EEG based brain-computer interface control applications: A comprehensive review

Authors

  • Kemal Polat Department of Electrical and Electronics Engineering, Faculty of Engineering, Bolu Abant Izzet Baysal University, Bolu, Turkey https://orcid.org/0000-0003-1840-9958
  • Abdullah Bilal Aygun Department of Biomedical Engineering, Faculty of Engineering, Karabuk University, Karabuk, Turkey https://orcid.org/0000-0002-0162-4419
  • Ahmet Resit Kavsaoglu Department of Biomedical Engineering, Faculty of Engineering, Karabuk University, Karabuk, Turkey https://orcid.org/0000-0002-4380-9075

Keywords:

Brain computer interfaces, EEG, machine learning, classification, mental control signals

Abstract

Brain computer interfaces (BCI) is a tool that can make user requests to computerized systems by directly processing brain signals. In order to perform the procedures to be performed, brain signals must be classified. For this purpose, many classification algorithms have been tried with machine learning. The purpose of this study is to talk about both the type of brain signals used in the brain computer interface and the machine learning techniques used in the classification of these signals. In addition, summary information about the classification methods used in brain computer interface control applications in recent years are given in a table.

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Published

2021-05-31

How to Cite

Polat, K., Bilal Aygun, A. ., & Kavsaoglu, A. R. . (2021). EEG based brain-computer interface control applications: A comprehensive review. Journal of Bionic Memory, 1(1), 20–33. Retrieved from http://jbionicmemory.com/index.php/jbm/article/view/4