Learning Subject-Invariant Representations from Speech-Evoked EEG Using Variational Autoencoders
Author:
Affiliation:
1. KU Leuven, PSI,Dept. of Electrical Engineering (ESAT),Leuven,Belgium
2. KU Leuven, ExpORL,Dept. Neurosciences,Leuven,Belgium
Funder
KU Leuven
European Research Council
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09747297.pdf?arnumber=9747297
Reference23 articles.
1. Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity
2. Subject-Invariant Eeg Representation Learning For Emotion Recognition
3. Latent Factor Decoding of Multi-Channel EEG for Emotion Recognition Through Autoencoder-Like Neural Networks
4. Learning Subject-Generalized Topographical EEG Embeddings Using Deep Variational Autoencoders and Domain-Adversarial Regularization
5. Auto-Encoding Variational Bayes;kingma;arXiv 1312 6114 [cs stat],2014
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4. AudioDiffusion: Generating High-Quality Audios from EEG Signals : Reconstructing Audio from EEG Signals;2023 4th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC);2023-08-18
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