Meta-Learning for Personalized Emotion Prediction from EEG Signals

Abstract

We have developed an emotion induction system that predicts participants' emotions from EEG and provides personalized music. Although it is important to secure the amount of data for training emotion prediction models, it is a burden for the participants to record EEG data for a long time. In this study, we aim to investigate a training method for using a small amount of EEG data. We propose using meta-learning that trains a pre-training model that can be adapted easily to each participant. As a result of predicting valence and arousal from EEG, the method with meta-learning showed a significantly lower prediction error than the method without meta-learning (p<.001).