2017 : Feature extraction using combination of intrinsic mode functions and power spectrum for EEG signal classification

Prof.Ir. Supeno Djanali M.Sc Ph.D
Prof. Ir. Handayani Tjandrasa M.Sc., Ph.D.
Ir. F.X. Arunanto M.Sc.


Abstract

The measurement of brain electrical activity recorded as EEG signals finds most application in epilepsy. EEG waveforms carry information about the underlying neural system dynamics and show different features amongst epilepsy syndromes. In this research, empirical mode decomposition (EMD) and power spectrum were employed to extract the features from EEG dataset of healthy participants, and epilepsy patients with seizure and seizure free conditions. The recorded EEG signals are represented by 500 signal segments from 5 sets of different conditions. The sum of Intrinsic Mode Function (IMF) power spectrum components gave 10 features for 50 components, and 20 features for 25 components, which were used as the classification inputs for artificial neural networks and random forest classifiers. The classifications were carried out for 3, 4, and 5 classes. From the experiments, the highest average accuracy …