Emotion classification from EEG signals

Emotions are the state of feelings resulting from physical and psychological change which in turn influence our behaviour. Emotions play an important role in corporate activities like organizing, planning, motivating etc. The negative emotions may lead to chronic emotional disorders like depression, anxiety, stress. Therefore, there is a need to analyse and classify these emotional changes through brain signals. The proposed paper aims to classify the types of emotions by observing EEG signals. There is always a problem of correlated data with EEG signals. This correlation results in redundancy of the data and increases computational time. This paper proposes the use of common spatial pattern to address the problem of correlated data and high dimensionality. The classification is carried out using support vector machine and the performance of the classifier is being measured in terms of accuracy of classification and standard deviation.