An on-line BCI for control of hand grasp sequence and holding using adaptive probabilistic neural network.
This paper presents a new EEG-based Brain-Computer Interface (BCI) for on-line controlling the sequence of hand grasping and holding 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. Moreover, for consistency of man-machine interface, it is desirable the intended movement to be what the subject imagines. For this purpose, we developed an on-line BCI which was based on the classification of EEG associated with imagination of the movement of hand grasping and resting state. A classifier based on probabilistic neural network (PNN) was introduced for classifying the EEG. The PNN is a feedforward neural network that realizes the Bayes decision discriminant function by estimating probability density function using mixtures of Gaussian kernels. Two types of classification schemes were considered here for on-line hand control: adaptive and static. In contrast to static classification, the adaptive classifier was continuously updated on-line during recording. The experimental evaluation on six subjects on different days demonstrated that by using the static scheme, a classification accuracy as high as the rate obtained by the adaptive scheme can be achieved. At the best case, an average classification accuracy of 93.0% and 85.8% was obtained using adaptive and static scheme, respectively. The results obtained from more than 1500 trials on six subjects showed that interactive virtual reality environment can be used as an effective tool for subject training in BCI.
Hazrati
Mehrnaz Kh
MKh
Department of Biomedical Engineering, Iran University of Science and Technology, Tehran, Iran.
Erfanian
Abbas
A
D000465:Algorithms; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D018737:Hand Strength / Q000502:physiology*; D006801:Humans; D016571:Neural Networks, Computer; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D014584:User-Computer Interface