27 citations · 89 across the 13 of their papers we have counts for
15 papers
SCORE: Subject Coordinate Recovery for Label-Free Cross-Subject EEG-to-Image Retrieval
Zhenyao Cui, Siyuan Kan, Siyang Li +2
Accurate visual decoding can reveal how the brain represents visual information and recover perceived content from neural signals such as electroencephalography (EEG), with potenti…
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…
RAICL: Retrieval-Augmented In-Context Learning for Vision-Language-Model Based EEG Seizure Detection
Siyang Li, Zhuoya Wang, Xiyan Gui +4
Electroencephalogram (EEG) decoding is a critical component of medical diagnostics, rehabilitation engineering, and brain-computer interfaces. However, contemporary decoding method…
Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces
Siyang Li, Jiayi Ouyang, Zhenyao Cui +4
Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, signal non-stationarity, and computationa…
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…
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…
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
Dingkun Liu, Siyang Li, Ziwei Wang +2
A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. However, decoding…
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…
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
Siyang Li, Ziwei Wang, Hanbin Luo +2
Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and…
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces
Ziwei Wang, Siyang Li, Jingwei Luo +2
A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular f…
Canine EEG Helps Human: Cross-Species and Cross-Modality Epileptic Seizure Detection via Multi-Space Alignment
Z. Wang, S. Li, Dongrui Wu
Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along with various animal species. The diagnostic processes of epilepsy are often hindere…
User-wise Perturbations for User Identity Protection in EEG-Based BCIs
Xiaoqing Chen, Siyang Li, Yunlu Tu +2
Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied…
Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs
Xiaoqing Chen, Ziwei Wang, Dongrui Wu
Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accura…