{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T21:13:35Z","timestamp":1762377215505,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:00:00Z","timestamp":1659657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Gachon University research fund","award":["GCU- 202110180001"],"award-info":[{"award-number":["GCU- 202110180001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The brain\u2013computer 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% \u00b1 0.75 for C4D2a_4C and 88.21% \u00b1 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.<\/jats:p>","DOI":"10.3390\/s22155860","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"5860","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Novel Quick-Response Eigenface Analysis Scheme for Brain\u2013Computer Interfaces"],"prefix":"10.3390","volume":"22","author":[{"given":"Hojong","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Gachon University, 1342 Seongnam-daero, Seongnam 13306, Korea"}]},{"given":"Junghun","family":"Park","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Kumoh National Institute of Technology, 61 Daehak-ro, Gumi 39177, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2688-302X","authenticated-orcid":false,"given":"Yeon-Mo","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Kumoh National Institute of Technology, 61 Daehak-ro, Gumi 39177, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,5]]},"reference":[{"key":"ref_1","unstructured":"(2022, April 22). 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