Features Extraction Techniques of EEG Signals for Brain Computer Interface Applications

Abstract

Electroencephalography (EEG) signals were analyzed in many research applications as a channel of communication between humans and computers. EEG signals associated with imagined fists and feet movements were filtered and processed using wavelet transform analysis for feature extraction. The proposed work used Neural Networks (NNs) as a classifier that enables the classification of imagined movements into one of the four classes (left hand , right hand , foot and tongue).Daubechies wavelet mother function(db8) was used analyze the extracted events and then different feature extraction measures were calculated for three detail levels of the wavelet coefficients .Intensive NN training and testing experiments were carried out, The result of classification performance is 86.7% was achieved with a NN classifier of 17 hidden layers while using the Integral EEG (IEEG) of the wavelet Daubechies coefficients as inputs to FNN