Simultaneous speech and gesture decoding for multimodal communication in paralysis

Stroke and neurodegenerative diseases can impair speech and nonverbal gestures, limiting natural communication. Brain-computer interfaces (BCIs) aim to restore these functions by translating neural activity into commands for external devices, although prior work has primarily focused on decoding speech or gestures in isolation. Here we show that neural signals recorded with a single high-density electrocorticography implant can support simultaneous decoding of speech and gestures in people with paralysis. We first show that isolated upper-limb and orofacial movements can be reliably decoded among three participants. Using parallel speech and gesture decoders, we then enabled participants to control a personalized virtual avatar by attempting speech and gestures simultaneously or in isolation. Training models on both isolated and simultaneous data improved performance across behavioral contexts. These findings demonstrate that one cortical implant can support multi-effector control and provide a step toward BCIs that enable more natural communication for people with paralysis.