Resumen: 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.
Achanccaray, D.; Acuña, K.; Carranza, E. y Andreu-Perez, J.
Año: 2017
Título de la revista: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Ciudad: Nápoles, Italia
Página inicial - Página final: 1-5
Url: https://ieeexplore.ieee.org/document/8015726