Brain2Word: Decoding Brain Activity for Language Generation
This work addresses brain-computer interfaces for language generation, offering a more realistic setup by evaluating on unseen subjects, but it is incremental as it builds on existing brain decoding methods.
The paper tackles the problem of decoding brain activity from fMRI scans to identify specific words a subject is reading, achieving 5.22% Top-1 and 13.59% Top-5 accuracy on unseen subjects, and uses these decoded words to guide text generation with GPT-2.
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. Furthermore, we use the decoded words to guide language generation with the GPT-2 model. This way, we advance the quest for a system that translates brain activities into coherent text.