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. 2022 Apr 26;22(9):3331.
doi: 10.3390/s22093331.

Past, Present, and Future of EEG-Based BCI Applications

Kaido Värbu  1 , Naveed Muhammad  1 , Yar Muhammad  2
Affiliations

Past, Present, and Future of EEG-Based BCI Applications

Kaido Värbu et al. Sensors (Basel). .

Abstract

An electroencephalography (EEG)-based brain-computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.

Keywords: brain–computer interface (BCI); electroencephalography (EEG); rehabilitation; systematic literature review.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the review; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
The flow of information during selection of studies according to the PRISMA model comprising Identification, Screening, Eligibility and Inclusion phase.
Figure 2
Figure 2
The number of publications (conference proceedings and articles) per year from the period from January 2009 to October 2019.
Figure 3
Figure 3
Number of publications per region/continent. The highest number of publications on the topic has been published in Asia.
Figure 4
Figure 4
Experimental publications per year covering the period from January 2009 to October 2019. The publications have been further divided into medical or non-medical domain or both in case both domains have been covered.
Figure 5
Figure 5
Distribution of publications per domain. The figure illustrates the distribution of the publications among medical and non-medical domain and volume of the publications covering both domains.
Figure 6
Figure 6
Distribution of publications in medical domain. In the medical domain, the largest field is assistive followed by monitoring, rehabilitation, assessment and other.
Figure 7
Figure 7
Distribution of publications in non-medical domain. In the non-medical domain, the largest field is monitoring followed by control machine, entertainment and other smaller fields.
Figure 8
Figure 8
Distribution of publications covering both domains. The publications covering both domains have been focused mainly on assistive, but other fields such as framework and control machine also make up a significant proportion of the publications.
Figure 9
Figure 9
EEG devices used in the publications ordered by the number and percentage of publications where the device has been used.
Figure 10
Figure 10
Number of EEG channels used in publications. In the majority of the publications, up to 40 EEG channels have been used.
Figure 11
Figure 11
Techniques used in the publications. The techniques have been represented according to the number of publications where the techniques have been used and prevalence among medical, non-medical or both domains.
Figure 12
Figure 12
Feature extraction used in the publications ordered by the number and percentage of publications where the extraction method has been used.
Figure 13
Figure 13
Classification method used in the publications ordered by the number and percentage of publications where the classification method has been used.
See this image and copyright information in PMC

References

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