شماره ركورد كنفرانس :
3297
عنوان مقاله :
Design Expanded BCI with Improved Efficiency for VR-embedded NeuroRehabilitation Systems
عنوان به زبان ديگر :
Design Expanded BCI with Improved Efficiency for VR-embedded NeuroRehabilitation Systems
پديدآورندگان :
Parivash Farhad School of Mechanical and Mechatronics Engineering Shahrood University of Technology Shahrood , Amuzadeh Leila Department of Biomedical Engineering Hamedan University of Technology Hamedan , Fallahi Alireza Department of Biomedical Engineering Hamedan University of Technology Hamedan
كليدواژه :
neuro-rehabilitation , virtual reality , EEG motor imagery signals , (brain computer interface(BCI
عنوان كنفرانس :
نوزدهمين سمپوزيوم بين المللي هوش مصنوعي و پردازش سيگنال
چكيده لاتين :
A general brain computer interface (BCI) usually consists of three main units known as preprocessing unit, feature selection unit and classification unit. In this paper, an EEG-based BCI with expanded structure is introduced that provides opportunity to improve efficiency of virtual reality (VR) embedded neurorehabilitation systems. The proposed BCI has to detect three different neuro-stimulations during specified motor imagery tasks and generate proper virtual neuro-stimulations for the avatar to do the task in the VR world. In the proposed BCI, discrete wavelet transformation (DWT) and multilayer perceptron (MLP) neural network are applied for preprocessing and classification, respectively; and an expounder is added to eliminate misclassifications which lead to wrong virtual neuro-stimulations. Offline EEG signals are applied to examine the proposed BCI and results are demonstrated.
