Construction of an Emotion Estimation Model Based on EEG Using MLP

Abstract

With the growth of video streaming and virtual reality, there is a need for services that better match people’s emotional responses. A model can be created to estimate emotions while watching videos and provide media recommendations. EEG signals can be used as input features to estimate latent emotions that are less apparent in facial expressions and gestures. A Multi Layer Perceptron (MLP) can be used for the estimation model, reducing learning time compared to deep learning and gaining generalization ability. The aim is to construct an estimation model using MLP and Power Spectrum Density (PSD) of EEG signals as input features. The average classification performance for 32 individuals was 74.3% for valence and 74.0% for arousal, comparable to a CNN trained using the same input features. We believe that the experimental results show that large networks and convolutional layers are not necessary for EEG-based emotion estimation.