Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2021 Aug 26;21(17):5746.
doi: 10.3390/s21175746.

Brain-Computer Interface: Advancement and Challenges

M F Mridha  1 , Sujoy Chandra Das  1 , Muhammad Mohsin Kabir  1 , Aklima Akter Lima  1 , Md Rashedul Islam  2 , Yutaka Watanobe  3
Affiliations
Review

Brain-Computer Interface: Advancement and Challenges

M F Mridha et al. Sensors (Basel). .

Abstract

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

Keywords: biomedical sensors; brain-computer interface; signal processing; systematic review.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The PRISMA process that is followed in this article.
Figure 2
Figure 2
Basic architecture of a BCI system.
Figure 3
Figure 3
The classification/taxonomy of the BCI system.
Figure 4
Figure 4
The basic architecture of BCI control signals.
Figure 5
Figure 5
The basic structure of CSP [286].
Figure 6
Figure 6
Classification of commonly used classifiers in BCI.
See this image and copyright information in PMC

References

    1. Berger H. Über das elektroenkephalogramm des menschen. Archiv. Psychiatr. 1929;87:527–570. doi: 10.1007/BF01797193. - DOI
    1. Lindsley D.B. Psychological phenomena and the electroencephalogram. Electroencephalogr. Clin. Neurophysiol. 1952;4:443–456. doi: 10.1016/0013-4694(52)90075-8. - DOI - PubMed
    1. Vidal J.J. Toward direct brain-computer communication. Annu. Rev. Biophys. Bioeng. 1973;2:157–180. doi: 10.1146/annurev.bb.02.060173.001105. - DOI - PubMed
    1. Zeng F.G., Rebscher S., Harrison W., Sun X., Feng H. Cochlear implants: System design, integration, and evaluation. IEEE Rev. Biomed. Eng. 2008;1:115–142. doi: 10.1109/RBME.2008.2008250. - DOI - PMC - PubMed
    1. Nicolas-Alonso L.F., Gomez-Gil J. Brain computer interfaces: A review. Sensors. 2012;12:1211–1279. doi: 10.3390/s120201211. - DOI - PMC - PubMed

LinkOut - more resources