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

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.