Summary of Mindbridge: a Cross-subject Brain Decoding Framework, by Shizun Wang et al.
MindBridge: A Cross-Subject Brain Decoding Framework
by Shizun Wang, Songhua Liu, Zhenxiong Tan, Xinchao Wang
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Brain decoding, a key area of neuroscience, seeks to reconstruct stimuli from brain signals acquired through functional magnetic resonance imaging (fMRI). Currently, brain decoding is limited to a per-subject-per-model approach, hindering its applicability. This constraint stems from variability in input dimensions across subjects due to differences in brain size, unique neural patterns influencing sensory information processing, and limited data availability for new subjects. Our novel approach, MindBridge, achieves cross-subject brain decoding using only one model, introducing a biological-inspired aggregation function and cyclic fMRI reconstruction mechanism for subject-invariant representation learning. Experimental results demonstrate competitive image reconstruction accuracy for multiple subjects, surpassing that of subject-specific models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Brain decoding is a way to understand what people are thinking by looking at their brains while they’re doing things. Right now, it’s only good for one person and one specific model. This means we can’t use the same model for someone else. There are three main problems: how big someone’s brain is affects how it works, each person has their own way of processing information, and we don’t have enough data to make new models. We created a new way called MindBridge that can work with different people using just one model. It uses special tricks to make sure the results are the same for everyone. This makes it better than other methods and could be really useful in real-life situations. |
Keywords
» Artificial intelligence » Representation learning
