LAUSR

Electroencephalogram signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by (1) the distribution of the data used, (2) consider of differences in participant characteristics, and (3) consider the characteristics of the Electroencephalogram signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: (1) What factors need to be considered to generate and distribute Electroencephalogram data? (2) How can Electroencephalogram signals be generated with consideration of differences in participant characteristics? And (3) How do Electroencephalogram signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in Electroencephalogram signals-based emotion recognition research. These include (a) determine robust methods for imbalanced Electroencephalogram signals data, (b) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, (c) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the Electroencephalogram signals, (d) determine the robust architecture of the Capsule Network method to overcome the loss of knowledge information and apply it in more diverse data set.