5 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…
Automated Contrastive Learning Strategy Search for Time Series
Baoyu Jing, Yansen Wang, Guoxin Sui +5
In recent years, Contrastive Learning (CL) has become a predominant representation learning paradigm for time series. Most existing methods manually build specific CL Strategies (C…