# Preprocessing for Lessening the Influence of Eye Artifacts in EEG Analysis  
[PERSON]  
AITC Research Group, E.T.S.I. Telecomunicacion, Universidad de Malaga, Andalucia Tech, Campus Universitario de Teatinos s/n, 29071 Malaga, Spain; [EMAIL_ADDRESS] (A.V. ); [EMAIL_ADDRESS] (A.M.B. ); [EMAIL_ADDRESS] (E.B. ); [EMAIL_ADDRESS] (N.T.H.) [PERSON]  
AITC Research Group, E.T.S.I. Telecomunicacion, Universidad de Malaga, Andalucia Tech, Campus Universitario de Teatinos s/n, 29071 Malaga, Spain; [EMAIL_ADDRESS] (A.V. ); [EMAIL_ADDRESS] (A.M.B. ); [EMAIL_ADDRESS] (E.B. ); [EMAIL_ADDRESS] (N.T.H.) [PERSON]  
AITC Research Group, E.T.S.I. Telecomunicacion, Universidad de Malaga, Andalucia Tech, Campus Universitario de Teatinos s/n, 29071 Malaga, Spain; [EMAIL_ADDRESS] (A.V. ); [EMAIL_ADDRESS] (A.M.B. ); [EMAIL_ADDRESS] (E.B. ); [EMAIL_ADDRESS] (N.T.H.) [PERSON]  
AITC Research Group, E.T.S.I. Telecomunicacion, Universidad de Malaga, Andalucia Tech, Campus Universitario de Teatinos s/n, 29071 Malaga, Spain; [EMAIL_ADDRESS] (A.V. ); [EMAIL_ADDRESS] (A.M.B. ); [EMAIL_ADDRESS] (E.B. ); [EMAIL_ADDRESS] (N.T.H.) [PERSON]  
AITC Research Group, E.T.S.I. Telecomunicacion, Universidad de Malaga, Andalucia Tech, Campus Universitario de Teatinos s/n, 29071 Malaga, Spain; [EMAIL_ADDRESS] (A.V. ); [EMAIL_ADDRESS] (A.M.B. ); elvira. (ICA) [9; 11; 12; 13] have been used to separate the underlying signals present in the multichannel EEG recordings. Generally, these studies show that PCA cannot completely separate eye artifacts from brain signals due to the PCA assumption according to which the decomposed components are algebraically orthogonal, which is in general difficult to satisfy [14]. ICA methods, on their side, are based on the assumption that the observed signals are a linear combination of \(n\) unknown and statistically independent sources [13]. The goal of ICA is to find a separating or unmixing matrix that extracts the independent components from the signals. With regard to EEG recordings, this model implies that the signals recorded on the scalp are mixtures of time courses of temporally independent cerebral and artifactual signals in such a way that potentials arising from different parts of the brain and diverse activities are summed linearly at the electrodes with propagation delays negligible [9]. The main use of the ICA approach to the eye artifact removal problem relies on the elimination of the artifactual components before mixing the components back to obtain clean EEG multichannel data. However, which components or excerpts should be considered artifactual and removed and which ones should not, must be defined. This question is usually answered by a visual inspection of the waveform and scalp topographies and the manual selection by an experienced scientist [9; 11]. However, [PERSON] et al. [13] proposed an automatic method for the selection of artifact-related components by matching the given scalp distribution of each component to a previously generated template; [PERSON] et al. describe [14] an automatic process for eye movement removal relying on the elimination of components based on the correlation with the electrooculogram (EOG) channel. Machine-learning approaches have also been proposed for the identification of artifacts in EEG signals after ICA [15] and the utilization of convolutional neural networks (CNNs) [16] has lately become possible with advances in computation. In this manuscript, we present and compare two approaches for the treatment of artifactual independent components (ICs) generated by ICA. In both methods, the identification and selection of the artifact-related ICs is based on the cross-correlation coefficient between the channel and each ICs. The most correlated-to-EOG ICs are selected as odd ones. In the first approach, the samples affected by the artifacts in the selected ICs are attenuated before mixing the components back into the EEG data channels.