{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T19:34:35Z","timestamp":1784748875252,"version":"3.55.0"},"reference-count":35,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T00:00:00Z","timestamp":1672876800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Clin EEG Neurosci"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:p>Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with the device by tackling a sequence of motor imagery tasks. However, the extracting user-specific features and increasing the accuracy of the classifier remain as difficult tasks in MI-based BCI. In this work, we propose a new method using artificial neural network (ANN) enhancing the performance of the motor imagery classification. Feature extraction techniques, like time domain parameters, band power features, signal power features, and wavelet packet decomposition (WPD), are studied and compared. Four classification algorithms are implemented which are Quadratic Discriminant Analysis, k-Nearest Neighbors, Linear Discriminant Analysis, and proposed ANN architecture. We added Batch Normalization layers to the proposed ANN architecture to improve the learning time and accuracy of the neural network. These layers also alleviate the effect of weight initialization and the addition of a regularization effect on the network. Our proposed method using ANN architecture achieves 0.5545 of kappa and 58.42% of accuracy on the BCI Competition IV-2a dataset. Our results show that the modified ANN method, with frequency and spatial features extracted by WPD and Common Spatial Pattern, respectively, offers a better classification compared to other current methods.<\/jats:p>","DOI":"10.1177\/15500594221148285","type":"journal-article","created":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:36:40Z","timestamp":1672965400000},"page":"455-464","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":17,"title":["Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network"],"prefix":"10.1177","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2041-1301","authenticated-orcid":false,"given":"Amira","family":"Echtioui","sequence":"first","affiliation":[{"name":"ATMS Lab, Advanced Technologies for Medicine and Signals, ENIS, Sfax University, Sfax, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wassim","family":"Zouch","sequence":"additional","affiliation":[{"name":"King Abdulaziz University (KAU), Jeddah, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Ghorbel","sequence":"additional","affiliation":[{"name":"ATMS Lab, Advanced Technologies for Medicine and Signals, ENIS, Sfax University, Sfax, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chokri","family":"Mhiri","sequence":"additional","affiliation":[{"name":"Department of Neurology, Habib Bourguiba University Hospital, Sfax, Tunisia"},{"name":"Neuroscience Laboratory \u201cLR-12-SP-19\u201d, Faculty of Medicine, Sfax University, Sfax, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Habib","family":"Hamam","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Moncton University, New Brunswick, Moncton, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"key":"bibr1-15500594221148285","volume-title":"Reading EEGs: A Practical Approach","author":"Greenfield LJ","year":"2012"},{"key":"bibr2-15500594221148285","doi-asserted-by":"publisher","DOI":"10.1016\/S1388-2457(99)00141-8"},{"key":"bibr3-15500594221148285","doi-asserted-by":"publisher","DOI":"10.1016\/S1388-2457(98)00038-8"},{"key":"bibr4-15500594221148285","author":"Grosse-Wentrup M","journal-title":"IEEE Trans Biomed Eng."},{"key":"bibr5-15500594221148285","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2012.00039"},{"key":"bibr6-15500594221148285","first-page":"4227","author":"Tonin L","year":"2011","journal-title":"Brain-controlled telepresence robot by motor-disabled people. 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EEG signals processing based on fractal dimension features and classified by neural network and support vector machine in motor imagery for a BCI. In VI Latin American congress on biomedical engineering CLAIB 2014, Paran\u00e1, Argentina 29, 30 & 31 October 2014, A. Braidot and A. Hadad, eds., vol. 49 of IFMBE Proceedings. Springer International Publishing, 2015: 615-618.","DOI":"10.1007\/978-3-319-13117-7_157"},{"key":"bibr23-15500594221148285","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0129435"},{"key":"bibr24-15500594221148285","doi-asserted-by":"crossref","unstructured":"Abbas W, Ahmad Khan N. FBCSP-based multi-class motor imagery classification using BP and TDP features. 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