Transforming EEG Signals into Images for Motor Imagery Classification Using a YOLO11-Based Model

Aya A. Abdullah; Khamis A. Zidan; A. S. Albahri

doi:10.48084/etasr.18199

Authors

Aya A. Abdullah Department of Computer Engineering, College of Engineering, Al-Iraqia University, Baghdad, Iraq
Khamis A. Zidan Vice Rector for Scientific Affairs, Al-Iraqia University, Baghdad, Iraq
A. S. Albahri Technical College, Imam Jaafar Al-Sadiq University, Baghdad, Iraq

Volume: 16 | Issue: 3 | Pages: 36753-36761 | June 2026 | https://doi.org/10.48084/etasr.18199

Received: 15 February 2026 | Revised: 7 April 2026 | Accepted: 17 April 2026 | Online: 6 June 2026

Corresponding author: Aya A. Abdullah

Abstract

A Brain–Computer Interface (BCI) enables direct communication between an individual’s brain and external hardware. Potential application areas of BCI systems include assistive technology, smart home environments, healthcare, and many other domains. Electroencephalography (EEG)-based Motor Imagery (MI) signals have been widely used in such applications; however, accurate classification of MI-EEG data remains challenging because traditional EEG classification methods often cannot effectively capture the complex spatiotemporal characteristics of raw EEG signals. To address this limitation, this study proposes a structured EEG-to-RGB representation combined with a YOLO11-L-cls-based classification framework for motor imagery EEG classification. The proposed approach was evaluated using two datasets, namely BCI Competition IV Dataset 2b (BCICIV2b) and Dataset III, under subject-independent evaluation settings. The proposed method achieved a classification accuracy of approximately 99% on BCICIV2b and 97.5% on Dataset III. In addition, the performance of the proposed method was compared with traditional machine learning techniques, including Random Forest (RF) and Linear Discriminant Analysis (LDA), as well as deep learning models such as Convolutional Neural Networks (CNNs). Extensive experimental analyses demonstrated the effectiveness and robustness of the proposed approach for developing MI-EEG-based BCI systems.

Keywords:

Motor Imagery (MI), Brain–Computer Interface (BCI), time–frequency image representation, YOLO11-L-cls-based classification

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