There is a growing need for sparse representational formats of human
affective states that can be utilized in scenarios with limited computational
memory resources. We explore whether representing neural data, in response to
emotional stimuli, in a latent vector space can serve to both predict emotional
states as well as generate synthetic EEG data that are participant- and/or
emotion-specific. We propose a conditional variational autoencoder based
framework, EEG2Vec, to learn generative-discriminative representations from EEG
data. Experimental results on affective EEG recording datasets demonstrate that
our model is suitable for unsupervised EEG modeling, classification of three
distinct emotion categories (positive, neutral, negative) based on the latent
representation achieves a robust performance of 68.49%, and generated synthetic
EEG sequences resemble real EEG data inputs to particularly reconstruct
low-frequency signal components. Our work advances areas where affective EEG
representations can be useful in e.g., generating artificial (labeled) training
data or alleviating manual feature extraction, and provide efficiency for
memory constrained edge computing applications.

本文提出了一种基于条件变分自编码器的 EEG2Vec 框架，可以在具有限计算内存资源的情景下预测情绪状态，并生成特定参与者和 / 或情感的合成 EEG 数据；实验结果表明，该模型能够适用于无监督 EEG 建模，并实现对正面、中性、负面三个情感类别的分类。

通过变分自编码器学习情感 EEG 表示

EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders

Style synthesis attracts great interests recently, while few works focus on
its dual problem "style separation". In this paper, we propose the Style
Separation and Synthesis Generative Adversarial Network (S3-GAN) to
simultaneously implement style separation and style synthesis on object
photographs of specific categories. Based on the assumption that the object
photographs lie on a manifold, and the contents and styles are independent, we
employ S3-GAN to build mappings between the manifold and a latent vector space
for separating and synthesizing the contents and styles. The S3-GAN consists of
an encoder network, a generator network, and an adversarial network. The
encoder network performs style separation by mapping an object photograph to a
latent vector. Two halves of the latent vector represent the content and style,
respectively. The generator network performs style synthesis by taking a
concatenated vector as input. The concatenated vector contains the style half
vector of the style target image and the content half vector of the content
target image. Once obtaining the images from the generator network, an
adversarial network is imposed to generate more photo-realistic images.
Experiments on CelebA and UT Zappos 50K datasets demonstrate that the S3-GAN
has the capacity of style separation and synthesis simultaneously, and could
capture various styles in a single model.

本文提出了一种新的深度学习网络 S3-GAN，可以同时实现特定类别物体照片的风格分离和风格合成。该网络结构包括编码器网络、生成器网络和对抗网络，基于前提假设物体照片在流形上，内容和风格是相互独立的，并通过编码器网络实现风格分离，生成器网络实现风格合成，对抗网络生成更加逼真的图像。实验表明，S3-GAN 可以在单个模型中同时捕捉各种风格，具备一定的风格分离和合成能力。