Direct classification of all American English phonemes using signals from functional speech motor cortex
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OBJECTIVE: Although brain-computer interfaces (BCIs) can be used in several different ways to restore communication, communicative BCI has not approached the rate or efficiency of natural human speech. Electrocorticography (ECoG) has precise spatiotemporal resolution that enables recording of brain activity distributed over a wide area of cortex, such as during speech production. In this study, we sought to decode elements of speech production using ECoG. APPROACH: We investigated words that contain the entire set of phonemes in the general American accent using ECoG with four subjects. Using a linear classifier, we evaluated the degree to which individual phonemes within each word could be correctly identified from cortical signal. MAIN RESULTS: We classified phonemes with up to 36% accuracy when classifying all phonemes and up to 63% accuracy for a single phoneme. Further, misclassified phonemes follow articulation organization described in phonology literature, aiding classification of whole words. Precise temporal alignment to phoneme onset was crucial for classification success. SIGNIFICANCE: We identified specific spatiotemporal features that aid classification, which could guide future applications. Word identification was equivalent to information transfer rates as high as 3.0 bits s(-1) (33.6 words min(-1)), supporting pursuit of speech articulation for BCI control.
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Multiple studies have demonstrated that isolated phonemes can be decoded from neural activity.
“Multiple studies have demonstrated that isolated phonemes can be decoded from neural activity17,106,107,118 (Fig. 1; Mugler et al. 2014 and Herff et al. 2015).”
From The speech neuroprosthesis (2024) · cites this paper as [107]
The Introduction reports that earlier studies classified phonemes isolated from words, supporting the general claim. But the Discussion says Mugler et al.’s own study classified individual phonemes within word production, not isolated phonemes, so it does not support citing this study as an example of that specific result. (LLM, conf=0.86)
Experimental — accuracy not yet measuredSpeech-decoding BCIs show promise for rapid communication, but their accuracy and vocabulary size still need improvement for general-purpose use.
“Recently, speech-decoding BCIs have shown exciting promise for restoring rapid communication (e.g. 34,16,17), but their accuracies and vocabulary sizes require further improvement to support general-purpose use.”
From High-performance brain-to-text communication via handwriting (2021) · cites this paper as [34]
Efforts to develop a speech neuroprosthesis are built largely on studies using data that are retrospectively analyzed from able-bodied speakers undergoing electrophysiological monitoring for clinical purposes.
“Efforts to develop a speech neuroprosthesis are built largely on studies using data that are retrospectively analyzed from able-bodied speakers undergoing electrophysiological monitoring for clinical purposes 7-16 .”
From An Accurate and Rapidly Calibrating Speech Neuroprosthesis (2024) · cites this paper as [9]
Early speech BCI demonstrations have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary.
“Early demonstrations, although promising, have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary1–7.”
From A high-performance speech neuroprosthesis (2023) · cites this paper as [7]
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Self-citation
- Authors
- Mugler, E. M., Patton, J. L., Flint, R. D., Wright, Z. A., Schuele, S. U., Rosenow, J., Shih, J. J., Krusienski, D. J., & Slutzky, M. W.
- Affiliations
- University of Illinois Chicago (US)
- Northwestern University (US)
- Mayo Clinic (US)
- Old Dominion University (US)
- Corresponding author
- Mugler, Emily M
- ORCID
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2014-05-19
- Volume
- 11
- Issue
- 3
- Pages
- 035015
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- PMC
- PMC4097188
- OpenAlex
- W2151544568
- Cited by
- 192
- References
- 34
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 22 · 2023: 15 · 2022: 17 · 2021: 16 · 2020: 18 · 2019: 22 · 2018: 19 · 2017: 15 · 2016: 15 · 2015: 6 · 2014: 2
- FWCI
- 7.07
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- ODU Digital Commons (Old Dominion University) (submittedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (R01EB000856, NIBIB/NINDS EB00856)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- phoneme classification, electrocorticography, brain-computer interface, speech production, word recognition, information transfer rate, spatio-temporal features, temporal alignment, phonological structure, communication restoration
- MeSH terms
- Adult, Algorithms, Communication Aids for Disabled, Communication Devices for People with Disabilities, Electroencephalography, Female, Humans, Language, Male, Motor Cortex, Pattern Recognition, Automated, Sound Spectrography, Speech, Speech Acoustics, Speech Production Measurement, Translating, United States, User-Computer Interface, Speech Recognition Software, Brain-Computer Interfaces
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