Political Affiliation: Member of the Communist Party of China (CPC)

Academic Title: Professor, Doctoral Supervisor

Position: Director of the Data Science Research Center

Affiliation: Institute of Computer Application Technology

Office Address: Room 513, Feiyun Building

Educational Background

Sep. 1998 – Jul. 2002, School of Computer Science, Lanzhou University, Bachelor of Engineering

Sep. 2002 – Jul. 2005, School of Computer Science and Technology, Lanzhou University, Master of Engineering

Sep. 2009 – Jun. 2015, School of Computer Science and Technology, Lanzhou University, Doctor of Engineering

Professional Experience

Apr. 2007 – Apr. 2013, Lecturer, School of Information Science and Engineering, Lanzhou University

May 2013 – Apr. 2018, Associate Professor, School of Information Science and Engineering, Lanzhou University

May 2018 – Present, Professor, School of Information Science and Engineering, Lanzhou University

Teaching Responsibilities

Undergraduate courses: Web Database Technology, C Language Programming, Assembly Language, etc.

Graduate Supervision

Since 2014, he has supervised more than 30 doctoral and master’s students.

Research Interests

His research fields include data mining, biomedical data processing, and big data. Current research focuses on the analysis and processing of multimodal data such as electroencephalogram (EEG) signals and eye movement signals of people with affective disorders.

Majors for Student Recruitment Computer Science and Technology, Computer Application Technology, and other related majors.

Research Projects and Achievements

Research projects (programs) and talent program projects presided over or participated in in the past five years:

General Program of the National Natural Science Foundation of China (Grant No. 62372216): Research on Key Technologies for Depression Recognition Based on EEG Signals

Key Project of the “Brain Science and Brain-Inspired Intelligence Research” under the Science and Technology Innovation 2030 Major Program (Grant No. 2022ZD0208500), Ministry of Science and Technology of China: Novel Non-invasive Brain-Computer Interface: Theory, Technology and Application Demonstration

Key Program of the Gansu Provincial Natural Science Foundation (Grant No. 22JR5RA410), Department of Science and Technology of Gansu Province: Research on Brain Network Model of Depressive Patients Based on EEG Source Localization

Key Program of the National Natural Science Foundation of China (Grant No. 61632014): Research on Computational Models of Neural Mechanisms of Attention

National Basic Research Program of China (973 Program) (Grant No. 2014CB744600), Ministry of Science and Technology of China: Research on Pre-warning Theory of Potential Depression Risk and Key Technologies of Biosensing Based on Multimodal Biological and Psychological Information Publications

Recent papers:

[1]. Huayu Chen, Li Junxiang, Huanhuan He, et al. Toward the Construction of Affective Brain-Computer Interface: A Systematic Review[J]. ACM COMPUTING SURVEYS, 2025, 57(6):156.
[2]. Qu Shanshan, Dixin Wang, Chang Yan, et al. Depression recognition using high-order generalized multilayer brain functional network fused with EEG multi-domain information[J]. INFORMATION FUSION, 2025, 114:102723.
[3]. Huayu Chen, Li Junxiang, Huanhuan He, et al. VAE-CapsNet: A common emotion information extractor for cross-subject emotion recognition[J]. KNOWLEDGE-BASED SYSTEMS, 2025, 311:113018.
[4]. Luo Gang, Han Yutong, Weichu Xie, et al. GCD-JFSE: Graph-based class-domain knowledge joint feature selection and ensemble learning for EEG-based emotion recognition[J]. KNOWLEDGE-BASED SYSTEMS, 2025, 309:112770.
[5]. Huang Jun, Xin Liu, Li Yizhou, et al. Composite multi-span amplitude-aware ordinal transition network: Fine-grained representation and quantification of complex system time series[J]. CHAOS SOLITONS&FRACTALS, 2025, 197:116487.

[6]. Luo Gang, Shuting Sun, Chang Yan, et al. IMGWOFS: A Feature Selector with Trade-off between Conflict Objectives for EEG-based Emotion Recognition[J]. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2024:1-13.
[7]. J. Zhu et al., Mutual Information Based Fusion Model (MIBFM): Mild Depression Recognition Using EEG and Pupil Area Signals, IEEE Transactions on Affective Computing, vol. 14, no. 3, pp. 2102-2115, 1 July-Sept. 2023
[8]. J. Li, Y. Hao, W. Zhang, X. Li and B. Hu, “Effective Connectivity Based EEG Revealing the Inhibitory Deficits for Distracting Stimuli in Major Depression Disorders” in IEEE Transactions on Affective Computing, vol. 14, no. 01, pp. 694-705, 2023.
[9]. J. Li, et al.,”Altered Brain Dynamics and Their Ability for Major Depression Detection Using EEG Microstates Analysis” in IEEE Transactions on Affective Computing, vol. 14, no. 03, pp. 2116-2126, 2023.
[10]. H. Chen, et al.,”Personal-Zscore: Eliminating Individual Difference for EEG-Based Cross-Subject Emotion Recognition” in IEEE Transactions on Affective Computing, vol. 14, no. 03, pp. 2077-2088, 2023.