EZSL-GAN: EEG-based Zero-Shot Learning approach using a Generative Adversarial Network

Abstract

Recent studies show that deep neural network can be effective for learning EEG-based classification network. In particular, Recurrent Neural Networks (RNN) show competitive performance to learn the sequential information of the EEG signals. However, none of the previous approaches considers recognizing the unknown EEG signals which have never been seen in the training dataset. In this paper, we first propose a new scheme for Zero-Shot EEG signal classification. Our EZSL-GAN has three parts. The first part is an EEG encoder network that generates 128-dim of EEG features using a Gated Recurrent Unit (GRU). The second part is a Generative Adversarial Network (GAN) that can tackle the problem for recognizing unknown EEG labels with a knowledge base. The third part is a simple classification network to learn unseen EEG signals from the fake EEG features which are generated from the learned Generator. We evaluate our method on the EEG dataset evoked from 40 classes visual object stimuli. The experimental results show that our EEG encoder achieves an accuracy of 95.89%. Furthermore, our Zero-Shot EEG classification method reached an accuracy of 39.65% for the ten untrained EEG classes. Our experiments demonstrate that unseen EEG labels can be recognized by the knowledge base.