Editorial: Multimodal fusion technologies and applications in the context of neuroscience

Xiaomao Fan; Meiyu Qiu; Weidong Gao; Wenjun Ma; Yunpeng Cai; Hui Zhou; Dingguo Zhang; Raffaele Gravina; Jian Huang

doi:10.3389/fnins.2023.1213207

Editorial: Multimodal fusion technologies and applications in the context of neuroscience

Front Neurosci. 2023 May 16:17:1213207. doi: 10.3389/fnins.2023.1213207. eCollection 2023.

Authors

Xiaomao Fan¹, Meiyu Qiu¹, Weidong Gao², Wenjun Ma³, Yunpeng Cai⁴, Hui Zhou⁵, Dingguo Zhang⁶, Raffaele Gravina⁷, Jian Huang⁸

Affiliations

¹ College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
² School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
³ School of Computer Science, South China Normal University, Guangzhou, China.
⁴ Research Center for Biomedical Information Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
⁵ School of Automation, Nanjing University of Science and Technology, Nanjing, China.
⁶ Center for Biosensors, Bioelectronics and Biodevices, University of Bath, Bath, United Kingdom.
⁷ Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, Italy.
⁸ School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.

No abstract available

Keywords: biomedical signals; data mining and knowledge discovery; deep learning applications; multimodal data fusion; neuroscience.

Publication types

Editorial

Grants and funding

This work was partially supported by the Basic and Applied Research Project of Guangdong Province under Grant No. 2022B1515130009, the Special Subject on Agriculture and Social Development, Key Research and Development Plan in Guangzhou under Grant No. 2023B03J0172, and Key Projects of National Social Science Foundation of China under Grant No. 19ZDA041.