EEG Signal Classification using Principal Component Analysis and Wavelet Transform with Neural Network

The Brain-Computer Interface (BCI) is the technology that enables direct communication between the human brain and the external devices. Electroencephalography (EEG) proves to be the most studied measure of recording brain activity in BCI design. The paper is intended to analyze and extract the features of EEG signal and to classify the signal so that human emotions can be discriminated and serve as the control signal for BCI. The proposed method involves EEG data acquisition and processing which is done by feature extraction and classification of features at different frequency levels for Beta, Alpha, Theta and Delta waves. The Principal Component Analysis(PCA), and the Wavelet Transform(WT) can be used for dimensionality reduction and feature extraction. The Artificial Neural Network (ANN) which is a computationally powerful model, is used as the classifier. The paper presents the comparison between the two approaches PCA and WT applied on the ANN Classifier.