{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mindbridge-a-cross-subject-brain-decoding","title":"MindBridge: A Cross-Subject Brain Decoding Framework","arxiv_id":"2404.07850","date":"2024-04-11","proceeding":"CVPR 2024 1","authors":["Shizun Wang","Songhua Liu","Zhenxiong Tan","Xinchao Wang"],"abstract":"Brain decoding, a pivotal field in neuroscience, aims to reconstruct stimuli from acquired brain signals, primarily utilizing functional magnetic resonance imaging (fMRI). Currently, brain decoding is confined to a per-subject-per-model paradigm, limiting its applicability to the same individual for whom the decoding model is trained. This constraint stems from three key challenges: 1) the inherent variability in input dimensions across subjects due to differences in brain size; 2) the unique intrinsic neural patterns, influencing how different individuals perceive and process sensory information; 3) limited data availability for new subjects in real-world scenarios hampers the performance of decoding models. In this paper, we present a novel approach, MindBridge, that achieves cross-subject brain decoding by employing only one model. Our proposed framework establishes a generic paradigm capable of addressing these challenges by introducing biological-inspired aggregation function and novel cyclic fMRI reconstruction mechanism for subject-invariant representation learning. Notably, by cycle reconstruction of fMRI, MindBridge can enable novel fMRI synthesis, which also can serve as pseudo data augmentation. Within the framework, we also devise a novel reset-tuning method for adapting a pretrained model to a new subject. Experimental results demonstrate MindBridge's ability to reconstruct images for multiple subjects, which is competitive with dedicated subject-specific models. Furthermore, with limited data for a new subject, we achieve a high level of decoding accuracy, surpassing that of subject-specific models. This advancement in cross-subject brain decoding suggests promising directions for wider applications in neuroscience and indicates potential for more efficient utilization of limited fMRI data in real-world scenarios. Project page: https://littlepure2333.github.io/MindBridge","url_abs":"https://arxiv.org/abs/2404.07850v1","url_pdf":"https://arxiv.org/pdf/2404.07850v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mindbridge-a-cross-subject-brain-decoding","repo_url":"https://github.com/littlepure2333/mindbridge","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"brain-decoding","task_name":"Brain Decoding"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"image-reconstruction","task_name":"Image Reconstruction"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2404.07850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07850"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/littlepure2333/mindbridge","reach":null}],"summary":{"ran":3,"unverified":1},"by_repo_kind":{"official":{"samples":4,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":4,"samples":[{"code_sha256_prefix":"6ad20eb220f07b81","entry":"Adapter_Layer","repo":"littlepure2333/mindbridge","repo_kind":"official","path":"src/models.py","file_url":"https://github.com/littlepure2333/mindbridge/blob/HEAD/src/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6ad20eb220f07b81"}},{"code_sha256_prefix":"4d35ccbc4d2a4783","entry":"MindSingle","repo":"littlepure2333/mindbridge","repo_kind":"official","path":"src/models.py","file_url":"https://github.com/littlepure2333/mindbridge/blob/HEAD/src/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"4d35ccbc4d2a4783"}},{"code_sha256_prefix":"1d036d6c2996c558","entry":"ResMLP","repo":"littlepure2333/mindbridge","repo_kind":"official","path":"src/models.py","file_url":"https://github.com/littlepure2333/mindbridge/blob/HEAD/src/models.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1d036d6c2996c558"}},{"code_sha256_prefix":"fe2baa4cb244c541","entry":"MindBridge","repo":"littlepure2333/mindbridge","repo_kind":"official","path":"src/models.py","file_url":"https://github.com/littlepure2333/mindbridge/blob/HEAD/src/models.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"fe2baa4cb244c541"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}