Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models

4 Jun 2020Yun LuoLi-Zhen ZhuZi-Yu WanBao-Liang Lu

The data scarcity problem in emotion recognition from electroencephalography (EEG) leads to difficulty in building an affective model with high accuracy using machine learning algorithms or deep neural networks. Inspired by emerging deep generative models, we propose three methods for augmenting EEG training data to enhance the performance of emotion recognition models... (read more)

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