{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T14:21:35Z","timestamp":1765808495483,"version":"build-2065373602"},"reference-count":18,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"American University of Sharjah","award":["RA SEEIT 01\/2024"],"award-info":[{"award-number":["RA SEEIT 01\/2024"]}]},{"name":"German Jordanian University","award":["RA SEEIT 01\/2024"],"award-info":[{"award-number":["RA SEEIT 01\/2024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Visual imagery (VI) is a mental process in which an individual generates and sustains a mental image of an object without physically seeing it. Recent advancements in assistive technology have enabled the utilization of VI mental tasks as a control paradigm to design brain\u2013computer interfaces (BCIs) capable of generating numerous control signals. This, in turn, enables the design of control systems to assist individuals with locked-in syndrome in communicating and interacting with their environment. This paper presents an electroencephalogram (EEG) dataset captured from 30 healthy native Arabic-speaking subjects (12 females and 18 males; mean age: 20.8 years; age range: 19\u201323) while they visually imagined the 28 letters of the Arabic alphabet. Each subject conducted 10 trials per letter, resulting in 280 trials per participant and a total of 8400 trials for the entire dataset. The EEG signals were recorded using the EMOTIV Epoc X wireless EEG headset (San Francisco, CA, USA), which is equipped with 14 data electrodes and two reference electrodes arranged according to the 10\u201320 international system, with a sampling rate of 256 Hz. To the best of our knowledge, this is the first EEG dataset that focuses on visually imagined Arabic letters.<\/jats:p>","DOI":"10.3390\/data10060081","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T08:49:57Z","timestamp":1747903797000},"page":"81","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Electroencephalogram Dataset of Visually Imagined Arabic Alphabet for Brain\u2013Computer Interface Design and Evaluation"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1296-0231","authenticated-orcid":false,"given":"Rami","family":"Alazrai","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan"}]},{"given":"Khalid","family":"Naqi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0698-5508","authenticated-orcid":false,"given":"Alaa","family":"Elkouni","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}]},{"given":"Amr","family":"Hamza","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}]},{"given":"Farah","family":"Hammam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1956-6376","authenticated-orcid":false,"given":"Sahar","family":"Qaadan","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9399-5769","authenticated-orcid":false,"given":"Mohammad I.","family":"Daoud","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan"},{"name":"Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3030-848X","authenticated-orcid":false,"given":"Mostafa Z.","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 22110, Jordan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9685-4937","authenticated-orcid":false,"given":"Hasan","family":"Al-Nashash","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3957","DOI":"10.1097\/00001756-200012180-00011","article-title":"Cortical activation evoked by visual mental imagery as measured by fMRI","volume":"11","author":"Knauff","year":"2000","journal-title":"NeuroReport Rapid Commun. Neurosci. Res."},{"key":"ref_2","first-page":"3","article-title":"Training visual imagery: Improvements of metacognition, but not imagery strength","volume":"10","author":"Rademaker","year":"2012","journal-title":"Front. Psychol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1080\/2326263X.2023.2287719","article-title":"Feasibility of decoding visual information from EEG","volume":"11","author":"Wilson","year":"2024","journal-title":"Brain-Comput. Interfaces"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yao, L., Sheng, Q.Z., Kanhere, S.S., Gu, T., and Zhang, D. (2018, January 19\u201323). Converting your thoughts to texts: Enabling brain typing via deep feature learning of eeg signals. Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom), Athens, Greece.","DOI":"10.1109\/PERCOM.2018.8444575"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, J., Awais, M., Hossain, M.A., Yee, L., Haowei, M., Mehedi, I.M., and Iskanderani, A.I.M. (2023). Thoughts of brain EEG signal-to-text conversion using weighted feature fusion-based multiscale dilated adaptive DenseNet with attention mechanism. Biomed. Signal Process. Control, 86.","DOI":"10.1016\/j.bspc.2023.105120"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.neucom.2017.08.039","article-title":"Deep learning based on batch normalization for P300 signal detection","volume":"275","author":"Liu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"100992","DOI":"10.1016\/j.aei.2019.100992","article-title":"Classification of brain signal (EEG) induced by shape-analogous letter perception","volume":"42","author":"Bose","year":"2019","journal-title":"Adv. Eng. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"138955","DOI":"10.1109\/ACCESS.2020.3012918","article-title":"A time-frequency distribution-based approach for decoding visually imagined objects using EEG signals","volume":"8","author":"Alazrai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1007\/s11517-021-02368-0","article-title":"Imagined character recognition through EEG signals using deep convolutional neural network","volume":"59","author":"Ullah","year":"2021","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117417","DOI":"10.1016\/j.eswa.2022.117417","article-title":"A deep learning approach for decoding visually imagined digits and letters using time\u2013frequency\u2013spatial representation of EEG signals","volume":"203","author":"Alazrai","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ramirez-Quintana, J.A., Macias-Macias, J.M., Ramirez-Alonso, G., Chacon-Murguia, M.I., and Corral-Martinez, L.F. (2023). A novel deep capsule neural network for vowel imagery patterns from EEG signals. Biomed. Signal Process. Control., 81.","DOI":"10.1016\/j.bspc.2022.104500"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"20989","DOI":"10.1007\/s00521-023-08870-w","article-title":"A hybrid deep learning framework for automated visual image classification using EEG signals","volume":"35","author":"Ahmadieh","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_13","unstructured":"(2025, February 22). Emotiv Epoc X. Available online: https:\/\/www.emotiv.com\/products\/epoc-x."},{"key":"ref_14","unstructured":"(2025, February 22). EmotivPro Software. Available online: https:\/\/www.emotiv.com\/products\/emotivpro."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Herrero, G., De Clercq, W., Anwar, H., Kara, O., Egiazarian, K., Van Huffel, S., and Van Paesschen, W. (2006, January 7\u20139). Automatic removal of ocular artifacts in the EEG without an EOG reference channel. Proceedings of the 7th Nordic Signal Processing Symposium-NORSIG, Reykjavik, Iceland.","DOI":"10.1109\/NORSIG.2006.275210"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.neulet.2018.12.045","article-title":"EEG-based BCI system for decoding finger movements within the same hand","volume":"698","author":"Alazrai","year":"2019","journal-title":"Neurosci. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Alazrai, R., Homoud, R., Alwanni, H., and Daoud, M.I. (2018). EEG-based emotion recognition using quadratic time-frequency distribution. Sensors, 18.","DOI":"10.3390\/s18082739"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Alazrai, R., Alwanni, H., Baslan, Y., Alnuman, N., and Daoud, M.I. (2017). Eeg-based brain-computer interface for decoding motor imagery tasks within the same hand using choi-williams time-frequency distribution. Sensors, 17.","DOI":"10.3390\/s17091937"}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/6\/81\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:38:23Z","timestamp":1760031503000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/10\/6\/81"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,22]]},"references-count":18,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["data10060081"],"URL":"https:\/\/doi.org\/10.3390\/data10060081","relation":{},"ISSN":["2306-5729"],"issn-type":[{"type":"electronic","value":"2306-5729"}],"subject":[],"published":{"date-parts":[[2025,5,22]]}}}