{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T01:14:18Z","timestamp":1791422058713,"version":"4.3.3"},"reference-count":48,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In brain\u2013computer interface (BCI) systems, motor imagery electroencephalography (MI-EEG) signals are commonly used to detect participant intent. Many factors, including low signal-to-noise ratios and few high-quality samples, make MI classification difficult. In order for BCI systems to function, MI-EEG signals must be studied. In pattern recognition and other fields, deep learning approaches have recently been successfully applied. In contrast, few effective deep learning algorithms have been applied to BCI systems, especially MI-based systems. In this paper, we address these problems from two aspects based on the characteristics of EEG signals: first, we proposed a combined time\u2013frequency domain data enhancement method. This method guarantees that the size of the training data is effectively increased while maintaining the intrinsic composition of the data. Second, our design consists of a parallel CNN that takes both raw EEG images and images transformed through continuous wavelet transform (CWT) as inputs. We conducted classification experiments on a public data set to verify the effectiveness of the algorithm. According to experimental results based on the BCI Competition IV Dataset2a, the average classification accuracy is 97.61%. A comparison of the proposed algorithm with other algorithms shows that it performs better in classification. The algorithm can be used to improve the classification performance of MI-based BCIs and BCI systems created for people with disabilities.<\/jats:p>","DOI":"10.3390\/s23041932","type":"journal-article","created":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T02:55:54Z","timestamp":1675911354000},"page":"1932","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Classification of Motor Imagery EEG Signals Based on Data Augmentation and Convolutional Neural Networks"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0027-8569","authenticated-orcid":false,"given":"Yu","family":"Xie","sequence":"first","affiliation":[{"name":"Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2353-6759","authenticated-orcid":false,"given":"Stefan","family":"Oniga","sequence":"additional","affiliation":[{"name":"Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary"},{"name":"North University Center of Baia Mare, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/S0278-2626(03)00036-8","article-title":"Learning to control brain activity: A review of the production and control of EEG components for driving brain\u2013computer interface (BCI) systems","volume":"51","author":"Curran","year":"2003","journal-title":"Brain Cogn."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/1743-0003-5-8","article-title":"Efficacy of motor imagery in post-stroke rehabilitation: A systematic review","volume":"5","author":"Schuster","year":"2008","journal-title":"J. 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