4 citations · 5 across the 6 of their papers we have counts for
6 papers
ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding
Kanglei Zhou, Chunyan Lan, Dongyang Li +2
Electroencephalogram (EEG) visual decoding aims to recover visual semantics from non-invasive neural time-series signals, for which robust alignment between noisy neural responses…
MindPilot: Closed-loop Visual Stimulation Optimization for Brain Modulation with EEG-guided Diffusion
Dongyang Li, Kunpeng Xie, Mingyang Wu +5
Whereas most brain-computer interface research has focused on decoding neural signals into behavior or intent, the reverse challenge-using controlled stimuli to steer brain activit…
ChineseEEG-2: An EEG Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening
Sitong Chen, Beiqianyi Li, Cuilin He +8
EEG-based neural decoding requires large-scale benchmark datasets. Paired brain-language data across speaking, listening, and reading modalities are essential for aligning neural a…
BrainFLORA: Uncovering Brain Concept Representation via Multimodal Neural Embeddings
Dongyang Li, Haoyang Qin, Mingyang Wu +2
Understanding how the brain represents visual information is a fundamental challenge in neuroscience and artificial intelligence. While AI-driven decoding of neural data has provid…
RealMind: Advancing Visual Decoding and Language Interaction via EEG Signals
Dongyang Li, Haoyang Qin, Mingyang Wu +4
Decoding visual stimuli from neural recordings is a critical challenge in the development of brain-computer interfaces (BCIs). Although recent EEG-based decoding approaches have ma…
Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion
Dongyang Li, Chen Wei, Shiying Li +3
How to decode human vision through neural signals has attracted a long-standing interest in neuroscience and machine learning. Modern contrastive learning and generative models imp…