A virtual reality and brain computer interface system for upper limb rehabilitation of post stroke patients

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Beltran-Castanon C.A.
Beltran-Castanon N.J.
Caceres J.C.G.
Cutipa J.M.R.
Ticona J.R.
Zambrano C.E.P.
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Institute of Electrical and Electronics Engineers Inc.
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This work presents a brain computer interface (BCI) framework for upper limb rehabilitation of post stroke patients, combining BCI and virtual reality (VR) technology; a VR feedback is shown to the participants to achieve a greater activation of certain brain regions involved with the performing of upper limb motor task. This system uses an adaptive neuro-fuzzy inference system (ANFIS) classifier to discriminate between a motor task and rest condition, the first one classifies between extension and rest conditions; and the second one classifies between flexion and rest conditions. In the training stage, eight healthy subjects participated in the sessions, the best accuracies are 99.3% and 88.9%, as a result of cross-validation. Meanwhile, the best accuracy in online test is 89%. The methodology here presented can be straightforwardly employed as a rehabilitation system for brain repair in individuals with neurological diseases or brain injury.
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Neuromuscular rehabilitation