  The human brain is a complex organ, still completely undiscovered, that
controls almost all the parts of the body. Apart from survival, the human brain
stimulates emotions. Recent research indicates that brain signals can be very
effective for emotion recognition. However, which parts of the brain exhibit
most of the emotions is still under-explored. In this study, we empirically
analyze the contribution of each part of the brain in exhibiting emotions. We
use the DEAP dataset to find the most optimal electrode set which eventually
leads to the effective brain part associated with emotions. We use Fast Fourier
Transformation for effective feature extraction and a 1D-CNN with residual
connection for classification. Though 32 electrodes from the DEAP dataset got
an accuracy of 97.34%, only 12 electrodes (F7, P8, O1, F8, C4, T7, PO3, Fp1,
Fp2, O2, P3, and Fz) achieve 95.81% accuracy. This study also shows that adding
more than 10 electrodes does not improve performance significantly. Moreover,
the frontal lobe is the most important for recognizing emotion.
