EEG signal classification using Principal Component Analysis with Neural Network in Brain Computer Interface applications

Abstract

Brain Computer Interface (BCI) is the method of communicating the human brain with an external device. People who are incapable to communicate conventionally due to spinal cord injury are in need of Brain Computer Interface. Brain Computer Interface uses the brain signals to take actions, control, actuate and communicate with the world directly using brain integration with peripheral devices and systems. Brain waves are in necessitating to eradicate noises and to extract the valuable features. Artificial Neural Network (ANN) is a functional pattern classification technique which is trained all the way through the error Back-Propagation algorithm. In this paper in order to classify the mental tasks, the brain signals are trained using neural network and also using Principal Component Analysis with Artificial Neural Network. Principal Component Analysis (PCA) is a dominant tool for analyzing data and finding patterns in it. In Principal Component Analysis, data compression is possible and it projects higher dimensional data to lower dimensional data. By using Principal Component Analysis with Neural Network, the redundant data in the dataset is eliminated first and the obtained data is trained using Neural Network. EEG data for five cognitive tasks from five subjects are taken from the Colorado University database. Pattern classification is applied for the data of all tasks of one subject using Neural Network and also using Principal Component Analysis with Neural Network. Finally it is observed that the correctly classified percentage of data is better in Principal Component Analysis with Neural Network compared to Neural Network alone.