{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T23:04:09Z","timestamp":1792019049199,"version":"4.4.0"},"reference-count":60,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T00:00:00Z","timestamp":1781913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Integrating neural and muscular signals into wearable robotics enables adaptive assistance during real-world tasks. This study proposes a multimodal neural interface for passive exoskeletons that combines electroencephalography (EEG) and electromyography (EMG) signals to classify motor gestures and estimate real-time cognitive and muscular effort, supported by finite-element-based biomechanical modeling. The system was implemented on the Ottobock Shoulder X passive exoskeleton\u00a9 and validated using synchronous EEG\u2013EMG acquisition via the LiveAmp platform\u00a9, a commercially available platform that was not developed specifically for this study. A hybrid CNN\u2013LSTM architecture with deep fusion was employed to enhance robustness and responsiveness under realistic operating conditions. This study proposes a multimodal neural interface for the software-level adaptive assistance of passive upper-limb exoskeletons. While the physical device maintains a static mechanical profile, the proposed digital framework achieves adaptation by interpreting the user\u2019s physiological and motor states. Ten healthy participants performed three functional tasks (screwing, moving the box, and lifting the box) under five assistive conditions. Finite element modeling (FEM) was used to characterize the torque\u2013angle relationship of the passive exoskeleton and to support the interpretation of experimentally observed assistive torque profiles. The FEM model, used as an offline biomechanical analysis tool to aid in the interpretation of experimental results, has not been integrated into the real-time control loop. Results showed an average classification accuracy of 90%, an F1-score of 0.85, and inference latency below 180 ms, confirming real-time applicability. Cognitive indices such as the Cognitive Load Index (CLI) and Frontal Asymmetry Index (FAI) enabled adaptive modulation of assistance strategies without requiring active actuation, thereby preserving the device\u2019s intrinsic passive nature. Comparative torque analysis highlighted the ergonomic benefits of passive systems in mid-range postures, while Finite Element Method (FEM) supported analysis clarified their limitations under highly dynamic loads compared to active solutions. These findings advance multimodal brain\u2013machine interfaces for wearable robotics by integrating physiological sensing, deep learning, and biomechanical modeling, offering a safe, energy-efficient, and adaptive approach with potential rehabilitation, occupational ergonomics, and human\u2013robot applications.<\/jats:p>","DOI":"10.3390\/s26123924","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T01:03:23Z","timestamp":1782263003000},"page":"3924","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multimodal EEG\u2013EMG and FEM-Based Adaptive Control of Passive Upper-Limb Exoskeletons"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6548-9503","authenticated-orcid":false,"given":"Luigi","family":"Bibb\u00f2","sequence":"first","affiliation":[{"name":"Department of Civil, Energy, Environment and Materials (DICEAM), \u201cMediterranea\u201d University of Reggio Calabria, I-89124 Reggio Calabria, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6846-1784","authenticated-orcid":false,"given":"Filippo","family":"Lagan\u00e0","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Applications Technologies and Sensors (BATS), Department of Health Science, \u201cMagna Gr\u00e6cia\u201d University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3391-1698","authenticated-orcid":false,"given":"Salvatore A.","family":"Pullano","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Applications Technologies and Sensors (BATS), Department of Health Science, \u201cMagna Gr\u00e6cia\u201d University of Catanzaro, 88100 Catanzaro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3153-9338","authenticated-orcid":false,"given":"Giovanni","family":"Angiulli","sequence":"additional","affiliation":[{"name":"DIIES Department, Mediterranea University, I-89122 Reggio Calabria, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Garcia-Moreno, F.M., Bermudez-Edo, M., Rodr\u00edguez-F\u00f3rtiz, M.J., and Garrido, J.L. 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