An ICA/HHT Hybrid Approach for Automatic Ocular Artifact Correction

Abstract

In the field of electroencephalogram (EEG) signal processing, the ocular artifact (OA), especially the blink is the most commonly observed, gives the largest disturbance, and can hardly be automatically corrected due to its sudden appearance and wide frequency band influence. In this paper, we focus on a 2-stage independent component analysis/Hilbert Huang transformation (ICA/HHT) hybrid OA correction method which realizes an automatic OA correction as well as a better information conservation to the OA distorted EEG data. In the 1st stage, the ICA is introduced to convert the raw EEG signals into a set of independent signal sources, i.e. a number of independent components (ICs). In the 2nd stage, the HHT is then applied to analyze the ICs in order to emphasize the differences between the OA related ICs and the normal EEG related ICs. On the intrinsic mode functions (IMFs) of each IC extracted by the empirical mode decomposition (EMD), the Hilbert spectrums can clearly indicate where an OA exists and make an automatic OA correction possible. EEG signal samples randomly picked from a variety of neuropsychology tasks were used to evaluate the proposed automatic OA correction method, and the outcomes approved an excellent OA correction performance as well as a better information conservation ability comparing to the well applied methods nowadays.