Online internal speech decoding from single neurons in a human participant

Abstract

Speech brain-machine interfaces (BMI’s) translate brain signals into words or audio outputs, enabling communication for people having lost their speech abilities due to diseases or injury. While important advances in vocalized, attempted, and mimed speech decoding have been achieved, results for internal speech decoding are sparse, and have yet to achieve high functionality. Notably, it is still unclear from which brain areas internal speech can be decoded. In this work, a tetraplegic participant with implanted microelectrode arrays located in the supramarginal gyrus (SMG) and primary somatosensory cortex (S1) performed internal and vocalized speech of six words and two pseudowords. We found robust internal speech decoding from SMG single neuron activity, achieving up to 91% classification accuracy during an online task (chance level 12.5%). Evidence of shared neural representations between internal speech, word reading, and vocalized speech processes were found. SMG represented words in different languages (English/ Spanish) as well as pseudowords, providing evidence for phonetic encoding. Furthermore, our decoder achieved high classification with multiple internal speech strategies (auditory imagination/ visual imagination). Activity in S1 was modulated by vocalized but not internal speech, suggesting no articulator movements of the vocal tract occurred during internal speech production. This works represents the first proof-of-concept for a high-performance internal speech BMI.