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Data augmentation for enhancing EEG-based emotion recognition with deep generative models.

Yun Luo1, Li-Zhen Zhu1, Zi-Yu Wan1

  • 1Center for Brain-like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai 200240, People's Republic of China.

Journal of Neural Engineering
|October 14, 2020
PubMed
Summary

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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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This summary is machine-generated.

Data scarcity in electroencephalography (EEG) emotion recognition is addressed by new generative models. Selective WGAN (sWGAN) and other methods augment EEG data, significantly improving affective model accuracy.

Area of Science:

  • Neuroscience
  • Machine Learning
  • Data Science

Background:

  • Emotion recognition from electroencephalography (EEG) faces challenges due to data scarcity, hindering the development of accurate machine learning and deep neural network models.
  • Deep generative models offer potential solutions for augmenting limited EEG datasets.

Purpose of the Study:

  • To address the data scarcity problem in EEG-based emotion recognition by proposing novel data augmentation methods.
  • To enhance the performance of affective models through improved EEG training datasets.

Main Methods:

  • Proposed three data augmentation methods: conditional Wasserstein GAN (cWGAN), selective VAE (sVAE), and selective WGAN (sWGAN).
  • Employed full and partial usage strategies for data augmentation, with partial usage selecting only high-quality generated data.

Related Experiment Videos

Last Updated: Dec 5, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.9K
  • Generated realistic EEG data in power spectral density and differential entropy forms for augmentation.

    • Main Results:

    • Systematic experiments on SEED and DEAP EEG datasets demonstrated the effectiveness of the proposed methods.
    • Generative model-based augmentation outperformed existing methods like conditional VAE, Gaussian noise, and rotational augmentation.
    • Optimal performance was achieved when the number of generated data points was less than 10 times the original dataset size.

    Conclusions:

    • The selective WGAN (sWGAN) method significantly enhances the performance of EEG-based emotion recognition models.
    • Data augmentation using generative models is a viable strategy to overcome data scarcity in affective computing.