  Emotion is an intricate physiological response that plays a crucial role in
how we respond and cooperate with others in our daily affairs. Numerous
experiments have been evolved to recognize emotion, however still require
exploration to intensify the performance. To enhance the performance of
effective emotion recognition, this study proposes a subject-dependent robust
end-to-end emotion recognition system based on a 1D convolutional neural
network (1D-CNN). We evaluate the
SJTU\footnote{\href{https://en.wikipedia.org/wiki/Shanghai_Jiao_Tong_University}{Shanghai
Jiao Tong University(SJTU)}} Emotion EEG Dataset SEED-V with five emotions
(happy, sad, neural, fear, and disgust). To begin with, we utilize the Fast
Fourier Transform (FFT) to decompose the raw EEG signals into six frequency
bands and extract the power spectrum feature from the frequency bands. After
that, we combine the extracted power spectrum feature with eye movement and
differential entropy (DE) features. Finally, for classification, we apply the
combined data to our proposed system. Consequently, it attains 99.80\% accuracy
which surpasses each prior state-of-the-art system.
