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Global research on artificial intelligence-enhanced human electroencephalogram analysis

  • S.I. : Healthcare Analytics
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Abstract

The application of artificial intelligence (AI) technologies in assisting human electroencephalogram (EEG) analysis has become an active scientific field. This study aims to present a comprehensive review of the research field of AI-enhanced human EEG analysis. Using bibliometrics and topic modeling, research articles concerning AI-enhanced human EEG analysis collected from the Web of Science database during the period 2009–2018 were analyzed. After examining 2053 research articles published around the world, it was found that the annual number of articles had significantly grown from 78 to 468, with the USA and China being the most influential and prolific. The results of the keyword analysis showed that “electroencephalogram,” “brain–computer interface,” “classification,” “support vector machine,” “electroencephalography,” and “signal” were the most frequently used. The results of topic modeling and evolution analyses highlighted several important issues, including epileptic seizure detection, brain–machine interface, EEG classification, mental disorders, emotion, and alcoholism and anesthesia. The findings suggest that such visualization and analysis of the research articles could provide a comprehensive overview of the field for communities of practice and inquiry worldwide.

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Notes

  1. https://cran.r-project.org/web/packages/wordcloud2/vignettes/wordcloud.html.

  2. https://cran.r-project.org/web/packages/Rwordseg/index.html.

  3. https://cran.r-project.org/web/packages/tmcn/index.html.

  4. https://www.vosviewer.com/.

  5. http://www.bbci.de/competition/.

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Acknowledgements

This work was supported by the HKIBS Research Seed Fund 2019/20 (190-009), the Research Seed Fund (102367), and LEO Dr David P. Chan Institute of Data Science of Lingnan University, Hong Kong.

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Authors and Affiliations

  1. Department of Mathematics and Information Technology, The Education University of Hong Kong, Tai Po, Hong Kong, China

    Xieling Chen

  2. School of Sciences, The University of Southern Queensland, Toowoomba, Australia

    Xiaohui Tao

  3. School of Science and Technology, The Open University of Hong Kong, Ho Man Tin, Hong Kong, China

    Fu Lee Wang

  4. Department of Computing and Decision Sciences, Lingnan University, Tuen Mun, Hong Kong, China

    Haoran Xie

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  1. Xieling Chen
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  2. Xiaohui Tao
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Correspondence to Fu Lee Wang.

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Chen, X., Tao, X., Wang, F.L. et al. Global research on artificial intelligence-enhanced human electroencephalogram analysis. Neural Comput & Applic 34, 11295–11333 (2022). https://doi.org/10.1007/s00521-020-05588-x

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