Emotion recognition from brain computer interface (EEG) has been studied
extensively for the past few years. Time-frequency analysis is widely used in
the past research; however, a variation of case study determines the brain
signal analysis. In this paper, human emotion from brain waves is recognized
in simple ways by calculating a frequency of signal variation. Entirely 35
healthy subjects from students with age 18-25 years old. The students are
divided into 3 groups; the first group consists of 15 students; the second
group consists of 10 students and the third group consists of 10 students.
Each student takes 4 seconds to test his or her internal emotions. The signal
speed is recorded during those 4 seconds. Based on stimulus time, various
knocks for Z1 and Z2 is observed during a particular time. The experiment
can be reproduced for in upcoming future by following the procedure. There
are two main elements to measure signal speed which are ΔT and gap. ΔT
subject to time differentiation of the changes in time-frequency of Alpha
signals. For an evaluation of this work, there is an available benchmark
database of EEG labeled with emotions; it mentions that emotional strength
can be used as a factor to differentiate between human emotions. The results
of this paper can be compared with previous researches which use the same
device to differentiate between happy and sad emotions in terms of
emotional strength. There is a strong correlation between emotional strength
and frequency, we proved that sad feeling is speedier and beyond steady
compared to happy since the number of ΔV to Z1 which represents sad
emotion of Alpha signals is greater than ΔV to Z2 that represents a happy
feeling in the same time period of the interaction process