Print Email Facebook Twitter Investigating Effects of Participant Variation on Performance of Visual Stimuli Reconstruction From fMRI Signals Using Machine Learning Title Investigating Effects of Participant Variation on Performance of Visual Stimuli Reconstruction From fMRI Signals Using Machine Learning Author Zheng, Quan (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Zhang, X. (mentor) Tömen, N. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-27 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. Subject Machine Learning (ML)Image ReconstructionImage Similarity Evaluation To reference this document use: http://resolver.tudelft.nl/uuid:1dd399e3-f90c-4000-9375-7cc94ad163be Part of collection Student theses Document type bachelor thesis Rights © 2023 Quan Zheng Files PDF BachelorThesis.pdf 799.21 KB Close viewer /islandora/object/uuid:1dd399e3-f90c-4000-9375-7cc94ad163be/datastream/OBJ/view