Jing Zhu

Orcid: 0000-0003-3047-287X

Affiliations:

Lanzhou University, Gansu Provincial Key Laboratory of Wearable Computing, China

According to our database¹, Jing Zhu authored at least 20 papers between 2016 and 2023.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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Bibliography

2023

ChatGPT for Computational Social Systems: From Conversational Applications to Human-Oriented Operating Systems.

[BibT_eX]

[DOI]

IEEE Trans. Comput. Soc. Syst., April, 2023

Mutual Information Based Fusion Model (MIBFM): Mild Depression Recognition Using EEG and Pupil Area Signals.

[BibT_eX]

[DOI]

IEEE Trans. Affect. Comput., 2023

2022

Content-based multiple evidence fusion on EEG and eye movements for mild depression recognition.

[BibT_eX]

[DOI]

Comput. Methods Programs Biomed., 2022

EEG based depression recognition using improved graph convolutional neural network.

[BibT_eX]

[DOI]

Comput. Biol. Medicine, 2022

Hybrid fusion model based on DBN and secondary classifier: Multimodal mild depression recognition using EEG and eye movement.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

2021

Federated Control: Toward Information Security and Rights Protection.

[BibT_eX]

[DOI]

IEEE Trans. Comput. Soc. Syst., 2021

2020

MODMA dataset: a Multi-model Open Dataset for Mental-disorder Analysis.

[BibT_eX]

[DOI]

CoRR, 2020

Attention Bias in Emotional Conflict in Major Depression Disorder: An Eye Tracking Study.

[BibT_eX]

[DOI]

Proceedings of the 22nd IEEE International Conference on E-health Networking, 2020

A functional network study of patients with mild depression based on source location.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020

EEG-based mild depression recognition using multi-kernel convolutional and spatial-temporal Feature.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020

2019

EEG-based mild depression recognition using convolutional neural network.

[BibT_eX]

[DOI]

Medical Biol. Eng. Comput., 2019

Depression recognition using machine learning methods with different feature generation strategies.

[BibT_eX]

[DOI]

Artif. Intell. Medicine, 2019

Multimodal Mild Depression Recognition Based on EEG-EM Synchronization Acquisition Network.

[BibT_eX]

[DOI]

IEEE Access, 2019

Multivariate Pattern Analysis of EEG-Based Functional Connectivity: A Study on the Identification of Depression.

[BibT_eX]

[DOI]

IEEE Access, 2019

Toward Depression Recognition Using EEG and Eye Tracking: An Ensemble Classification Model CBEM.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019

2018

Attentional bias in MDD: ERP components analysis and classification using a dot-probe task.

[BibT_eX]

[DOI]

Comput. Methods Programs Biomed., 2018

A Study on Depression Detection Using Eye Tracking.

[BibT_eX]

[DOI]

Proceedings of the Human Centered Computing - 4th International Conference, 2018

Resting State EEG Based Depression Recognition Research Using Deep Learning Method.

[BibT_eX]

[DOI]

Proceedings of the Brain Informatics - International Conference, 2018

2017

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering.

[BibT_eX]

[DOI]

Complex., 2017

2016

Exploring User Mobile Shopping Activities Based on Characteristic of Eye-Tracking.

[BibT_eX]

[DOI]

Proceedings of the Human Centered Computing - Second International Conference, 2016

Jing Zhu

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