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    Automatic Removal of Ocular Artefacts in EEG Signal by Using Independent Component Analysis and Artificial Neural Network
    (IEEE, 2017) CInar, Salim; Menguc, Engin Cemal; Acir, Nurettin
    Ocular artefacts caused by eye movements can distort Electroencephalogram (EEG) recordings. It is important to obtain clean EEG signals in diagnosing and interpreting diseases. Meaningful EEG signals should not be distorted during the removal of artefacts. In this study, Independent Component Analysis and Artificial Neural Network were used together to remove ocular artefacts. The method was tested by using the real dataset. The Relative Error (RE) and Correlation Coefficient (CC) was used to test the performance of the method. Relative error = 0.227 0.229 and correlation coefficient = 0.941 0.088 was calculated in the performance analysis. According to the results, the proposed method is successful in removing ocular artefacts in EEG signals.