4 papers
Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs
Jun-Yu Pan, Yansen Wang, Enze Zhang +3
Leveraging the universal representations of pre-trained LLMs and MLLMs offers a promising path toward brain foundation models. However, visually-evoked EEG datasets remain scarce,…
EEGChaT: A Transformer-Based Modular Channel Selector for SEEG Analysis
Chen Wang, Yansen Wang, Dongqi Han +2
Analyzing stereoelectroencephalography (SEEG) signals is critical for brain-computer interface (BCI) applications and neuroscience research, yet poses significant challenges due to…
EgoBrain: Synergizing Minds and Eyes For Human Action Understanding
Nie Lin, Yansen Wang, Dongqi Han +5
The integration of brain-computer interfaces (BCIs), in particular electroencephalography (EEG), with artificial intelligence (AI) has shown tremendous promise in decoding human co…
NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals
Wei-Bang Jiang, Yansen Wang, Bao-Liang Lu +1
Recent advancements for large-scale pre-training with neural signals such as electroencephalogram (EEG) have shown promising results, significantly boosting the development of brai…