EMOTION ANALYSIS FROM PHYSIOLOGICAL SIGNAL USING EEG

Abstract

modern world, hearing a song or seeing a video has become an imperative entertainment to people. Music Video Content (MVC) must be retrieved based on emotional information on human presence of mind. Many researches focus the study of relationship between videos and users’ induced physiological and psychological responses. The existing system performs the emotion analysis by using single-trial classification with arousal, valence and liking using features extracted from the electroencephalogram (EEG) and peripheral physiological signals and MCA (Multimedia Content Analysis) modalities. It uses semi-automatic stimuli selection method using affective tags, which was validated by an analysis of the ratings given by the participants. But, it has limitations while considering the signal noise, physiological differences among individuals, and limited quality of self-assessments. To overcome these limitations, it is necessary to develop a new technique for effective MVC model. In the proposed work, a new framework for personalized MV affective analysis, visualization, and retrieval is used. By stimulating the human affective response mechanism, affective video content analysis extracts the affective information contained in videos, and, with the affective information, natural, user-friendly, and effective MVC access strategies could be developed. Based on the values retrieved by Independent Components Analysis (ICA), the music video is retrieved from the large-scale MV databases. The proposed approach may provide an efficient mechanism for searching results with a high degree of precision with minimal error. Thus it will be helpful for overcoming the current limitations and improve the final performance of affective computation.