A major consequence of stroke is the selective loss of motor skills, but modern science and technology have developed an effective method, transcranial magnetic stimulation (TMS), which recognizes and promotes areas strongly associated with motor abilities. However, TMS is difficult to use, limiting usability to experts, and it requires specialized equipment that is expensive and difficult to attain. In this study, Choi et al propose a cost-effective, simple deep learning algorithm that can be incorporated into electroencephalograms (EEGs) to more effectively and precisely recognize the neural circuitry in motor hotspots. A convolutional neural network (CNN), a specific type of deep learning model, was trained to identify motor hotspots with the highest accuracy amongst 30 participants. After conducting the experiment, Choi et al found that the best mean error distance for healthy participants (0.35 ± 0.04 mm) and stroke patients (1.64 ± 0.14 mm) supported the potential incorporation of this deep learning model into clinical settings. These results underscore the high accuracy of the model in recognizing and differentiating motor hotspots. In addition, Choi et al found that understanding the mechanics of this algorithm was fairly simple, extending its usability to patients and caregivers, and it was far more cost-effective than TMS. Though the authors highlight that further research is needed, they emphasize the revolutionary nature of this model, suggesting that it can make neurorehabilitation more accessible and affordable.