Multimodal Fusion Technologies and Applications in the Context of Neuroscience

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Editorial

16 May 2023

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

Xiaomao Fan

,

Meiyu Qiu

,

Weidong Gao

,

Wenjun Ma

, 4 more and

Jian Huang

1,833 views

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Editors

7

Xiaomao Fan

Shenzhen Technology University

Yunpeng Cai

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS)

Weidong Gao

Beijing University of Posts and Telecommunications (BUPT)

Raffaele Gravina

University of Calabria

Hui Zhou

Nanjing University of Science and Technology

Dingguo Zhang

University of Bath

Jian Huang

Huazhong University of Science and Technology

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In recent years, sensor and information technologies have greatly boosted the wearable/portal/medical devices development. A large number of multimodal biomedical signals such as electroencephalography (EEG), electrocardiography (ECG), electrooculogram (EOG), and electromyography (EMG), have been recorded for rehabilitation analysis, mental disorders evaluation, emotion recognition, cardiovascular disease diagnosis, etc. In these research fields, most researchers often use single-modal biomedical signals to build the corresponding analysis models. However, many clinical practice tasks, such as disease diagnosis, arrhythmias detection, and sleep condition monitoring, require multimodal biomedical signals together to make correct diagnoses, decisions, identifications, and predictions. It is noted that learning from multimodal biomedical signals can offer the possibility of capturing corresponded information and gaining an in-depth understanding of the relationship among different modalities. In this research topic, the collection of articles is intended to cover recent original research works to advance the fundamental theory and technologies in biomedical signal processing methods, multimodal fusion algorithms, and biomedical signal-based clinical applications. This Research Topic welcomes submissions of the following topics:

1. Biomedical processing methodology

2. Machine learning/deep learning based multimodal fusion algorithms

3. Self- and semi-supervised multimodal fusion algorithms

4. Biomedical signal based clinical applications

5. Fusion performance evaluation

6. Fusion of multimode signals for human-machine interface

7. Applications of multimodal fusion in biomedical signals

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Editors

Xiaomao Fan

Shenzhen Technology University

Yunpeng Cai

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS)

Weidong Gao

Beijing University of Posts and Telecommunications (BUPT)

Raffaele Gravina

University of Calabria

Hui Zhou

Nanjing University of Science and Technology

Dingguo Zhang

University of Bath

Jian Huang

Huazhong University of Science and Technology

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58,206 Total views

40,328 Article views

15,896 Article downloads

1,982 Topic views

Published In

Frontiers in Neuroscience

Neural Technology

Frontiers in Sensors

Biosensors

Frontiers in Computational Neuroscience

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