Deep Learning Approaches for EEG-Motor Imagery-Based BCIs: Current Models, Generalization Challenges, and Emerging Trends

Abstract

This study critically examines the evolution of deep learning (DL) for electroencephalogram (EEG) based motor imagery (MI) decoding with a focus on real-time Brain Computer Interfaces (BCIs) development. Prior studies often prioritize accuracy in isolation, neglecting computational efficiency, interpretability, noise robustness, and neurophysiological variability across subjects and tasks, while recent DL advancements have introduced novel architectures to address these issues. This work systematically evaluates those novel architectures and emerging trends through addressing 4 research questions (RQs) based on an extensive review. Initially, over 188 papers from 3 databases were retrieved with a focus on publications from 2024 to 2025. Later, through multi-stage filtering based on strict inclusion criteria, a refined corpus of 68 high-quality studies was selected. This analysis reveals that state-of-the-art models achieve competitive accuracy, varying 85-100% on public datasets, but still face challenges in computational demands, noise resilience, generalization and BCI deployment. Additionally, preprocessing and integrated hybrid feature extraction paired with explainable AI (XAI) techniques are discussed. Emerging trends such as neuromorphic computing, federated learning (FL), and closed-loop adaptive systems offering solutions to current deployment barriers have been included in the discussion. Ethical and ecological considerations, such as data privacy, algorithmic bias, and energy efficiency, are notably represented in the literature. This review contributes a holistic framework for evaluating DL models, emphasizing the need to balance accuracy, efficiency, and adaptability. By synthesizing insights from large-scale datasets and explainability tools, this study exposes the limitations of current DL studies reliant on homogenous data, unavailability of codes to reproduce models and proposes strategies to mitigate neurophysiological variability. The finding underscores the urgency of prioritizing clinical relevance, ethical validation, and ecological robustness to bridge the lab to real-world divide, offering actionable directions for future research in low-power, generalizable, and user-centric BCI design.