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

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-08, Vol.22 (15), p.5860
• Main Authors: Choi, Hojong, Park, Junghun, Yang, Yeon-Mo
• Format: Article
• Language: English
• Subjects: Access control; Algorithms; Classification; Communication; Competition; Datasets; Deep learning; Discriminant analysis; Domains; eigenface analysis; Electroencephalography; Human-computer interface; Image classification; image data augmentation; Interfaces; Methods; motor imagery classification; quick response neuro images; Signal processing; standardized and sharable quick response eigenfaces; Support vector machines; Wavelet transforms
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22155860

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.