| Abstrak/Abstract |
Emotions play an essential role in human social
interactions. Its importance has sparked research on emotion
recognition mainly based on electroencephalogram signals.
However, differences in individual characteristics significantly
affect the electroencephalogram signal pattern and impact the
emotion recognition process. Several studies have used the
baseline reduction approach with the Difference method to
represent the differences in individual characteristics on
electroencephalogram signals. On the other hand, the baseline
reduction process on signal data, in general, can also use the
Relative Difference and Fractional Difference methods.
Therefore, the contribution of this research is to compare the
performance of the three baseline reduction methods on
emotion recognition based on electroencephalogram signals. In
this study, feature extraction and representation were also
carried out using Differential Entropy and 3D Cube.
Furthermore, Convolutional Neural Network and Decision Tree
methods are used to classify emotions. The experimental results
using the DEAP dataset show that the Relative Difference and
Fractional Difference methods are superior in reducing the
baseline electroencephalogram signal compared to the
Difference method. In addition, the Relative Difference and
Fractional Difference methods produce a smoother
electroencephalogram signal pattern in the baseline reduction
process.
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