Moreno-Castelblanco and colleagues conducted a systematic review to explore how artificial intelligence (AI) is being applied to EEG signals for decoding lower-limb motor imagery (MI). MI, the mental rehearsal of movement without actual execution, is a foundation for brain–computer interfaces (BCIs) that could one day restore mobility and independence for people with motor disabilities such as stroke or spinal cord injury. From 287 publications screened, 35 studies met the inclusion criteria. Most (about 85%) relied on machine or deep learning models such as support vector machines (SVM), convolutional neural networks (CNN), or long short-term memory (LSTM) architectures. Many studies also combined EEG with other signals like electromyography (EMG), and half employed advanced decomposition techniques, such as wavelets, to extract clearer features. These strategies often improved classification performance, with some controlled experiments reaching above 90% accuracy, and in certain cases even surpassing 95%. Despite these advances, the review highlighted key challenges. The studies varied widely in EEG setups, preprocessing methods, and task design, making comparisons difficult. Most were limited to small groups of healthy participants in lab conditions, with only a quarter tested in clinical or rehabilitation environments. The authors conclude that the field is promising but not yet ready for widespread deployment. Larger, standardized datasets, shared protocols, and real-world trials are essential next steps. With these, AI-driven BCIs could move from experimental tools toward practical systems that support gait training, exoskeletons, and rehabilitation, ultimately helping patients regain independence.