World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
50
Citations
7914
World Ranking
5705
National Ranking
759

Overview

Xingyu Wang is affiliated with East China University of Science and Technology in China and specializes in the field of Engineering with a focus on Cognitive Neuroscience, Civil and Structural Engineering, Industrial and Manufacturing Engineering, Electrical and Electronic Engineering, and Molecular Biology as subfields. Their research spans multiple interdisciplinary areas relating to both biological systems and engineered infrastructures.

The scientist's main topics of work include:

  • EEG and Brain-Computer Interfaces
  • Advanced Memory and Neural Computing
  • Infrastructure Maintenance and Monitoring
  • Industrial Vision Systems and Defect Detection
  • Gaze Tracking and Assistive Technology
  • Computational Drug Discovery Methods
  • Protein Structure and Dynamics

Publication venues where Xingyu Wang has frequently contributed include:

  • Journal of Chemical Information and Modeling
  • Cognitive Neurodynamics
  • International Wound Journal
  • Journal of Neural Engineering
  • IEEE Transactions on Neural Systems and Rehabilitation Engineering

Recent scientific papers authored or co-authored by Xingyu Wang cover topics ranging from EEG classification improvements to molecular docking and infrastructure monitoring. Selected papers include:

  • Multi-View Multi-Scale Optimization of Feature Representation for EEG Classification Improvement, 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking, 2022, Journal of Chemical Information and Modeling
  • Novel channel selection model based on graph convolutional network for motor imagery, 2022, Cognitive Neurodynamics
  • Automatic Pavement Crack Detection in Multisource Fusion Images Using Similarity and Difference Features, 2023, IEEE Sensors Journal
  • Neural network-based prediction system for port throughput: A case study of Ningbo-Zhoushan Port, 2023, Research in Transportation Business & Management

Frequent coauthors collaborating with Xingyu Wang include:

  • Jing Jin
  • Andrzej Cichocki
  • Dongwei Qiu
  • Ren Xu
  • Yu Zhang

Best Publications

  • Temporally Constrained Sparse Group Spatial Patterns for Motor Imagery BCI

    Yu Zhang;Chang S. Nam;Guoxu Zhou;Jing Jin

  • L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI

    Yu Zhang;Guoxu Zhou;Jing Jin;Minjue Wang

  • Sparse Bayesian Classification of EEG for Brain–Computer Interface

    Yu Zhang;Guoxu Zhou;Jing Jin;Qibin Zhao

  • Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface

    Yu Zhang;Guoxu Zhou;Jing Jin;Xingyu Wang

  • Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces

    Yu Zhang;Yu Wang;Guoxu Zhou;Jing Jin

  • Internal Feature Selection Method of CSP Based on L1-Norm and Dempster–Shafer Theory

    Jing Jin;Ruocheng Xiao;Ian Daly;Yangyang Miao

  • Sparse Bayesian Learning for Obtaining Sparsity of EEG Frequency Bands Based Feature Vectors in Motor Imagery Classification.

    Yu Zhang;Yu Wang;Jing Jin;Xingyu Wang

  • Sparse Group Representation Model for Motor Imagery EEG Classification

    Yong Jiao;Yu Zhang;Xun Chen;Erwei Yin

  • An adaptive P300-based control system.

    Jing Jin;Brendan Zachary Allison;Eric Sellers;Clemens Brunner

  • Multiway canonical correlation analysis for frequency components recognition in SSVEP-Based BCIs

    Yu Zhang;Guoxu Zhou;Qibin Zhao;Akinari Onishi

  • A novel BCI based on ERP components sensitive to configural processing of human faces

    Yu Zhang;Qibin Zhao;Jing Jin;Xingyu Wang

  • Improved SFFS method for channel selection in motor imagery based BCI

    Zhaoyang Qiu;Jing Jin;Hak-Keung Lam;Yu Zhang

  • A P300 brain-computer interface based on a modification of the mismatch negativity paradigm.

    Jing Jin;Eric W Sellers;Sijie Zhou;Yu Zhang

  • A new hybrid BCI paradigm based on P300 and SSVEP.

    Minjue Wang;Ian Daly;Brendan Z. Allison;Jing Jin

  • Spatial-Temporal Discriminant Analysis for ERP-Based Brain-Computer Interface

    Yu Zhang;Guoxu Zhou;Qibin Zhao;Jing Jin

  • The changing face of P300 BCIs: a comparison of stimulus changes in a P300 BCI involving faces, emotion, and movement.

    Jing Jin;Brendan Z. Allison;Brendan Z. Allison;Tobias Kaufmann;Andrea Kübler

  • Discriminative Feature Extraction via Multivariate Linear Regression for SSVEP-Based BCI

    Haiqiang Wang;Yu Zhang;Nicholas R. Waytowich;Dean J. Krusienski

  • Towards correlation-based time window selection method for motor imagery BCIs.

    Jiankui Feng;Erwei Yin;Jing Jin;Rami Saab

  • Robust Similarity Measurement Based on a Novel Time Filter for SSVEPs Detection.

    Jing Jin;Zhiqiang Wang;Ren Xu;Chang Liu

  • A combined brain-computer interface based on P300 potentials and motion-onset visual evoked potentials.

    Jing Jin;Brendan Z. Allison;Xingyu Wang;Christa Neuper;Christa Neuper

  • AN ERP-BASED BCI USING AN ODDBALL PARADIGM WITH DIFFERENT FACES AND REDUCED ERRORS IN CRITICAL FUNCTIONS

    Jing Jin;Brendan Z Allison;Yu Zhang;Xingyu Wang

  • Bispectrum-Based Channel Selection for Motor Imagery Based Brain-Computer Interfacing

    Jing Jin;Chang Liu;Ian Daly;Yangyang Miao

Frequent Co-Authors

Jing Jin
Jing Jin East China University of Science and Technology
Andrzej Cichocki
Andrzej Cichocki Systems Research Institute
Ian Daly
Ian Daly University of Essex
Guoxu Zhou
Guoxu Zhou Guangdong University of Technology
Brendan Z. Allison
Brendan Z. Allison University of California, San Diego
Christa Neuper
Christa Neuper University of Graz
Eric W. Sellers
Eric W. Sellers East Tennessee State University
Dewen Hu
Dewen Hu National University of Defense Technology
Akio Ikeda
Akio Ikeda Kyoto University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Studying Computer Science in the USA opens doors to a variety of online degree options and dynamic career pathways. Many students are looking for programs that offer flexibility and affordability while providing strong job prospects after graduation.

For those interested in engineering fields with a solid technical foundation, you can explore the cheapest mechanical engineering degree online to gain skills in design and manufacturing. Students who prefer a theoretical approach might consider an online bachelor's degree in physics, which builds critical thinking for research and innovation roles.

As data becomes essential across industries, data science is an in-demand field. If affordability is a priority, you can find the cheapest data science degree to start your analytics journey. Alternatively, pursuing electrical engineering can lead to diverse career outcomes—discover more about online electrical engineering career outcomes and the opportunities available with this versatile degree.

Whatever pathway you choose, accredited online programs can provide the knowledge and flexibility you need to advance in tech-driven industries throughout the USA and beyond.

Best Scientists Citing Xingyu Wang

Trending Scientists