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Artifacts Reduction of EEG-SSVEP Signals for Emotion Detection with Robust Principal Component Analysis

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Abstract

Study of brain activity generally raises the difficult of distinguishing between the real activity and the artifacts which is caused by an external influence. Therefore, the classification accuracy is still limited by unpredictable signal variations due to background noise. In this paper, we propose a method of artifacts reduction using robust principal component analysis which is applied for an emotion extraction to identify the EEG-SSVEP signals which are induced by the four short movie stimuli (i.e., sad, angry, happy, and calm emotions). The proposed method was tested in real EEG-SSVEP records acquired from eight subjects. The average of 81.5% classification accuracy is achieved for each stimuli. The classification result shows that the proposed method can effectively reduce the artifacts from all subjects.

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