It is very difficult to distinguish emotions from each other.
EEG is most often used for distinguishing emotions and diagnosing epilepsy, sleep disorders, coma, brain death, etc. [2, 3, 4].
In addition to distinguishing emotions, this study proposes a new reliable working area to analyze EEG signal to extract features.
The experimental result indicates that the valence value is most effective to distinguish emotions on a gamma band of EEG.
According to James, what distinguishes emotions is the fact that each involves the perception of a unique set of bodily changes.
Speech signal energy, which refers to the volume or intensity of speech, also provides information that can be used to distinguish emotions (i.e., joy and anger increase energy levels in comparison to other emotional states).
This feature makes them a perfect means for learning to distinguish emotions.
It will focus on acquiring knowledge of depression and anxiety, learning to recognize and distinguish emotions, dealing with difficult situations, solving social problems, and creating and using a supportive social network.
While it is generally agreed that emotions "expressed" in the music must be distinguished from emotions "felt" in the listener (Gabrielson and Juslin 2003), many questions remain about the essential characteristics of the complex bodily, cognitive, and emotional reactions evoked by music.
While the categorical approach focuses on the distinct characteristics that distinguish the emotions from each other, in the dimensional approach, emotions are expressed on a two-dimensional system according to two axes such as valence and arousal.
