IMPROVING BRAIN DECODING METHODS AND EVALUATION

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 the model's 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 with 2.62% Top-1 and 9.76% Top-5 accuracy in this challenging task. Moreover our model can be fine-tuned on data from the test subject to achieve 4.22% Top-1 and 12.87% Top-5 accuracy, significantly outperforming all the considered competitive baselines.