This review, presented by Chen et al. (2025), investigates hierarchical deep learning systems for motor imagery identification. Leveraging the strength of Convolutional Neural Networks (CNNs) in isolating spatial electroencephalography (EEG) features and the capacity of Long Short-Term Memory (LSTM) to model temporal signal dynamics, the authors propose an attention-enhanced convolutional–recurrent framework, referred to as the CNN-LSTM-Attention model. The researchers used an EEG dataset from 15 neurologically healthy individuals. Each participant participated in six recording sessions, with 48 trials per session, across four motor imagery classes (12 trials per class per session), for a total of 4320 trials. The four-class imagery paradigm included the imagination of left-hand movement, right-hand movement, bilateral foot movement, and tongue movement. The CNN-LSTM-Attention model demonstrated an accuracy of 97.25% ± 0.78%. It had precision scores of 97.18% ± 0.89%, recall scores of 97.25% ± 0.78%, and F1 scores of 97.21% ± 0.83%. It also demonstrates high discriminative power, with an AUC of 0.995 ± 0.002. However, it is limited by an increased training time of 523.6 ± 21.5 seconds due to increased computational complexity. Overall, this structure outperformed traditional machine learning and deep learning processes. In the discussion, the authors highlight the promise of this framework in improving EEG-based motor-imagery decoding, which may further enhance the reliability of assistive devices in Brain-Computer Interface (BCI) systems. They also note concerns regarding clinical population applicability due to increased training times and signal variability. Overall, motor imagery remains a promising target due to its non-invasive nature and potential value in neurological rehabilitation.