TR2018-184
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders
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- , "Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders", International IEEE EMBS Conference on Neural Engineering, DOI: 10.1109/NER.2019.8716897, March 2019.BibTeX TR2018-184 PDF
- @inproceedings{Ozdenizci2019mar,
- author = {Ozdenizci, Ozan and Wang, Ye and Koike-Akino, Toshiaki and Erdogmus, Deniz},
- title = {Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders},
- booktitle = {International IEEE EMBS Conference on Neural Engineering},
- year = 2019,
- month = mar,
- doi = {10.1109/NER.2019.8716897},
- url = {https://www.merl.com/publications/TR2018-184}
- }
- , "Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders", International IEEE EMBS Conference on Neural Engineering, DOI: 10.1109/NER.2019.8716897, March 2019.
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Research Areas:
Abstract:
We introduce adversarial neural networks for representation learning as a novel approach to transfer learning in brain-computer interfaces (BCIs). The proposed approach aims to learn subject-invariant representations by simultaneously training a conditional variational autoencoder (cVAE) and an adversarial network. We use shallow convolutional architectures to realize the cVAE, and the learned encoder is transferred to extract subject-invariant features from unseen BCI users’ data for decoding. We demonstrate a proof-of-concept of our approach based on analyses of electroencephalographic (EEG) data recorded during a motor imagery BCI experiment.