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.

After stroke, maladaptive neuroplasticity often presents as excessive recruitment of contralesional motor areas and the adoption of proximal compensatory m

Background/Objectives: Systematically elucidating the neural mechanisms of bowel and bladder control is crucial for developing closed-loop brain–computer interface (BCI) neurorehabilitation for patients with spinal cord injury (SCI). This study aims to characterize the spatiotemporal Electroencephalography (EEG) dynamics and compensatory cortical reorganization underlying continence motor intentions across healthy aging and SCI cohorts. Methods: 32-channel EEG signals were collected from healthy young adults, healthy older adults, and SCI patients during motor attempt paradigms of suppressed urination and defecation (SUD) and active urination and defecation (UD). Data were analyzed utilizing event-related spectral perturbation (ERSP) and phase-locking value (PLV) functional connectivity across alpha (8–13 Hz), beta1 (13–20 Hz), and beta2 (20–30 Hz) bands. Results: All cohorts exhibited generally consistent broadband event-related desynchronization (ERD) in the sensorimotor cortex (SMC) during visceral motor attempt. Conversely, the frontal cortex (FC) demonstrated marked frequency-band differentiation. During the SUD task, healthy older adults showed significantly greater SMC beta2 ERD than young adults, whereas SCI patients showed a similar but non-significant trend. Frontal beta2 findings were preliminary and should be interpreted with caution due to potential residual artifacts. Network analysis revealed that physiological aging and SCI are associated with large-scale topological reconfigurations, prominently featuring enhanced long-range frontal functional connectivity to compensate for sensory or peripheral deficits. Conclusions: High-level cortical intentions for continence are preserved after SCI, with frontal high-frequency activity possibly reflecting a compensatory process for sensorimotor disruption. These findings substantiate that visceral intentions exhibit discernible characteristics at the cortical level, establishing a spatiotemporal framework for targeted pelvic nerve modulation via brain–machine interfaces.