There is major potential for using electroencephalography (EEG) in brain decoding that has been untapped due to the need for large amounts of data. Advances in machine learning have mitigated this need through data augmentation techniques, such as Generative Adversarial Networks (GANs). Here, we gauged the extent to which GANs can augment EEG data to enhance classification performance. Our objectives were to determine which classifiers benefit from GAN-augmented EEG and to estimate the impact of sample sizes on GAN-enhancements. We investigated three classifiers---neural networks, support vector machines, and logistic regressions---across seven sample sizes ranging from 5 to 100 participants. GAN-augmented EEG enhanced classification for neural networks and support vector machines, but not logistic regressions. Further, GAN-enhancements diminished as sample sizes increased---suggesting it is most effective with small samples, which may facilitate research that is unable to collect large amounts of data.

In light of constraints inherent to empirical research, such as finite time and resources, there has been growing interest in using artificial intelligence to streamline the scientific process. However, despite advancements in automating scientific discovery, the implementation of strategies for sampling useful experiments remains a challenge. This metascientific study evaluates different experimental sampling strategies based on their effectiveness in advancing the discovery of linear models of human cognition based on synthetic data. We investigate the hypothesis put forth by Dubova et al. (2022) that random sampling of experiments is more effective than model-driven sampling. Indeed, the results of this study indicate that random sampling is more effective in a majority of cases, and that the underperformance of model-driven strategies can be attributed to a narrow sampling of the design space. Despite limitations in our approach, the work presented offers a novel framework for the metascientific study of autonomous empirical research.