27 citations · 95 across the 17 of their papers we have counts for
10 papers · 1 filter
Temporal Out-of-Distribution Detection for Asynchronous Motor Imagery Brain-Computer Interfaces
Chenhao Liu, Siyang Li, Luofei Tan +1
Real online brain--computer interfaces operate on continuous electroencephalography (EEG) streams, where users are usually at rest and enter motor-imagery task states only intermit…
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…
AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding
Xiaoqing Chen, Siyang Li, Dongrui Wu
Electroencephalogram (EEG) decoding models for brain-computer interfaces (BCIs) struggle with cross-dataset learning and generalization due to channel layout inconsistencies, non-s…
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…
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…
Motor Imagery Classification for Asynchronous EEG-Based Brain-Computer Interfaces
Huanyu Wu, Siyang Li, Dongrui Wu
Motor imagery (MI) based brain-computer interfaces (BCIs) enable the direct control of external devices through the imagined movements of various body parts. Unlike previous system…
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…
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…