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In the literature, conditional, closed-loop stimulation paradigms have been shown to increase bladder capacity and voiding efficacy compared to continuous stimulation. Current limitations to the implementation of the closed-loop stimulation paradigm include the lack of robust and real-time decoding strategies for the bladder fullness state. We recorded intraneural pudendal nerve signals in five anesthetized pigs. Three bladder-filling states, corresponding to empty, full, and micturition, were decoded using the Random Forest classifier. The decoding algorithm showed a mean balanced accuracy above 86.67% among the three classes for all five animals. Our approach could represent an important step toward the implementation of an adaptive real-time closed-loop stimulation protocol for pudendal nerve modulation, paving the way for the design of an assisted-as-needed neuroprosthesis.<\/jats:p>","DOI":"10.1063\/5.0156484","type":"journal-article","created":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T19:31:49Z","timestamp":1696534309000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":12,"title":["Decoding bladder state from pudendal intraneural signals in pigs"],"prefix":"10.1063","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3208-1971","authenticated-orcid":false,"given":"A.","family":"Giannotti","sequence":"first","affiliation":[{"name":"The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna 1 , Pisa, 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