Emotion awareness is one of the most important subjects in the
field of affective computing. Using nonverbal behavioral methods such as
recognition of facial expression, verbal behavioral method, recognition of
speech emotion, or physiological signals-based methods such as recognition

of emotions based on electroencephalogram (EEG) can predict human emo-
tion. However, it is notable that data obtained from either nonverbal or ver-
bal behaviors are indirect emotional signals suggesting brain activity. Unlike

the nonverbal or verbal actions, EEG signals are reported directly from the
human brain cortex and thus may be more effective in representing the inner
emotional states of the brain. Consequently, when used to measure human
emotion, the use of EEG data can be more accurate than data on behavior.
For this reason, the identification of human emotion from EEG signals has

become a very important research subject in current emotional brain-
computer interfaces (BCIs) aimed at inferring human emotional states based

on the EEG signals recorded. In this paper, a hybrid deep learning approach
has proposed using CNN and a long short-term memory (LSTM) algorithm
is investigated for the purpose of automatic classification of epileptic disease

from EEG signals. The signals have been processed by CNN for feature ex-
traction from runtime environment while LSTM has used for classification

of entire data. Finally, system demonstrates each EEG data file as normal or

epileptic disease. In this research to describes a state of art for effective epi-
leptic disease detection prediction and classification using hybrid deep learn-
ing algorithms. This research demonstrates a collaboration of CNN and

LSTM for entire classification of EEG signals in numerous existing systems.