Trends of Multimodal Neural Engineering Study: A Bibliometric Review

Abstract

Neural engineering, an emerging interdisciplinary subject, is aimed at using engineering techniques to investigate the function and manipulate the behavior of the nervous system. The development of technology along with the advancement in Science helps to apply increasing multimodal research into the field of neural engineering, which has promoted the development of neural engineering. In this paper, a bibliometric analysis of 808 articles in Web of Science from 2003 to 2019 was conducted to determine the current status and future trends of multimodal neural engineering study. This paper conducted a five-step bibliometric analysis based on the proposed multimodal neural engineering research framework (NE-MUL). The results showed that in the past 17 years, multimodal research not only made great contributions to the development of neural engineering, but also brought this field a series of new problems (multimodal fusion, recurrent multimodal learning, multimodal convolutional network, etc.) This paper generated a map of existing research findings with their relationship and provided future researchers with meaningful suggestions and assistance.

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Acknowledgements

We thank for all the experts who participated in this research and this research is supported by the Natural Science Foundation of China (Grant Number: 51878382).

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Correspondence to Pin-Chao Liao.

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Wang, J., Cheng, R. & Liao, PC. Trends of Multimodal Neural Engineering Study: A Bibliometric Review. Arch Computat Methods Eng (2021). https://doi.org/10.1007/s11831-021-09557-y

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Keywords

  • Neural engineering
  • Multimodal
  • Bibliometric
  • Brain