{"id":4027,"date":"2025-12-10T13:59:18","date_gmt":"2025-12-10T12:59:18","guid":{"rendered":"https:\/\/www.um.sav.sk\/en\/?p=4027"},"modified":"2025-12-10T13:59:46","modified_gmt":"2025-12-10T12:59:46","slug":"1-patient-therapist-collaborative-bci-vr-motor-neurorehabilitation-after-stroke","status":"publish","type":"post","link":"https:\/\/www.um.sav.sk\/en\/selected-results\/1-patient-therapist-collaborative-bci-vr-motor-neurorehabilitation-after-stroke\/","title":{"rendered":"Patient-therapist collaborative BCI-VR motor neurorehabilitation after stroke"},"content":{"rendered":"

Investigators:<\/strong> Nina Evetovi\u0107, Arina Polyanskaya,\u00a0Roman Rosipal, Zuzana Ro\u0161\u0165\u00e1kov\u00e1<\/p>\n

Integrated brain-computer interface (BCI) and virtual reality (VR) technologies provide a flexible platform for a series of neurorehabilitation therapies based on motor imagery in patients after stroke. However, these approaches lack the social aspect and human interaction. As part of our research, we successfully designed an intelligent BCI system in which the patient and therapist collaborate through their avatars within a VR environment. This system enables the creation and selection of various occupational therapy scenarios according to the patient\u2019s recovery needs, mental state, and immediate responses [3]. Using machine learning methods – particularly neural networks, N-way partial least squares, and tensor decomposition – we also investigated the possibilities of monitoring mental fatigue levels during VR-based rehabilitation [1], EEG signal preprocessing [2, 5], and oscillatory EEG rhythms detection [4].<\/p>\n

Projects:<\/strong> 101118964-DONUT, 09I03-03-V04-00205, 09I03-03-V04-00443, APVV-21-0105<\/p>\n

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Fig. 1: <\/em>Diagram illustrating the collaborative brain-computer interface and virtual reality (BCI-VR) environment designed for neurorehabilitation of patients after ischemic stroke.<\/em><\/p>\n

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Publications:<\/strong><\/p>\n