Abstract:
This study presents a quantitative analysis of publications on the classification of motor imagery EEG signals using deep learning methods through bibliometric analysis. The aim of the study is to enhance understanding of accumulated knowledge in the field and guide future research. To achieve this, data related to the topic were filtered from the Web of Science database and analyzed using VosViewer. A notable finding is the observed increase in research on "Deep learning techniques for classifying motor imagery EEG signals" since 2013. China emerges as the leading country in both publication and citation impact. Furthermore, prominent researchers, influential publications, effective categories, and institutions were identified using time and heat maps. Finally, the study concludes that while articles dominate the research in this area, conference papers and review articles also hold significant positions.

Keywords: EEG, Deep Learning, Motor Imagery, Bibliometric Analysis

Download Article File