A Novel Quick-Response Eigenface Analysis Scheme for Brain-Computer Interfaces
- Authors
- Choi, Hojong; Park, Junghun; Yang, Yeon-Mo
- Issue Date
- Aug-2022
- Publisher
- MDPI
- Keywords
- motor imagery classification; eigenface analysis; quick response neuro images; image data augmentation; standardized and sharable quick response eigenfaces
- Citation
- Sensors, v.22, no.15
- Journal Title
- Sensors
- Volume
- 22
- Number
- 15
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/85429
- DOI
- 10.3390/s22155860
- ISSN
- 1424-8220
- Abstract
- 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.
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