Classification between Focal and Non Focal EEG Signals Based on Signal Processing and Neural Networks

In this study we propose a novel method for the classification of focal and non focal electroencephalogram (EEG) signals based upon different signal processing techniques and neural networks. First, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) using empirical mode decomposition (EMD), and the third and fourth IMFs components are extracted which contain most of the EEG signals' energy and are considered to be the predominant IMFs. Second, phase space of the two predominant IMFs components is reconstructed, in which the properties associated with the EEG system dynamics are preserved. Three-dimensional (3D) phase space reconstruction (PSR) together with Euclidean distance (ED) has been utilized to derive features. Third, neural networks are then used as the classifier to distinguish between focal and non focal EEG signals. Finally, experiments are carried out on the Bern Barcelona database to assess the effectiveness of the proposed method. By using the 10-fold cross-validation style, the achieved accuracy on the 50 pairs and 3750 pairs of EEG signals is reported to be 96% and 95.37%, respectively.