Online internal speech decoding from single neurons in a human participant
Open accessAbstract
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.
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Training brain-computer interface systems on word classification holds particular promise for speech prosthesis and neuropsychological rehabilitation, particularly when based on electrocorticography and bimodal datasets.
“Training brain-computer interface systems on word classification holds particular promise for speech prosthesis and neuropsychological rehabilitation [88], particularly when based on methods such as electrocorticography [89] and the use of bimodal datasets (e.g., fMRI combined with EEG) [90].”
From Inner speech as language process and cognitive tool (2023) · cites this paper as [88]
The Introduction describes speech BMIs as a promising way to restore communication, and the Discussion reports online word classification in one participant. The paper does not establish their promise for neuropsychological rehabilitation more broadly. Electrocorticography and bimodal datasets are attached to separate citations in the citing sentence, so they are not judged against this source. (LLM, conf=0.85)
Experimental — accuracy not yet measuredMost early studies in individuals with vocal-tract paralysis involve only single participants.
“Given that most early studies in individuals with vocal-tract paralysis involve only single participants12–16,64,95,120, great care is needed when comparing results, as the broad spectrum of disease presentations is not sampled”
From The speech neuroprosthesis (2024) · cites this paper as [64]
The Introduction and Experimental model and subject details describe a study of one participant. But the Discussion calls him “speech-abled,” so this paper does not provide an example of a study in an individual with vocal-tract paralysis. It also does not establish that most early studies used single participants. (LLM, conf=0.90)
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- Authors
- Wandelt, S. K., Bjånes, D. A., Pejsa, K., Lee, B., Liu, C., & Andersen, R. A.
- Affiliations
- California Institute of Technology (US)
- University of Southern California (US)
- Corresponding author
- Wandelt, Sarah K.
- Venue
- medRxiv
- Publisher
- openRxiv
- Published
- 2022-11-05
- OpenAlex
- W4308302840
- Cited by
- 17
- References
- 53
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 5 · 2023: 5
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- medRxiv (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke (U01NS098975)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Keywords
- single-unit activity, supramarginal gyrus, primary somatosensory cortex, tetraplegia, phonetic encoding, pseudowords, word reading
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