Abstract Details

Title Using an Intracortical Brain Computer Interface to Decode Contextual Modulation from Speech

Topic Neuro-rehabilitation

Presentation(s) P7 - Poster Session 7 (11:45 AM-12:45 PM)

Poster/Presentation
Number 11-006

Objective To understand how contextual elements of speech are encoded in human motor cortex, toward the development of an intracortical brain-computer interface (iBCI) for speech restoration.

Background Loss of communication is one of the most disabling symptoms of ALS and other neurologic conditions that cause paralysis. Recently, iBCIs have been used to decode the neural activity associated with intended phoneme production to restore communication. While these neuroprostheses accurately render users’ intended speech content, little is known regarding how cortex encodes contextual speech elements, such as volume, prosody, or inflection.

Design/Methods

This research is conducted under an IDE from FDA and permission from the MGH IRB. A 39-year-old with quadriplegia (with preserved speech) from cervical spinal cord injury had two 96-channel microelectrode arrays placed chronically in left precentral gyrus. During recording sessions at his home, the participant read sentences by either whispering, speaking, or shouting (based on an onscreen cue) while neural activity was recorded. Neural signals were preprocessed and binned into 20ms blocks; power within the 250-5000Hz band and threshold-crossing spiking events were used for analyses.

Results The participant read 40 sentences at each of three volumes. Simultaneous audio recordings were used to determine the onset/offset of each spoken word. Using word-aligned neural data, we trained a multi-layer recurrent neural network to predict, in an offline analysis of held-out testing data, (1) the categorical volume class and (2) the RMS amplitude of each spoken word. Word volume category could be predicted with 77.7% accuracy from intracortical signals alone and the model accounted for 42.1% of the variance in recorded amplitude.

Conclusions

In this preliminary study, an iBCI system was used to classify speech volume directly from motor cortical neural activity. Ongoing work focusses on expanding the range of contextual modulations decoded from cortex to restore fluent speech-based communication to people with severe dysarthria and anarthria.

Authors/Disclosures
Daniel Rubin, MD, PhD (Massachusetts General Hospital, Neurology) PRESENTER	The institution of Dr. Rubin has received research support from Harvard Catalyst. The institution of Dr. Rubin has received research support from NIDCD. The institution of Dr. Rubin has received research support from AHA. Dr. Rubin has received intellectual property interests from a discovery or technology relating to health care.
Claire Nicolas (Massachusetts General Hospital)	No disclosure on file
John Simeral (Dept Veterans Affairs and Brown Univ.)	No disclosure on file
Sydney Cash, MD (Massachusetts General Hospital)	Dr. Cash has received stock or an ownership interest from Beacon Biosignals.
Leigh R. Hochberg, MD, PhD, FAAN (Massachusetts General Hospital)	The institution of Dr. Hochberg has received research support from The MGH Translational Research Center has a clinical research support agreement (CRSA) with Axoft, Neuralink, Neurobionics, Precision Neuro, Synchron, and Reach Neuro, for which LRH provides consultative input. LRH is a non-compensated member of the Board of Directors of a nonprofit assistive communication device technology foundation (Speak Your Mind Foundation). Mass General Brigham (MGB) is convening the Implantable Brain-Computer Interface Collaborative Community (iBCI-CC); charitable gift agreements to MGB, including those received to date from Paradromics, Synchron, Precision Neuro, Neuralink, and Blackrock Neurotech, support the iBCI-CC, for which LRH provides effort.. The institution of Dr. Hochberg has received research support from Dr. Hochberg also receives research support from the US Department of Veterans Affairs and the National Institutes of Health.. Dr. Hochberg has received intellectual property interests from a discovery or technology relating to health care.

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