A brain–computer interface that decodes continuous language from non-invasive recordings would have many scientific and practical applications. Currently, however, non-invasive language decoders can only identify stimuli from among a small set of words or phrases. Here we introduce a non-invasive decoder that reconstructs continuous language from cortical semantic representations recorded using functional magnetic resonance imaging (fMRI). Given novel brain recordings, this decoder generates intelligible word sequences that recover the meaning of perceived speech, imagined speech and even silent videos, demonstrating that a single decoder can be applied to a range of tasks. We tested the decoder across cortex and found that continuous language can be separately decoded from multiple regions. As brain–computer interfaces should respect mental privacy, we tested whether successful decoding requires subject cooperation and found that subject cooperation is required both to train and to apply the decoder. Our findings demonstrate the viability of non-invasive language brain–computer interfaces.
Wednesday, May 24, 2023
Using AI to decipher words and sentences from brain scans
Things are happening very fast in AI, as this work from Huth and his collaborators shows. Previous work has shown that speeh articulation and other signals can be decoded from invasive intracranial recordings, and they have developed a language decoder that now accomplishs this with non-invasive fMRI. Motivated readers can obtain the detailed description of the work by emailing me. Their abstract: