17 papers
STEAM: A Spatio-TEmporal Alignment Mixture-of-Experts Model with Hierarchical Pre-training for EEG Decoding
Zhu Chen, Dingkun Liu, Yuheng Chen +1
Brain-computer interfaces (BCIs) have been widely used in motor rehabilitation, disease diagnosis, and other neural engineering scenarios. However, conventional neural signal decod…
StackingNet: Collective Inference Across Independent AI Foundation Models
Siyang Li, Chenhao Liu, Dongrui Wu +2
Artificial intelligence built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems remain isolated and cannot readil…
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…
EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models
Dingkun Liu, Yuheng Chen, Zhu Chen +5
Electroencephalography (EEG) foundation models (FMs) have recently emerged as a promising paradigm for brain-computer interfaces, aiming to learn transferable neural representation…
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…