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Neural Eng."],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    contralaterally-controlled functional electrical stimulation (CCFES) is an emerging intervention that has evidence for improving therapy for post-stroke hand hemiplegia more than cyclic stimulation (e.g. hand dexterity). Its key feature is that the amount of paretic hand opening assistance is controlled by the degree of non-paretic hand opening, which enables it to assist hand opening during task practice. However, current CCFES techniques do not prevent participants from reducing volitional effort, which is referred to as \u2018slacking\u2019 and does not benefit motor relearning. As purely a computational study, the current work presents a novel effort-dependent CCFES controller that assists as-needed when detecting volitional hand opening, and provides evidence for stability, sensitivity, along with target tracking performance.\n                    <jats:italic>Approach.<\/jats:italic>\n                    In this study, simulations were used to investigate the effort-dependent controller\u2019s stability and sensitivity to system variability using the first-ever computational model of a CCFES trajectory tracking task. System variability was represented by a range of model parameters, including volitional and stimulated hand opening speed, EMG occlusion, stimulation response (\n                    <jats:italic>M<\/jats:italic>\n                    -Wave) phase shifts, and\n                    <jats:italic>M<\/jats:italic>\n                    -Wave filters: Gram\u2013Schmidt, comb, and blanking. System performance was quantified by tracking error and effort estimation accuracy (signal-to-noise ratio, and linear regression\n                    <jats:italic>r<\/jats:italic>\n                    -squared).\n                    <jats:italic>Main Results.<\/jats:italic>\n                    Results showed that effort-dependent CCFES was highly stable across varying hand opening parameters, EMG-related conditions, and filtering quality\u2014with % changes in stability margins being less than 2.2%. It was also discovered that Gram\u2013Schmidt filter provided significantly better target tracking performance than comb filter and blanking, but it was also significantly more sensitive across parameter variations. Case scenarios were also simulated to demonstrate the stability and performance of effort-dependent CCFES during a tracking task.\n                    <jats:italic>Significance.<\/jats:italic>\n                    The demonstrated stability across a wide range of physiologically and clinically relevant conditions suggests that this approach can provide reliable, adaptive assistance during task practice without compromising safety or movement consistency. These findings support the further investigation of effort-dependent CCFES in human experiments.\n                  <\/jats:p>","DOI":"10.1088\/1741-2552\/ae57da","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T22:51:28Z","timestamp":1774565488000},"page":"046047","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Stability and sensitivity analysis of effort-dependent CCFES for simulated hemiplegic hand assistance after stroke: computational model"],"prefix":"10.1088","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6412-8220","authenticated-orcid":true,"given":"M Akif","family":"Gormez","sequence":"first","affiliation":[{"name":"Case Western Reserve University"},{"name":"MetroHealth System"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3659-9986","authenticated-orcid":false,"given":"Nathaniel S","family":"Makowski","sequence":"additional","affiliation":[{"name":"MetroHealth System"},{"name":"Case Western Reserve University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8683-2865","authenticated-orcid":true,"given":"Patrick E","family":"Crago","sequence":"additional","affiliation":[{"name":"Case Western Reserve University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3057-1221","authenticated-orcid":true,"given":"Michael J","family":"Fu","sequence":"additional","affiliation":[{"name":"Case Western Reserve University"},{"name":"MetroHealth System"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,8,11]]},"reference":[{"key":"jneae57dabib1","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1177\/1545968317721975","type":"journal-article","article-title":"Robotic assistance for training finger movement using a hebbian model: a randomized controlled trial","volume":"31","author":"Rowe","year":"2017","journal-title":"Neurorehabilit. 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