Development of Brain Computer Interface, using Neural Network

This paper focus on the development of a Brain Computer Interface (BCI) using neural network, where BCI is an
electronic communication system which accomplish the operating principle of “stimulate thinking and make it
happen destitute of anyone’s physical efforts”. Based on the environmental conditions the BCI allows the users to act on his thoughts, without using their peripheral muscles and nerves. A machine learning algorithm is included in all the neighboring BCI, which learns a function from the training data’s besides used to distinguish non identical brain activities. The investigation of this work with Brain Computer Interface (BCI) signals possessed from Electro- EncephaloGraphy (EEG) to bring out a relationship between a person’s mind condition and a computer employed signal processing system that illustrates the EEG signals. A Radial basic function framework for machine learning is used here for the linear discriminant analysis of EEG data. Based on the Radial Basic Function’s (RBF) the output an intended mental task can be performed.