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Article

A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces

1
Department of Electronic Engineering, Gachon University, 1342 Seongnam-daero, Seongnam 13306, Korea
2
School of Electronic Engineering, Kumoh National Institute of Technology, 61 Daehak-ro, Gumi 39177, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Sukho Lee and Dae-Ki Kang
Sensors 2022, 22(15), 5860; https://doi.org/10.3390/s22155860 (registering DOI)
Received: 1 July 2022 / Revised: 29 July 2022 / Accepted: 1 August 2022 / Published: 5 August 2022
(This article belongs to the Special Issue Deep Learning Technology and Image Sensing)
The brain–computer interface (BCI) is used to understand brain activities and external bodies with the help of the motor imagery (MI). As of today, the classification results for EEG 4 class BCI competition dataset have been improved to provide better classification accuracy of the brain computer interface systems (BCIs). Based on this observation, a novel quick-response eigenface analysis (QR-EFA) scheme for motor imagery is proposed to improve the classification accuracy for BCIs. Thus, we considered BCI signals in standardized and sharable quick response (QR) image domain; then, we systematically combined EFA and a convolution neural network (CNN) to classify the neuro images. To overcome a non-stationary BCI dataset available and non-ergodic characteristics, we utilized an effective neuro data augmentation in the training phase. For the ultimate improvements in classification performance, QR-EFA maximizes the similarities existing in the domain-, trial-, and subject-wise directions. To validate and verify the proposed scheme, we performed an experiment on the BCI dataset. Specifically, the scheme is intended to provide a higher classification output in classification accuracy performance for the BCI competition 4 dataset 2a (C4D2a_4C) and BCI competition 3 dataset 3a (C3D3a_4C). The experimental results confirm that the newly proposed QR-EFA method outperforms the previous the published results, specifically from 85.4% to 97.87% ± 0.75 for C4D2a_4C and 88.21% ± 6.02 for C3D3a_4C. Therefore, the proposed QR-EFA could be a highly reliable and constructive framework for one of the MI classification solutions for BCI applications. View Full-Text
Keywords: motor imagery classification; eigenface analysis; quick response neuro images; image data augmentation; standardized and sharable quick response eigenfaces motor imagery classification; eigenface analysis; quick response neuro images; image data augmentation; standardized and sharable quick response eigenfaces
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MDPI and ACS Style

Choi, H.; Park, J.; Yang, Y.-M. A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces. Sensors 2022, 22, 5860. https://doi.org/10.3390/s22155860

AMA Style

Choi H, Park J, Yang Y-M. A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces. Sensors. 2022; 22(15):5860. https://doi.org/10.3390/s22155860

Chicago/Turabian Style

Choi, Hojong, Junghun Park, and Yeon-Mo Yang. 2022. "A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces" Sensors 22, no. 15: 5860. https://doi.org/10.3390/s22155860

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