Decoding Continuous Character-based Language from Non-invasive Brain Recordings

Over the past decade, advancements in brain-computer interfaces have demonstrated the feasibility of decoding various forms of communication, such as speech sounds [80, 81], hand gestures [79, 82], articulatory movements [77, 78], and other signals [76] from intracranial recordings. Despite their efficacy, the requirement for invasive brain surgery limits the applicability of these decoding methods to patients with severe impediments in speech or communication due to neurodegenerative diseases, strokes, or traumatic brain injuries. In contrast, non-invasive recordings, particularly those employing functional magnetic resonance imaging (fMRI) [72, 74], magnetoencephalography (MEG) and electroencephalography (EEG) [73], have demonstrated the ability to record rich linguistic information, and decoding natural language from such non-invasive recordings holds the potential for broader applications in both restorative interventions and augmentative technologies. Previous efforts to decode natural language from non-invasive recordings have primarily focused on recognizing letters, words, or fragments within a predetermined set of possibilities [66-69, 72, 73]. A recent breakthrough has demonstrated the feasibility of decoding continuous language from non-invasive recordings of native English speakers [65].