An Analytical Review of Brainwave Analysis for Mind-Controlled Drones: Utilizing Hybrid Neural Networks and Motor Imagery

Authors

Ather Salah Hadi Al-isawi, Department of Software Engineering and Information System, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang 43400-Selangor, MalaysiaFollow
Sufri Muhammad, Department of Software Engineering and Information System, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang 43400-Selangor, MalaysiaFollow
Novia Indriaty Admodisastro, Department of Software Engineering and Information System, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang 43400-Selangor, MalaysiaFollow
Siti Anom Ahmad, Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400-Selangor, MalaysiaFollow
Raihani Mohamed, Department of Computer Science, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang 43400-Selangor, MalaysiaFollow

Abstract

Brain-computer interfaces (BCIs) provide direct drone control by interpreting motor imagery brain impulses. This review paper critically assesses the progress made in using hybrid neural networks and motor imagery to operate mind-controlled drones and addresses the challenges of encoding and decoding complex brain signals for precise and reliable drone control. In this review paper, the primary objective is to analyze and synthesize the latest brainwave analysis technologies, focusing on hybrid neural networks and motor imagery tasks for drone control via brain-computer interfaces. This study aims to provide insights into the effectiveness of these technologies in enhancing the accuracy and reliability of mind-controlled drone systems. The method of conducting the review process is PRSM. This review paper shows that signal analysis reliability dictates a model that accurately decodes motor imagery-related brain signals and converts them into drone control commands. The integration of machine learning algorithms with neurophysiological principles has demonstrated significant improvements in the performance of mind-controlled drone systems. In conclusion, the synergistic utilization of hybrid neural networks and motor imagery techniques holds great potential for advancing the field of mind-controlled drones. Further research and optimization of signal processing algorithms are essential for enhancing the speed, precision, and robustness of mind-controlled drone systems, ultimately leading to transformative advancements in human-machine interactions across various domains. This review paper lays the groundwork for future research endeavours aimed at unlocking the full potential of brainwave analysis for mind-controlled drones.

Keywords

Brain-computer interfaces (BCI), Hybrid neural networks, Mind-controlled drones, Motor imagery, Signal processing algorithms

Subject Area

Computer Science

Article Type

Article

First Page

3530

Last Page

3545

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite this Article

Al-isawi, Ather Salah Hadi; Muhammad, Sufri; Admodisastro, Novia Indriaty; Ahmad, Siti Anom; and Mohamed, Raihani (2025) "An Analytical Review of Brainwave Analysis for Mind-Controlled Drones: Utilizing Hybrid Neural Networks and Motor Imagery," Baghdad Science Journal: Vol. 22: Iss. 10, Article 28.
DOI: https://doi.org/10.21123/2411-7986.5100