19 citations · 31 across the 10 of their papers we have counts for
10 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…
Translating Mental Imaginations into Characters with Codebooks and Dynamics-Enhanced Decoding
Jingyuan Li, Yansen Wang, Nie Lin +1
Advancements in non-invasive electroencephalogram (EEG)-based Brain-Computer Interface (BCI) technology have enabled communication through brain activity, offering significant pote…
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…
The VEP Booster: A Closed-Loop AI System for Visual EEG Biomarker Auto-generation
Junwen Luo, Chengyong Jiang, Qingyuan Chen +5
Effective visual brain-machine interfaces (BMI) is based on reliable and stable EEG biomarkers. However, traditional adaptive filter-based approaches may suffer from individual var…
A Large-scale Medical Visual Task Adaptation Benchmark
Shentong Mo, Xufang Luo, Yansen Wang +1
Visual task adaptation has been demonstrated to be effective in adapting pre-trained Vision Transformers (ViTs) to general downstream visual tasks using specialized learnable layer…
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…
LSPT: Long-term Spatial Prompt Tuning for Visual Representation Learning
Shentong Mo, Yansen Wang, Xufang Luo +1
Visual Prompt Tuning (VPT) techniques have gained prominence for their capacity to adapt pre-trained Vision Transformers (ViTs) to downstream visual tasks using specialized learnab…
ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
Yuqi Chen, Kan Ren, Yansen Wang +3
Modeling continuous-time dynamics on irregular time series is critical to account for data evolution and correlations that occur continuously. Traditional methods including recurre…