An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods

Abstract

In the modern world of automation, biological signals, especially Electroencephalogram (EEG) is gaining wide attention as a source of biometric information. Eye-blinks and movement of the eyeballs produce electrical signals (contaminate the EEG signals) that are collectively known as ocular artifacts. These noise signals are required to be separated from the EEG signals to obtain the accurate results. This paper reports an experiment of ocular artifacts elimination from EEG signal using blind source separation algorithm based on independent component analysis and principal component analysis. EEG signals are recorded on three conditions, which are normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of EEG signals in the range of 12-14 Hz either on normal, closed, and blinked eyes conditions is obtained.

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“…Removal of artifacts is crucial for achieving good EEG decoding performance [53], [54]. Independent component analysis (ICA) does not require any prior information or additional reference channel for removal of artifacts [55]- [59]. In our study, we used FastICA algorithm to decompose the signals into independent components and detected those related to eye-blinks, heart and muscles electrical activity by visual inspection, and made the uncontaminated EEG signals reconstructed.…”