6 papers
FAConformer: Frequency-Aware Convolutional Transformer for Auditory Attention Decoding
Ziwei Wang, Xingyi He, Tianwang Jia +2
Auditory attention decoding (AAD) aims to infer the attended speaker from neural responses in multi-speaker acoustic environments and is a key problem for neuro-steered hearing sys…
Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions
Ziwei Wang, Zhentao He, Xingyi He +6
Deep learning has achieved transformative performance across diverse domains, largely driven by large-scale and high-quality training data. In contrast, the development of brain-co…
DBConformer: Dual-Branch Convolutional Transformer for EEG Decoding
Ziwei Wang, Hongbin Wang, Tianwang Jia +3
Electroencephalography (EEG)-based brain-computer interfaces (BCIs) transform spontaneous/evoked neural activity into control commands for external communication. While convolution…
Magnetoencephalography (MEG) Based Non-Invasive Chinese Speech Decoding
Zhihong Jia, Hongbin Wang, Yuanzhong Shen +4
As an emerging paradigm of brain-computer interfaces (BCIs), speech BCI has the potential to directly reflect auditory perception and thoughts, offering a promising communication a…
SACM: SEEG-Audio Contrastive Matching for Chinese Speech Decoding
Hongbin Wang, Zhihong Jia, Yuanzhong Shen +5
Speech disorders such as dysarthria and anarthria can severely impair the patient's ability to communicate verbally. Speech decoding brain-computer interfaces (BCIs) offer a potent…
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…