Spectral and Spatial Feature Extraction of Electroencephalographic (EEG) Data Using Independent Component Analysis (ICA)

Purpose of this research is to extract features associated with human brain signal related to electroencephalographic measurements and classification of extracted EEG signals to the relevant the brain region. EEG brain signals from 14 electrodes placed on the human scalp is recorded non-invasively using Emotiv EPOC / EPOC+: Scientific contextual EEG system with a sampling rate of 128 Hz. EEG data of human brain functions related to evoked motor imagery tasks consisting of two different classes of activities, namely imagination of right arm-movement i.e. arm down (termed here as PUSH) and arm up (termed here as PULL) for three healthy subjects is recorded. After pre-processing for noise and artifacts removal, the EEG signals associated with investigated evoked activities are extracted using Independent Component Analysis (ICA). The results obtained show good contrast plots for the extracted brain signals recorded on F7, FC5 and FC6 electrodes, decomposed on independent components, namely IC1, IC4, IC5, IC6. Classification of extracted features is mapped on to the motor imagery parts of human brain. The algorithm based on independent component analysis gives good results for feature extraction corresponding to evoked signals. Power spectra are also determined for the extracted independent components.