Decoding spoken words using local field potentials recorded from the cortical surface
Open accessAbstract
Pathological conditions such as amyotrophic lateral sclerosis or damage to the brainstem can leave patients severely paralyzed but fully aware, in a condition known as 'locked-in syndrome'. Communication in this state is often reduced to selecting individual letters or words by arduous residual movements. More intuitive and rapid communication may be restored by directly interfacing with language areas of the cerebral cortex. We used a grid of closely spaced, nonpenetrating micro-electrodes to record local field potentials (LFPs) from the surface of face motor cortex and Wernicke's area. From these LFPs we were successful in classifying a small set of words on a trial-by-trial basis at levels well above chance. We found that the pattern of electrodes with the highest accuracy changed for each word, which supports the idea that closely spaced micro-electrodes are capable of capturing neural signals from independent neural processing assemblies. These results further support using cortical surface potentials (electrocorticography) in brain-computer interfaces. These results also show that LFPs recorded from the cortical surface (micro-electrocorticography) of language areas can be used to classify speech-related cortical rhythms and potentially restore communication to locked-in patients.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [8]
Methods §2.1 describes a patient undergoing ECoG monitoring for epilepsy, and §2.2 describes recorded spoken-word trials selected for subsequent decoding analysis. This paper therefore exemplifies speech-neuroprosthesis research using data from a speaking clinical patient. It does not establish that such studies constitute the majority of efforts, and its participant had medically refractory epilepsy rather than being unqualifiedly able-bodied. (LLM, conf=0.86)
Experimental — accuracy not yet measuredEarly 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 [5]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 73 · Cited by 235
- Authors
- Kellis, S., Miller, K., Thomson, K., Brown, R., House, P., & Greger, B.
- Affiliations
- University of Utah (US)
- University of Washington (US)
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2010-09-01
- Volume
- 7
- Issue
- 5
- Pages
- 056007
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- PMC
- PMC2970568
- OpenAlex
- W1994711727
- Cited by
- 235
- References
- 73
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 11 · 2023: 10 · 2022: 15 · 2021: 10 · 2020: 19 · 2019: 17 · 2018: 25 · 2017: 13 · 2016: 26 · 2015: 12 · 2014: 13 · 2013: 15 · 2012: 15
- FWCI
- 9.31
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neural Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Funders
- National Science Foundation (9986866)
- National Institutes of Health
- Defense Advanced Research Projects Agency
- National Eye Institute (R01EY019363, P30EY014800)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- locked-in syndrome, word decoding, local field potentials, electrocorticography, microelectrodes, Wernicke's area, brain-computer interface, language areas, communication restoration
- MeSH terms
- Acoustic Stimulation, Electroencephalography, Humans, Male, Motor Cortex, Speech, Temporal Lobe, Evoked Potentials, Motor
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
