Global research on artificial intelligence-enhanced human electroencephalogram analysis

Xieling CHEN, Xiaohui TAO, Fu Lee WANG, Haoran XIE

Research output: Journal PublicationsJournal Article (refereed)peer-review

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.
Original languageEnglish
JournalNeural Computing and Applications
DOIs
Publication statusE-pub ahead of print - 7 Jan 2021

Bibliographical note

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.

Keywords

  • Artificial intelligence technologies
  • Bibliometrics
  • Electroencephalogram
  • Human brain
  • Research topics
  • Visualization

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