Is that real? A multifaceted evaluation of the quality of simulated EEG signals for passive BCI

Abstract

The collection of electroencephalogram (EEG)data is costly, thus it is useful to explore the generation of artificial EEG signals. However, it is difficult to evaluate the quality of these signals. In this work, we generate artificial EEG signals and compare them with real EEG measurements. We analyze the signals in the time and frequency domain and conduct a survey addressed to experts in the field of EEG signal analysis. Finally, we assess whether augmenting EEG data with generated data improves the classification performance. The artificial EEG signals were generated by a progressive Wasserstein Generative Adversarial Network (Wasserstein GAN) with gradient penalty, that was trained on attention recognition data. To investigate the effect of data augmentation, the Shallow Filterbank Common Spatial Pattern network (shallow FBCSPNet) was chosen. The analysis shows that the simulated EEG signals appear realistic in both time and frequency domain. The survey found no significant differences in EEG typical features between real and simulated, but the estimation of noise level tended to be higher for the simulated signals. The data augmentation for the classification resulted in a moderate improvement in accuracy and F-score. Overall, the results show that the quality of the simulated EEG signals is comparable to the quality of the real EEG signals.