15 citations · 17 across the 21 of their papers we have counts for
6 papers · 1 filter
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…
SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfaces
Tianwang Jia, Xiaoqing Chen, Dongrui Wu
Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) are widely adopted due to their efficiency and portability; however, their decoding algorithms still face multiple…
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…
Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces
Dongrui Wu
Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibrati…