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Review
. 2023 Jun 28;23(13):6001.
doi: 10.3390/s23136001.

State-of-the-Art on Brain-Computer Interface Technology

Janis Peksa  1   2 , Dmytro Mamchur  1   3
Affiliations
Review

State-of-the-Art on Brain-Computer Interface Technology

Janis Peksa et al. Sensors (Basel). .

Abstract

This paper provides a comprehensive overview of the state-of-the-art in brain-computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.

Keywords: EEG; artificial intelligence; brain–computer interface; classification; signal processing.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
BCI operation principle.
Figure 2
Figure 2
BCI sensor mounting types: invasive (IM), semi-invasive (ECoG), and non-invasive (MEG, EEG, fNIRS).
Figure 3
Figure 3
An example of the frequency spectrum of a human brain electroencephalogram.
Figure 4
Figure 4
The CNN architecture in BCI analysis.
See this image and copyright information in PMC

References

    1. Mridha M.F., Das S.C., Kabir M.M., Lima A.A., Islam M.R., Watanobe Y. Brain-Computer Interface: Advancement and Challenges. Sensors. 2021;21:5746. doi: 10.3390/s21175746. - DOI - PMC - PubMed
    1. Mora-Cortes A., Manyakov N.V., Chumerin N., Van Hulle M.M. Language Model Applications to Spelling with Brain-Computer Interfaces. Sensors. 2014;14:5967–5993. doi: 10.3390/s140405967. - DOI - PMC - PubMed
    1. Belwafi K., Gannouni S., Aboalsamh H. Embedded Brain Computer Interface: State-of-the-Art in Research. Sensors. 2021;21:4293. doi: 10.3390/s21134293. - DOI - PMC - PubMed
    1. Värbu K., Muhammad N., Muhammad Y. Past, Present, and Future of EEG-Based BCI Applications. Sensors. 2022;22:3331. doi: 10.3390/s22093331. - DOI - PMC - PubMed
    1. Siribunyaphat N., Punsawad Y. Brain–Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control. Sensors. 2023;23:2069. doi: 10.3390/s23042069. - DOI - PMC - PubMed

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