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Date
2020-12-12Type
- Other Conference Item
ETH Bibliography
yes
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Abstract
Brain decoding, understood as the process of mapping brain activities to the stimuli that generated them, has been an active research area in the last years. In the case of language stimuli, recent studies have shown that it is possible to decode fMRI scans into an embedding of the word a subject is reading. However, such word embeddings are designed for natural language processing tasks rather than for brain decoding. Therefore, they limit our ability to recover the precise stimulus. In this work, we propose to directly classify an fMRI scan, mapping it to the corresponding word within a fixed vocabulary. Unlike existing work, we evaluate on scans from previously unseen subjects. We argue that this is a more realistic setup and we present a model that can decode fMRI data from unseen subjects. Our model achieves 5.22% Top-1 and 13.59% Top-5 accuracy in this challenging task, significantly outperforming all the considered competitive baselines. Show more
Publication status
publishedExternal links
Book title
Medical Imaging Meets NeurIPS Workshop (MED-NeurIPS 2020). Accepted AbstractsPublisher
MED-NeurIPS 2020Event
Organisational unit
03604 - Wattenhofer, Roger / Wattenhofer, Roger
Notes
Extended abstract. Conference lecture held on December 12, 2020. Due to the Coronavirus (COVID-19) the conference was conducted virtually.More
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ETH Bibliography
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