A Novel Brain–Computer Interface Application: Precise Decoding of Urination and Defecation Motor Attempts in Spinal Cord Injury Patients

Patients with spinal cord injury (SCI) often face urinary and defecation dysfunction, and existing treatments have limited effectiveness. Brain-computer interface (BCI) technology has been shown to have positive effects on the rehabilitation of SCI patients, but its application in promoting the recovery of urinary and defecation functions has not been explored. This study proposes a new BCI application approach and develops an accurate decoding model targeted at urination and defecation motor attempt tasks. Specifically, we designed a Bidirectional Temporal Convolutional Network (UDCNN-BiTCN) to decode both the suppressed urination and defecation (S-UD) task and the urination and defecation (UD) task. Seventy-one participants (including 44 healthy controls and 27 SCI patients) were recruited for the experiment. The results showed that UDCNN-BiTCN achieved an average accuracy of 91.47% on the S-UD task and 91.81% on the UD task. The study also conducted within-subject cross-task transfer learning and cross-subject experiments, further validating the superiority of the model. In addition, we conducted a comprehensive analysis of this new paradigm from the perspective of classification performance. The research approach and findings in this study provide a valuable new perspective for BCI applications in the recovery of urinary and defecation functions.