Predicting naming responses based on pre-articulatory electrical activity in individuals with aphasia
Open accessAbstract
Objective To investigate whether pre-articulatory neural activity could be used to predict correct vs. incorrect naming responses in individuals with post-stroke aphasia.Methods We collected 64-channel high density electroencephalography (hdEEG) data from 5 individuals with chronic post-stroke aphasia (2 female/3 male, median age: 54 years) during naming of 80 concrete images. We applied machine learning on continuous wavelet transformed hdEEG data separately for alpha and beta energy bands (200 ms pre-stimulus to 1500 ms post-stimulus, but before articulation), and determined whether electrode/time-range/energy (ETE) combinations were predictive of correct vs incorrect responses for each participant.Results The five participants correctly named between 30% and 70% of the 80 stimuli correctly. We observed that pre-articulatory scalp EEG ETE combinations could predict correct vs incorrect responses with accuracies ranging from 63% to 80%. For all but one participant, the prediction accuracies were statistically better than chance.Conclusions Our findings indicate that pre-articulatory neural activity may be used to predict correct vs incorrect naming responses for some individuals with aphasia.Significance The individualized pre-articulatory neural pattern associated with correct naming responses could be used to both predict naming problems in aphasia and lead to the development of brain stimulation strategies for treatment.
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Wilmskoetter et al. (33) predicted naming task performance from brain electrical activity before pronunciation, with model AUC ranging from 0.62 to 0.75.
“Wilmskoetter et al. (33) predicted naming task performance from brain electrical activity before pronunciation, and the model’s AUC ranged from 0.62 to 0.75”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [33]
The Results and Conclusions say that pre-articulatory EEG activity was used to predict correct versus incorrect naming responses in individuals with aphasia. Our copy reports prediction accuracies of 63%–80% and discusses AUROC, but the passages found do not establish the claimed AUROC range of 0.62–0.75; accuracy is a different measure. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTwelve included studies mainly used accuracy, AUC, F1-score, precision, recall, and other indicators to assess model performance.
“Twelve studies (19, 20, 22, 24, 25, 27–30, 33, 34, 37) mainly used accuracy, AUC, F1-score, precision, recall, and other indicators.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [33]
Two studies (25, 33) used electroencephalogram indicators to evaluate the neural activity level.
“The other two studies (25, 33) used electroencephalogram indicators to evaluate the neural activity level.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [33]
The Wilmskoetter et al. (33) model indicated moderate predictive ability.
“indicating moderate predictive ability.”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [33]
Existing models used to monitor symptoms are mostly based on restricted task scenarios, such as analyzing speech intelligibility (31) or predicting naming accuracy (33) through picture naming tasks.
“The existing models used to monitor symptoms are mostly based on restricted task scenarios, such as analyzing speech intelligibility (31) or predicting naming accuracy (33) through picture naming tasks”
From Machine learning models in post-stroke aphasia: a scoping review (2026) · cites this paper as [33]
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“The other two studies (25, 33) used electroencephalogram indicators to evaluate the neural activity level.”
Influential
- Authors
- Wilmskoetter, J., Del Gaizo, J., Phillip, L., Behroozmand, R., Gleichgerrcht, E., Fridriksson, J., Riley, E., & Bonilha, L.
- Affiliations
- Medical University of South Carolina (US)
- University of South Carolina (US)
- Syracuse University (US)
- Corresponding author
- Wilmskoetter, Janina
- ORCID
- Venue
- Clinical Neurophysiology
- Publisher
- Elsevier BV
- Published
- 2019-11
- Volume
- 130
- Issue
- 11
- Pages
- 2153-2163
- ISSN
- 1388-2457 (ISSN-L)
- PMC
- PMC6935719
- OpenAlex
- W2970570354
- Cited by
- 9
- References
- 26
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 5
- FWCI
- 0.73
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Clinical Neurophysiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- American Heart Association
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Keywords
- post-stroke aphasia, response prediction, high-density EEG, wavelet analysis, chronic aphasia
- MeSH terms
- Adult, Aphasia, Brain, Brain Mapping, Electroencephalography, Female, Humans, Male, Middle Aged, Retrospective Studies, Speech, Stroke
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