Investigating Effects of Participant Variation on Performance of Visual Stimuli Reconstruction From fMRI Signals Using Machine Learning

Abstract

Image reconstruction from neural activation data is a field that has been growing in popularity with developments such as neuralink in the brain-machine interface space. To make better decisions when collecting data for this purpose, it is important to know what qualities to optimize for. The present paper investigates the relation between participant variation and visual stimulus reconstruction performance from functional magnetic resonance imaging (fMRI) data, which can guide decisions on whether resources should be spent collecting more data from fewer individuals or vice versa. We conducted performance evaluation on the Self-Supervised Image Reconstruction machine learning architecture proposed by Gaziv et al. using three pixel-wise and two structural image similarity measures. Our results show that reconstructions from one subject's fMRI data consistently performed best across all five performance metrics. However, statistically significant variance in reconstruction performance across subjects was found for only the feature-based similarity index. While the present paper found statistically significant results, we recommend future research to further investigate this notion by employing similar evaluation on other models.