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Classification of EEG Signals for Eye Focuses Using Artificial Neural Network

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Abstract

This paper presents a classification method of EEG signal for eye focuses which consists of three eyes movement left, top, and right. The electroencephalography (EEG) data were recorded from eight volunteers including males and females. The volunteers were stimulated using designed steady-state visual evoked potential (SSVEP) to gaze the designed SSVEP. The acquired EEG data were processed using wavelet decomposition <BARISBARU>and reconstruction. The reconstructed and decomposed signals were used as features to the input of artificial neural network (ANN). Based on the classification results, the decomposed signals of D1 give the best performance with the average accuracy of 98 persen for validation, 67.19 persen for validation, and 60.94 persen for testing

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