3 citations · 12 across the 10 of their papers we have counts for
10 papers
Assembling the Mind's Mosaic: Towards EEG Semantic Intent Decoding
Jiahe Li, Junru Chen, Fanqi Shen +6
Enabling natural communication through brain-computer interfaces (BCIs) remains one of the most profound challenges in neuroscience and neurotechnology. While existing frameworks o…
NeuroSketch: A Practical Design Recipe for Neural Decoding
Gaorui Zhang, Zhizhang Yuan, Jialan Yang +4
Neural decoding is fundamental to brain-computer interfaces, with growing applications in healthcare. Previous research has focused on leveraging signal processing and deep learnin…
PD: A Project Duplication Detection Framework via Adapted Multi-Agent Debate
Dezheng Bao, Yueci Yang, Chutian Yu +9
Project duplication detection is critical for project quality assessment because it helps avoid investment in repeated proposals. Existing methods usually cast it as ranking and re…
Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnostics
Jiahe Li, Xin Chen, Fanqi Shen +7
Neurological disorders pose major global health challenges, driving advances in brain signal analysis. Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are widely use…
Brant-X: A Unified Physiological Signal Alignment Framework
Daoze Zhang, Zhizhang Yuan, Junru Chen +2
Physiological signals serve as indispensable clues for understanding various physiological states of human bodies. Most existing works have focused on a single type of physiologica…
Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification
Junru Chen, Tianyu Cao, Jing Xu +4
Time Series Classification (TSC) encompasses two settings: classifying entire sequences or classifying segmented subsequences. The raw time series for segmented TSC usually contain…
Are Synthetic Time-series Data Really not as Good as Real Data?
Fanzhe Fu, Junru Chen, Jing Zhang +3
Time-series data presents limitations stemming from data quality issues, bias and vulnerabilities, and generalization problem. Integrating universal data synthesis methods holds pr…
MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals
Donghong Cai, Junru Chen, Yang Yang +2
Brain signals are important quantitative data for understanding physiological activities and diseases of human brain. Most existing studies pay attention to supervised learning met…
BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning
Junru Chen, Yang Yang, Tao Yu +3
Epilepsy is one of the most serious neurological diseases, affecting 1-2% of the world's population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves…
Unsupervised Adversarially-Robust Representation Learning on Graphs
Jiarong Xu, Yang Yang, Junru Chen +4
Unsupervised/self-supervised pre-training methods for graph representation learning have recently attracted increasing research interests, and they are shown to be able to generali…