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This technique, in conjunction with the proposed deep learning framework, allows for a combination of similar subject pairs to take advantage of one another and boost the classification performance of MI-BCIs. The proposed framework features a multi-domain feature extractor based on common spatial patterns with a sliding window and a parallel two-branch convolutional neural network. The performance of the proposed methodology has been evaluated on the multi-class BCI Competition IV Dataset 2a through repeated 10-fold cross-validation. Experimental results indicated that the implementation of the ACSSR method (80.47%) in the proposed framework has led to a considerable improvement in the classification performance compared to the classification without data augmentation (77.63%), and other fundamental data augmentation techniques used in the literature. The study contributes to the advancements for the development of effective MI-BCIs by showcasing the ability of the ACSSR method to address the challenges in motor imagery signal classification tasks.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad200c","type":"journal-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T22:23:30Z","timestamp":1705616610000},"page":"015021","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4665-1952","authenticated-orcid":true,"given":"Enes","family":"Ozelbas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0150-5476","authenticated-orcid":true,"given":"Emine Elif","family":"T\u00fclay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7432-0272","authenticated-orcid":false,"given":"Serhat","family":"Ozekes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2024,2,2]]},"reference":[{"key":"mlstad200cbib1","doi-asserted-by":"publisher","first-page":"109612","DOI":"10.1109\/access.2019.2934018","article-title":"A deep learning framework for decoding motor imagery tasks of the same hand using EEG signals","volume":"7","author":"Alazrai","year":"2019","journal-title":"IEEE Access"},{"key":"mlstad200cbib2","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1109\/TBME.2019.2921198","volume":"67","author":"Foong","year":"2020","journal-title":"IEEE Trans. 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