Recent Trends in Motor Imagery Decoding Architectures: A Comprehensive Review

In this review, the deep learning architectures designed so far to decode the electroencephalography (EEG)-based motor imagery (MI) data has been systematically investigated. This article highlights the importance of end-to-end deep learning frameworks, with a growing focus on attention mechanisms and interpretability; which are replacing handcrafted feature extraction methods. It accomplishes this by classifying current research into three primary paradigms: conventional signal processing techniques, convolutional neural network (CNN)based approaches, and hybrid attention-based models. While CNN-based approaches use automated feature learning to achieve 72.9-99.1% accuracy, conventional approaches use well-established signal processing techniques to achieve 75-96% accuracy. The most advanced hybrid attention-based models available today achieve 55.8-88.5% accuracy and offer better interpretability and cross-subject generalization. Important trends include the creation of subject-independent classifiers, the incorporation of multi-modal features, and the use of neuromorphic computing concepts. Real-time deployment, explainable AI for clinical applications, and energy efficiency are given top priority in future directions. This review provides researchers and practitioners with an organized understanding of recent developments and identifies promising directions for creating practical motor imagery-based brain-computer interfaces.