Enhancing the Classification of EEG Signals using Wasserstein Generative Adversarial Networks

Collecting EEG signal data during a human visual recognition task is a costly and time-consuming process. However, training good classification models usually requires a large amount of quality data. We propose a data augmentation method based on Generative Adversarial Networks (GANs) to generate artificial EEG signals from existing data, in order to improve classification performance on data collected during a visual recognition task. We evaluate the quality of the artificially generated signal in terms of the accuracy of a Convolutional Neural Network-based classifier that uses both real and augmented data to classify the outcome of the cognitive task. The preliminary results suggest that the introduction of artificially generated signals have a positive effect on the performance of the classifier. Moreover, we provide a method to quantify the level of information which indicates that the generated signals indeed follow the properties of the real ones.