# Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders

## TL;DR

This paper presents a novel transfer learning method for brain-computer interfaces using adversarial variational autoencoders to learn subject-invariant EEG representations, improving decoding across users.

## Contribution

It introduces an adversarial neural network approach with a conditional variational autoencoder for transfer learning in BCIs, emphasizing subject-invariant feature extraction.

## Key findings

- Successful extraction of subject-invariant EEG features
- Improved cross-subject BCI decoding performance
- Proof-of-concept demonstrated on motor imagery EEG data

## 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.