Virtual Reality (VR) combined with near real-time EEG signal processing can be used as an improvement to already existing rehabilitation techniques, enabling practitioners and therapists to get immersed into a virtual environment together with patients. The goal of this study is to propose a classification model along with all preprocessing and feature extraction steps, able to produce satisfying results while maintaining near real time performance. The proposed solutions are tested on an EEG signal dataset, containing left/right hand motor imagery movement experiments performed by 52 subjects. Performance of different models is measured using accuracy score and execution time both in the testing and training phase. In conclusion, one model is proposed as optimal with respect to the requirements of potential patient rehabilitation procedures.
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