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    Automatic Removal of Ocular Artefacts In EEG Signal by Using Independent Component Analysis and Chauvenet Criterion
    (Ieee, 2017) Cinar, Salim; Acır, Nurettin
    Eye movements (saccade, blink and cause artefacts in Electroencephalogram recordings. The ocular artefact can distort the EEG signals. Removal of ocular artefact is important issue in EEG signal analysis. The main task of artefact removal algorithms is to obtain cleaned EEG without losing meaningful EEG signal. The main focus of this work is to remove ocular artefact automatically by using Independent Component Analysis and Chauvenet criterion. The method is tested on real dataset. Relative error and Correlation coefficient are used for the performance test. The performance of the proposed method was Relative error= 0.273 +/- 0.148, Correlation coefficient= 0.943 +/- 0.042 in the dataset. The results show that the porposed method effectively removes ocular artefacts in EEG.
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    A novel system for automatic removal of ocular artefacts in EEG by using outlier detection methods and independent component analysis
    (Pergamon-Elsevier Science Ltd, 2017) Cinar, Salim; Acır, Nurettin
    ElectroEncephaloGram (EEG) gives information about the electrical characteristics of the brain. EEG can be used for various applications, such as diagnosis of diseases, neuroscience and Brain Computer Interface (BCI). Several artefacts sources can disturb the brain signals in EEG measurements. The signals caused by eye movements are the most important sources of artefacts that must be removed in order to obtain a clean EEG signal. During the removal of Ocular Artefacts (OAs), the preserve of the original EEG signal is one of the most important points to be taken into account. An ElectroOculoGram (EOG) reference signal is needed in order to remove OAs in some methods. However, long-term EOG measurements can disturb a subject. In this paper, a novel robust method is proposed in order to remove OAs automatically from EEG without EOG reference signal by combining Outlier Detection and Independent Component Analysis (OD-ICA). The OD-ICA method searches OA patterns in all components instead of a single component. Moreover, OD-ICA removes only OA patterns and preserves meaningful EEG signal. In this method, user intervention is not needed. These advantages make the method robust. The OD-ICA is tested on two real datasets. Relative Error (RE), Correlation Coefficient (CorrCoeff) and percentage of finding OA pattern are used for the performance test. Furthermore, three different methods are used as Outlier Detection (OD) methods. These are the Chauvenet Criterion, the Peirce's Criterion and the Adjusted Box Plot. The performance analysis is made between our proposed method and the method of zeroing the component with artefact. The experiment results show that the proposed OD-ICA method effectively removes OAs from EEG signals and is also successful in preserving the meaningful EEG signals during the removal of OAs. (C) 2016 Elsevier Ltd. All rights reserved.