Developing an Optimal Brain Computer Interface Model using Functional Near Infrared Spectroscopy: A Review

Authors

  • Jia Heng Ong
  • Kim Seng Chia

DOI:

https://doi.org/10.4186/ej.2023.27.10.21 Full article

Abstract

Brain-Computer Interfaces (BCIs) are promising in advancing numerous applications. Although many functional near-infrared spectroscopy (fNIRS)-based BCIs have been studied, the development of an optimal fNIRS-based BCI model remains unclear. This study aims to review recent methodologies that used to optimize fNIRS-BCI models in four aspects i.e. signal acquisition, pre-processing, feature extraction, and machine learning. Besides, the differences, strengths, and limitations of various algorithms are discussed and highlighted. By comprehensively examining the recent trends and challenges in fNIRS BCI model development, this study proposes and discusses potential techniques in advancing fNIRS-based BCIs model development. The results suggest that future fNIRS-based BCI studies should focus on addressing cross-subject classification challenges and real-world fNIRS-BCI applications.

Keywords:

functional near infrared spectroscopy, brain computer interface, machine learning, review

Affiliations

  • Jia Heng Ong Universiti Tun Hussein Onn Malaysia; Merry Electronics (Singapore) Pte. Ltd., Singapore
  • Kim Seng Chia Tun Hussein Onn University of Malaysia

Corresponding author: Kim Seng Chia, kschia@uthm.edu.my

2082 1176

Author Biographies

  • Jia Heng Ong

    Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia

    Merry Electronics (Singapore) Pte. Ltd., Singapore

  • Kim Seng Chia

    Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia

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How to Cite

[1]
J. H. Ong and K. S. Chia, “Developing an Optimal Brain Computer Interface Model using Functional Near Infrared Spectroscopy: A Review”, Eng. J., vol. 27, no. 10, pp. 21–31, Oct. 2023, doi: 10.4186/ej.2023.27.10.21.

Citations

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Published

2023-10-31

Issue

Vol. 27 No. 10 (2023): Regular Issue

Section

Modern Engineering Technology

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