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An on-line BCI system for hand movement control using real-time recurrent probabilistic neural network

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2 Author(s)
Ahmadi, M. ; Dept. of Biomed. Eng., Iran Univ. of Sci. & Technol. (IUST), Tehran ; Erfanian, A.

This paper presents a new EEG-based Brain-Computer Interface (BCI) for on-line controlling the hand movement in a virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. For this purpose, two classifiers are designed. The first classifier which is based on the imagination of right-hand movement is for controlling the hand grasping, holding and opening. The second classifier, which is based on the imagination of left-hand movement is designed for error correction and activating the first classifier. One important issue in developing an on-line BCI is the robust and accurate classification of EEG signal which is characterized with a time-varying distribution. In this work, we present a real-time recurrent probabilistic neural network for classifying the EEG signals. The results show that the subjects were able to achieve an accuracy more than 80% during the first session of experiment without off-line training and 73%-91% during the last session using single-trial classification with no adaptation.

Published in:

Neural Engineering, 2009. NER '09. 4th International IEEE/EMBS Conference on

Date of Conference:

April 29 2009-May 2 2009