19 citations · 65 across the 21 of their papers we have counts for
23 papers
DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models
Yuqiu Deng, Muyun Jiang, Zongpeng Zhang +8
EEG foundation models (EFMs) have improved EEG representation learning by transferring knowledge from large-scale multi-subject pretraining to downstream tasks. However, their pret…
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,…
Kuramoto Oscillatory Phase Encoding: Neuro-inspired Synchronization for Improved Learning Efficiency
Mingqing Xiao, Yansen Wang, Dongqi Han +2
Spatiotemporal neural dynamics and oscillatory synchronization are widely implicated in biological information processing and have been hypothesized to support flexible coordinatio…
Improving Diffusion Planners by Self-Supervised Action Gating with Energies
Yuan Lu, Dongqi Han, Yansen Wang +1
Diffusion planners are a strong approach for offline reinforcement learning, but they can fail when value-guided selection favours trajectories that score well yet are locally inco…
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…
Chain-of-Model Learning for Language Model
Kaitao Song, Xiaohua Wang, Xu Tan +14
In this paper, we propose a novel learning paradigm, termed Chain-of-Model (CoM), which incorporates the causal relationship into the hidden states of each layer as a chain style,…
SimSort: A Data-Driven Framework for Spike Sorting by Large-Scale Electrophysiology Simulation
Yimu Zhang, Dongqi Han, Yansen Wang +3
Spike sorting is an essential process in neural recording, which identifies and separates electrical signals from individual neurons recorded by electrodes in the brain, enabling r…
Toward Relative Positional Encoding in Spiking Transformers
Changze Lv, Yansen Wang, Dongqi Han +4
Spiking neural networks (SNNs) are bio-inspired networks that mimic how neurons in the brain communicate through discrete spikes, which have great potential in various tasks due to…
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…
Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals
Yu-Ting Lan, Kan Ren, Yansen Wang +4
Seeing is believing, however, the underlying mechanism of how human visual perceptions are intertwined with our cognitions is still a mystery. Thanks to the recent advances in both…
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling
Ziyue Li, Yuchen Fang, You Li +7
A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU). However, i…
Story Ending Generation with Incremental Encoding and Commonsense Knowledge
Jian Guan, Yansen Wang, Minlie Huang
Generating a reasonable ending for a given story context, i.e., story ending generation, is a strong indication of story comprehension. This task requires not only to understand th…
Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders
Yansen Wang, Chenyi Liu, Minlie Huang +1
Asking good questions in large-scale, open-domain conversational systems is quite significant yet rather untouched. This task, substantially different from traditional question gen…