  Human affects are complex paradox and an active research domain in affective
computing. Affects are traditionally determined through a self-report based
psychometric questionnaire or through facial expression recognition. However,
few state-of-the-arts pieces of research have shown the possibilities of
recognizing human affects from psychophysiological and neurological signals. In
this article, electroencephalogram (EEG) signals are used to recognize human
affects. The electroencephalogram (EEG) of 100 participants are collected where
they are given to watch one-minute video stimuli to induce different affective
states. The videos with emotional tags have a variety range of affects
including happy, sad, disgust, and peaceful. The experimental stimuli are
collected and analyzed intensively. The interrelationship between the EEG
signal frequencies and the ratings given by the participants are taken into
consideration for classifying affective states. Advanced feature extraction
techniques are applied along with the statistical features to prepare a fused
feature vector of affective state recognition. Factor analysis methods are also
applied to select discriminative features. Finally, several popular supervised
machine learning classifier is applied to recognize different affective states
from the discriminative feature vector. Based on the experiment, the designed
random forest classifier produces 89.06% accuracy in classifying four basic
affective states.
