19 citations · 65 across the 21 of their papers we have counts for
10 papers · 1 filter
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…
Advancing Spiking Neural Networks for Sequential Modeling with Central Pattern Generators
Changze Lv, Dongqi Han, Yansen Wang +3
Spiking neural networks (SNNs) represent a promising approach to developing artificial neural networks that are both energy-efficient and biologically plausible. However, applying…
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…
Efficient and Effective Time-Series Forecasting with Spiking Neural Networks
Changze Lv, Yansen Wang, Dongqi Han +3
Spiking neural networks (SNNs), inspired by the spiking behavior of biological neurons, provide a unique pathway for capturing the intricacies of temporal data. However, applying S…
EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model
Yuqi Chen, Kan Ren, Kaitao Song +4
Self-supervised learning has emerged as a highly effective approach in the fields of natural language processing and computer vision. It is also applicable to brain signals such as…