Search Results for author: Yueyang Li

Found 14 papers, 2 papers with code

STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis

no code implementations31 Dec 2024 Wenhao Dong, Yueyang Li, Weiming Zeng, Lei Chen, Hongjie Yan, Wai Ting Siok, Nizhuan Wang

Many existing methods that use functional magnetic resonance imaging (fMRI) classify brain disorders, such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), often overlook the integration of spatial and temporal dependencies of the blood oxygen level-dependent (BOLD) signals, which may lead to inaccurate or imprecise classification results.

Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding

no code implementations23 Dec 2024 Yueyang Li, Zijian Kang, Shengyu Gong, Wenhao Dong, Weiming Zeng, Hongjie Yan, Wai Ting Siok, Nizhuan Wang

Experimental results demonstrate significant improvements in visual decoding accuracy and model generalization compared to state-of-the-art methods, advancing the field of EEG-based neural visual representation decoding in BMI.

EEG Representation Learning

EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation

no code implementations30 Sep 2024 Hongyu Chen, Weiming Zeng, Chengcheng Chen, Luhui Cai, Fei Wang, Yuhu Shi, Lei Wang, Wei zhang, Yueyang Li, Hongjie Yan, Wai Ting Siok, Nizhuan Wang

This paper presents the EEG Emotion Copilot, a system optimizing a lightweight large language model (LLM) with 0. 5B parameters operating in a local setting, which first recognizes emotional states directly from EEG signals, subsequently generates personalized diagnostic and treatment suggestions, and finally supports the automation of assisted electronic medical records.

Computational Efficiency EEG +3

A Tale of Single-channel Electroencephalogram: Devices, Datasets, Signal Processing, Applications, and Future Directions

no code implementations20 Jul 2024 Yueyang Li, Weiming Zeng, Wenhao Dong, Di Han, Lei Chen, Hongyu Chen, Hongjie Yan, Wai Ting Siok, Nizhuan Wang

Single-channel electroencephalogram (EEG) is a cost-effective, comfortable, and non-invasive method for monitoring brain activity, widely adopted by researchers, consumers, and clinicians.

EEG Emotion Recognition +1

MHNet: Multi-view High-order Network for Diagnosing Neurodevelopmental Disorders Using Resting-state fMRI

1 code implementation3 Jul 2024 Yueyang Li, Weiming Zeng, Wenhao Dong, Luhui Cai, Lei Wang, Hongyu Chen, Hongjie Yan, Lingbin Bian, Nizhuan Wang

However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification.

Functional Connectivity Graph Neural Network

You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation

no code implementations21 Jun 2024 Hongyu Chen, Weiming Zeng, Luhui Cai, Lei Wang, Jia Lu, Yueyang Li, Hongjie Yan, Wai Ting Siok, Nizhuan Wang

The YOAS totally consists of four sequential stages: Data Preparation, Data Preprocessing, Biased-EEG Generation, and Synthetic EEG Generation.

EEG

MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction

1 code implementation20 Jun 2024 Luhui Cai, Weiming Zeng, Hongyu Chen, Hua Zhang, Yueyang Li, Yu Feng, Hongjie Yan, Lingbin Bian, Wai Ting Siok, Nizhuan Wang

Graph deep learning (GDL) has demonstrated impressive performance in predicting population-based brain disorders (BDs) through the integration of both imaging and non-imaging data.

Graph Learning Representation Learning

Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data

no code implementations12 Nov 2023 Chenyang Bi, Yueyang Li, Haichi Luo

Unsupervised anomaly detection methods are at the forefront of industrial anomaly detection efforts and have made notable progress.

Unsupervised Anomaly Detection

CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection

no code implementations12 Nov 2023 Shunfeng Wang, Yueyang Li, Haichi Luo, Chenyang Bi

While these unsupervised anomaly detection methods offer convenience, they also overlook the crucial prior information embedded within anomalous samples.

Contrastive Learning Self-Supervised Anomaly Detection +2

Sub-action Prototype Learning for Point-level Weakly-supervised Temporal Action Localization

no code implementations16 Sep 2023 Yueyang Li, Yonghong Hou, Wanqing Li

Point-level weakly-supervised temporal action localization (PWTAL) aims to localize actions with only a single timestamp annotation for each action instance.

Pseudo Label Weakly-supervised Temporal Action Localization +1

Replay Attack Detection Based on Parity Space Method for Cyber-Physical Systems

no code implementations3 Jun 2023 Dong Zhao, Yang Shi, Steven X. Ding, Yueyang Li, Fangzhou Fu

The replay attack detection problem is studied from a new perspective based on parity space method in this paper.

Semantic Feature Integration network for Fine-grained Visual Classification

no code implementations13 Feb 2023 Hui Wang, Yueyang Li, Haichi Luo

By eliminating unnecessary features and reconstructing the semantic relations among discriminative features, our SFI-Net has achieved satisfying performance.

Classification Fine-Grained Image Classification

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