Abstract
Electroencephalography (EEG) is a portable brain-imaging technique with the advantage of high-temporal resolution that can be used to record electrical activity of the brain. However, it is difficult to analyze EEG signals due to the contamination of ocular artifacts, and which potentially results in misleading conclusions. Also, it is a proven fact that the contamination of ocular artifacts cause to reduce the classification accuracy of a brain-computer interface (BCI). It is therefore very important to remove/reduce these artifacts before the analysis of EEG signals for applications like BCI. In this paper, a hybrid framework that combines independent component analysis (ICA), regression and high-order statistics has been proposed to identify and eliminate artifactual activities from EEG data. We used simulated, experimental and standard EEG signals to evaluate and analyze the effectiveness of the proposed method. Results demonstrate that the proposed method can effectively remove ocular artifacts as well as it can preserve the neuronal signals present in EEG data. A comparison with four methods from literature namely ICA, regression analysis, wavelet-ICA (wICA), and regression-ICA (REGICA) confirms the significantly enhanced performance and effectiveness of the proposed method for removal of ocular activities from EEG, in terms of lower mean square error and mean absolute error values and higher mutual information between reconstructed and original EEG.
Highlights
Nowadays, researchers have been using non-invasive neuro-physiological techniques to understand the functional dynamics of the brain (Jöbsis, 1977; Friston et al, 1994; Kiebel et al, 2009; Kamran et al, 2012, 2015; Hogervorst et al, 2014)
The present study proposed to use a combination of entropy and kurtosis to improve the automatic detection of artifactual independent components (ICs)
The performance of the automatic identification is compared with ADJUST based identification of ocular artifacts related ICs (Mognon et al, 2010)
Summary
Researchers have been using non-invasive neuro-physiological techniques to understand the functional dynamics of the brain (Jöbsis, 1977; Friston et al, 1994; Kiebel et al, 2009; Kamran et al, 2012, 2015; Hogervorst et al, 2014). Eye movements and blinking generate major artifacts with high magnitudes as Ocular Artifact Removal from EEG compared to the pure neuronal activity (Berg and Scherg, 1991; Wallstrom et al, 2004; Dimigen et al, 2011). Such interferences are commonly known as ocular artifacts (Corby and Kopell, 1972; Gratton et al, 1983). For EEG signal analysis, a method is required that can efficiently remove ocular artifacts without distorting and losing neuronal-activityrelated EEG signals
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