ArticleImaging neuroscience (Cambridge, Mass.)2024
Through their eyes: Multi-subject brain decoding with simple alignment techniques.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- NeuroFusion: A Unified Framework for Generalized Visual Stimulus Decoding from fMRI Across Datasets and Subjects.Neuroinformatics · 2026Article
- MIRAGE: Robust multi-modal architectures translate fMRI-to-image models from vision to mental imagery.PLoS computational biology · 2026Article
- Decoding real-world visual scenes from alpha and gamma band flicker evoked oscillations in human EEG.Scientific reports · 2026Article
- Reconstructing music perception from brain activity using a prior guided diffusion model.Scientific reports · 2025Article
- Evidence for compositionality in fMRI visual representations via Brain Algebra.Communications biology · 2025Article
- Semantic language decoding across participants and stimulus modalities.Current biology : CB · 2025Article
- Innovating beyond electrophysiology through multimodal neural interfaces.Nature reviews electrical engineering · 2025Article
- Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics.Communications biology · 2024Article
- Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics.bioRxiv : the preprint server for biology · 2024Article
- A modular semantic-structural pipeline for visual decoding from primate spiking data via selective temporal integration.Imaging neuroscience (Cambridge, Mass.)Article
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5 authors.
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Abstract
To-date, brain decoding literature has focused on single-subject studies, that is, reconstructing stimuli presented to a subject under fMRI acquisition from the fMRI activity of the same subject. The objective of this study is to introduce a generalization technique that enables the decoding of a subject's brain based on fMRI activity of another subject, that is, cross-subject brain decoding. To this end, we also explore cross-subject data alignment techniques. Data alignment is the attempt to register different subjects in a common anatomical or functional space for further and more general analysis. We utilized the Natural Scenes Dataset, a comprehensive 7T fMRI experiment focused on vision of natural images. The dataset contains fMRI data from multiple subjects exposed to 9,841 images, where 982 images have been viewed by all subjects. Our method involved training a decoding model on one subject's data, aligning new data from other subjects to this space, and testing the decoding on the second subject based on information aligned to the first subject. We also compared different techniques for fMRI data alignment, specifically ridge regression, hyper alignment, and anatomical alignment. We found that cross-subject brain decoding is possible, even with a small subset of the dataset, specifically, using the common data, which are around
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