replication Dynamic Window (FRDW) for Online Motor Imagery Classification
X. Chen, J. An, H. Wu +3
Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successf…