Motor Imagery-Based Brain-Computer Interfaces: Challenges, Methods, and Future Directions

Brain-computer interfaces (BCIs) offer direct interaction between human brain and external devices, bypassing peripheral nerves and muscles. Within noninvasive BCI, electroencephalography (EEG) stands out due to its affordability and high temporal resolution. Motor imagery (MI)-based BCIs, in particular, harness the user's imagination of body movements to generate control commands without external triggers. This survey aims to synthesize recent literature on MI-based BCIs, providing an overview of key methods, existing challenges, and prospective solutions. By focusing on thematic insights, we highlight how researchers are refining signal preprocessing, feature extraction, and classification strategies to boost system performance. We conducted a thematic analysis of major works in EEG acquisition, artifact removal, feature extraction methods, classification approaches,deep neural networks, and adaptive or transfer learning frameworks. We also explored user-centered factors such as training protocols and the phenomenon of BCI-illiteracy. Notable advancements have emerged in signal preprocessing, sophisticated machine learning models, and calibration-reducing strategies like transfer learning. However, the complexity of EEG signals, high inter-subject variability, and the presence of BCI-illiteracy still limit real-world adoption. Adaptive techniques and personalized feedback can mitigate these constraints, and novel methods continue to emerge. MI-BCIs hold promise for rehabilitation, assistive technologies, neuroergonomics, and broader human-machine interactions. By clarifying these core themes this survey underscores the need for multidisciplinary, user-centric approaches to advance MI-BCI reliability, accuracy, and overall usability in day-to-day contexts.