Explanipedia

Frequency-Aware Spatial-Temporal Attention Explainable Network for EEG Decoding Open

Luyao Jin, Yonghao Song, Huan Zhao, Junyi Cao, Vincent C. K. Cheung , et al. · 2025

Representation learning in spatial and temporal domains has shown significant potential in EEG decoding, advancing the field of brain-computer interfaces (BCIs). However, the critical role of frequency information, closely tied to the brai…

Neuro-3D: Towards 3D Visual Decoding from EEG Signals Open

Zhanqiang Guo, Jiamin Wu, Yonghao Song, Weijian Mai, Qihao Zheng , et al. · 2024

Human's perception of the visual world is shaped by the stereo processing of 3D information. Understanding how the brain perceives and processes 3D visual stimuli in the real world has been a longstanding endeavor in neuroscience. Towards …

Estimating and approaching the maximum information rate of noninvasive visual brain-computer interface Open

Nanlin Shi, Yining Miao, Changxing Huang, Xiang Li, Yonghao Song , et al. · 2024

An essential priority of visual brain-computer interfaces (BCIs) is to enhance the information transfer rate (ITR) to achieve high-speed communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, l…

On maximal Roman domination in graphs: complexity and algorithms Open

Zehui Shao, Yonghao Song, Qiyun Liu, Zhixing Duan, Huiqin Jiang · 2024

For a simple undirected connected graph G = ( V, E ), a maximal Roman dominating function (MRDF) of G is a function f : V ( G ) → {0, 1, 2} with the following properties: ( i ) For every vertex v ∈ { v ∈ V | f ( v ) = 0}, there exists a ve…

Fusing Ventral Visual Pathway with Text Sequence Stimulation for High-Performance and Comfortable Brain-Computer Interfaces Open

Xiaoyang Li, Shaojie Zhang, Yonghao Song, Xiaogang Chen, Yijun Wang , et al. · 2024

High-performance cVEP-BCI under minimal calibration Open

Yining Miao, Nanlin Shi, Changxing Huang, Yonghao Song, Xiaogang Chen , et al. · 2023

The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs) modu…

Estimating and approaching maximum information rate of noninvasive visual brain-computer interface Open

Nanlin Shi, Yining Miao, Changxing Huang, Xiang Li, Yonghao Song , et al. · 2023

The mission of visual brain-computer interfaces (BCIs) is to enhance information transfer rate (ITR) to reach high speed towards real-life communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs,…

Decoding Natural Images from EEG for Object Recognition Open

Yonghao Song, Bingchuan Liu, Xiang Li, Nanlin Shi, Yijun Wang , et al. · 2023

Electroencephalography (EEG) signals, known for convenient non-invasive acquisition but low signal-to-noise ratio, have recently gained substantial attention due to the potential to decode natural images. This paper presents a self-supervi…

High-Performance Cvep-Bci Under Minimal Calibration Open

Yining Miao, Nanlin Shi, Changxing Huang, Yonghao Song, Xiaogang Chen , et al. · 2023

Global Adaptive Transformer for Cross-Subject Enhanced EEG Classification Open

Yonghao Song, Qingqing Zheng, Qiong Wang, Xiaorong Gao, Pheng‐Ann Heng · 2023

Due to the individual difference, EEG signals from other subjects (source) can hardly be used to decode the mental intentions of the target subject. Although transfer learning methods have shown promising results, they still suffer from po…

EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization Open

Yonghao Song, Qingqing Zheng, Bingchuan Liu, Xiaorong Gao · 2022

Due to the limited perceptual field, convolutional neural networks (CNN) only extract local temporal features and may fail to capture long-term dependencies for EEG decoding. In this paper, we propose a compact Convolutional Transformer, n…

Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation Open

Lei Shen, Haolan Zhan, Xin Shen, Yonghao Song, Xiaofang Zhao · 2021

Open-domain dialogue generation in natural language processing (NLP) is by default a pure-language task, which aims to satisfy human need for daily communication on open-ended topics by producing related and informative responses. In this …

Improving Sequential Recommendation Consistency with Self-Supervised Imitation Open

Yuan Xu, Hongshen Chen, Yonghao Song, Xiaofang Zhao, Zhuoye Ding · 2021

Most sequential recommendation models capture the features of consecutive items in a user-item interaction history. Though effective, their representation expressiveness is still hindered by the sparse learning signals. As a result, the se…

Improving Sequential Recommendation Consistency with Self-Supervised Imitation Open

Yuan Xu, Hongshen Chen, Yonghao Song, Xiaofang Zhao, Zhuoye Ding , et al. · 2021

Most sequential recommendation models capture the features of consecutive items in a user-item interaction history. Though effective, their representation expressiveness is still hindered by the sparse learning signals. As a result, the se…

Transformer-based Spatial-Temporal Feature Learning for EEG Decoding Open

Yonghao Song, Xueyu Jia, Lie Yang, Longhan Xie · 2021

At present, people usually use some methods based on convolutional neural networks (CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have limitations in perceiving global dependencies, which is not adequate for common EEG para…

Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface Open

Yonghao Song, Lie Yang, Xueyu Jia, Longhan Xie · 2021

The cross-subject application of EEG-based brain-computer interface (BCI) has always been limited by large individual difference and complex characteristics that are difficult to perceive. Therefore, it takes a long time to collect the tra…

Motor Imagery EEG Decoding Method Based on a Discriminative Feature Learning Strategy Open

Lie Yang, Yonghao Song, Ke Ma, Longhan Xie · 2021

With the rapid development of deep learning, more and more deep learning-based motor imagery electroencephalograph (EEG) decoding methods have emerged in recent years. However, the existing deep learning-based methods usually only adopt th…

Group-wise Contrastive Learning for Neural Dialogue Generation Open

Hengyi Cai, Hongshen Chen, Yonghao Song, Zhuoye Ding, Yongjun Bao , et al. · 2020

Neural dialogue response generation has gained much popularity in recent years. Maximum Likelihood Estimation (MLE) objective is widely adopted in existing dialogue model learning. However, models trained with MLE objective function are pl…

A Practical EEG-Based Human-Machine Interface to Online Control an Upper-Limb Assist Robot Open

Yonghao Song, Siqi Cai, Lie Yang, Guofeng Li, Weifeng Wu , et al. · 2020

Background and Objective: Electroencephalography (EEG) can be used to control machines with human intention, especially for paralyzed people in rehabilitation exercises or daily activities. Some effort was put into this but still no…

Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight Open

Hengyi Cai, Hongshen Chen, Yonghao Song, Cheng Zhang, Xiaofang Zhao , et al. · 2020

Current state-of-the-art neural dialogue models learn from human conversations following the data-driven paradigm. As such, a reliable training corpus is the crux of building a robust and well-behaved dialogue model. However, due to the op…

Learning from Easy to Complex: Adaptive Multi-Curricula Learning for Neural Dialogue Generation Open

Hengyi Cai, Hongshen Chen, Cheng Zhang, Yonghao Song, Xiaofang Zhao , et al. · 2020

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varie…

Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation Open