Rhythm-based features for classification of focal and non-focal EEG signals

Abstract

Electroencephalogram (EEG) contains five rhythms, which provide details about various activities of brain. These rhythms are separated using Hilbert-Huang transform for classification of focal and non-focal EEG signals. For this, the EEG signal is disintegrated into narrow bands intrinsic mode functions (IMFs) using empirical mode decomposition, and analytic representation of IMFs is computed by Hilbert transformation that helps to extract instantaneous frequencies of respective IMFs. Frequency bands of EEG signals known as rhythms are separated from analytic IMFs using instantaneous frequencies. Two efficient parameters Pearson product-moment correlation coefficient and Spearman rank correlation coefficient extracted from the rhythms are used with different kernel functions of least-squares support vector machine for the classification of focal and non-focal EEG signals. Thus, obtained results show improved performance of proposed method as compared to other existing methods.