A high-performance speech neuroprosthesis

Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into tex

Speech-related dorsal motor cortex activity does not interfere with iBCI cursor control.

ObjectiveSpeech-related neural modulation was recently reported in 'arm/hand' area of human dorsal motor cortex that is used as a signal source for intracortical brain-computer interfaces (iBCIs). This raises the concern that speech-related modulation might deleteriously affect the decoding of arm movement intentions, for instance by affecting velocity command outputs. This study sought to clarify whether or not speaking would interfere with ongoing iBCI use.ApproachA participant in the BrainGate2 iBCI clinical trial used an iBCI to control a computer cursor; spoke short words in a stand-alone speech task; and spoke short words during ongoing iBCI use. We examined neural activity in all three behaviors and compared iBCI performance with and without concurrent speech.Main resultsDorsal motor cortex firing rates modulated strongly during stand-alone speech, but this activity was largely attenuated when speaking occurred during iBCI cursor control using attempted arm movements. 'Decoder-potent' projections of the attenuated speech-related neural activity were small, explaining why cursor task performance was similar between iBCI use with and without concurrent speaking.SignificanceThese findings indicate that speaking does not directly interfere with iBCIs that decode attempted arm movements. This suggests that patients who are able to speak will be able to use motor cortical-driven computer interfaces or prostheses without needing to forgo speaking while using these devices

Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus.

ObjectiveTo evaluate the potential of intracortical electrode array signals for brain-computer interfaces (BCIs) to restore lost speech, we measured the performance of decoders trained to discriminate a comprehensive basis set of 39 English phonemes and to synthesize speech sounds via a neural pattern matching method. We decoded neural correlates of spoken-out-loud words in the 'hand knob' area of precentral gyrus, a step toward the eventual goal of decoding attempted speech from ventral speech areas in patients who are unable to speak.ApproachNeural and audio data were recorded while two BrainGate2 pilot clinical trial participants, each with two chronically-implanted 96-electrode arrays, spoke 420 different words that broadly sampled English phonemes. Phoneme onsets were identified from audio recordings, and their identities were then classified from neural features consisting of each electrode's binned action potential counts or high-frequency local field potential power. Speech synthesis was performed using the 'Brain-to-Speech' pattern matching method. We also examined two potential confounds specific to decoding overt speech: acoustic contamination of neural signals and systematic differences in labeling different phonemes' onset times.Main resultsA linear decoder achieved up to 29.3% classification accuracy (chance = 6%) across 39 phonemes, while an RNN classifier achieved 33.9% accuracy. Parameter sweeps indicated that performance did not saturate when adding more electrodes or more training data, and that accuracy improved when utilizing time-varying structure in the data. Microphonic contamination and phoneme onset differences modestly increased decoding accuracy, but could be mitigated by acoustic artifact subtraction and using a neural speech onset marker, respectively. Speech synthesis achieved r = 0.523 correlation between true and reconstructed audio.SignificanceThe ability to decode speech using intracortical electrode array signals from a nontraditional speech area suggests that placing electrode arrays in ventral speech areas is a promising direction for speech BCIs