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- W4200379229 abstract "We are studying to realize an emotion induction system that generates music based on emotions predicted in real-time from electroencephalogram (EEG). Since there are individual differences in EEG while listening to music, a model trained from a single participant’s data is expected to provide highly accurate emotion prediction. However, a time-consuming EEG recording is required to avoid data shortage. We need to reduce the recording time to minimize the burden on the participants. Therefore, we train a model which considers the individuality of EEG from multiple participants’ data and fine-tune it from a small amount of a single target’s data. In this paper, we propose a method using meta-learning for pre-training. We compared three methods: two methods using multiple participants’ data (with/without meta-learning) and a method using a single participant’s data. Our proposed method obtained the lowest RMSE (valence: 0.244 and arousal: 0.287). We demonstrate the effectiveness to use meta-learning to train an emotion prediction model, which is a necessary step for constructing the emotion induction system." @default.
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- W4200379229 date "2021-10-18" @default.
- W4200379229 modified "2023-09-28" @default.
- W4200379229 title "Meta-Learning for Emotion Prediction from EEG while Listening to Music" @default.
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- W4200379229 doi "https://doi.org/10.1145/3461615.3486569" @default.
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