Electroencephalogram Dataset of Visually Imagined Arabic Alphabet for Brain–Computer Interface Design and Evaluation

Rami Alazrai (), Khalid Naqi, Alaa Elkouni, Amr Hamza, Farah Hammam, Sahar Qaadan, Mohammad I. Daoud, Mostafa Z. Ali and Hasan Al-Nashash
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Rami Alazrai: Department of Computer Engineering, School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan
Khalid Naqi: Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
Alaa Elkouni: Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
Amr Hamza: Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
Farah Hammam: Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates
Sahar Qaadan: Department of Mechatronics Engineering, School of Applied Technical Sciences, German Jordanian University, Amman 11180, Jordan
Mohammad I. Daoud: Department of Computer Engineering, School of Electrical Engineering and Information Technology, German Jordanian University, Amman 11180, Jordan
Mostafa Z. Ali: Department of Computer Information Systems, Jordan University of Science and Technology, Irbid 22110, Jordan
Hasan Al-Nashash: Department of Electrical Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates

Data, 2025, vol. 10, issue 6, 1-11

Abstract: 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–computer 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: 19–23) 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 10–20 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.

Keywords: electroencephalogram (EEG); visual imagery (VI); brain–computer interface (BCI); Emotiv; locked-in syndrome; neuroimaging; cognitive neuroscience; non-Latin alphabet (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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