Predicting naming responses based on pre-articulatory electrical activity in individuals with aphasia

Wilmskoetter, Janina; Gaizo, John Del; Phillip, Lorelei; Behroozmand, Roozbeh; Gleichgerrcht, Ezequiel; Fridriksson, Julius; Riley, Ellyn A.; Bonilha, Leonardo

doi:10.1016/j.clinph.2019.08.011

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Although there have been a number of recent studies on lesions associated with production of speech errors in aphasia ( Mirman et al, 2015 , Schwartz et al, 2009 , Schwartz et al, 2012 , Singh et al, 2018 , Stark et al, 2019 , Wilmskoetter et al, 2019 ), there have not been many studies on lesions associated with monitoring speech errors. Only a few studies to date have systematically investigated the neural correlates of speech error monitoring in aphasia.…”

Section: Introductionmentioning

confidence: 99%

Structural disconnection of the posterior medial frontal cortex reduces speech error monitoring

McCall

Dickens

Mandal

et al. 2022

NeuroImage: Clinical

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Section: Introductionmentioning

confidence: 99%

Structural disconnection of the posterior medial frontal cortex reduces speech error monitoring

McCall

Dickens

Mandal

et al. 2022

NeuroImage: Clinical

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“…Nevertheless, recent studies are promising for EEG use in decoding speech in individuals with brain damage, especially if many electrodes are available (so-called high-density EEG [hdEEG] with 3 64 scalp channels). 16,17…”

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confidence: 99%

“…Nevertheless, recent studies are promising for EEG use in decoding speech in individuals with brain damage, especially if many electrodes are available (so-called highdensity EEG [hdEEG] with 3 64 scalp channels). 16,17 The aim of this study is to assess the extent to which hdEEG combined with machine learning can be used to decode prearticulatory brain activity associated with language production. We conducted a study using machine learning with hdEEG recordings of healthy individuals to predict animate and inanimate semantic categories of naming responses.…”

mentioning

confidence: 99%

Semantic Categorization of Naming Responses Based on Prearticulatory Electrical Brain Activity

Wilmskoetter

Roth

McDowell

et al. 2022

Journal of Clinical Neurophysiology

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Purpose: Object naming requires visual decoding, conceptualization, semantic categorization, and phonological encoding, all within 400 to 600 ms of stimulus presentation and before a word is spoken. In this study, we sought to predict semantic categories of naming responses based on prearticulatory brain activity recorded with scalp EEG in healthy individuals. Methods:We assessed 19 healthy individuals who completed a naming task while undergoing EEG. The naming task consisted of 120 drawings of animate/inanimate objects or abstract drawings. We applied a one-dimensional, two-layer, neural network to predict the semantic categories of naming responses based on prearticulatory brain activity.Results: Classifications of animate, inanimate, and abstract responses had an average accuracy of 80%, sensitivity of 72%, and specificity of 87% across participants. Across participants, time points with the highest average weights were between 470 and 490 milliseconds after stimulus presentation, and electrodes with the highest weights were located over the left and right frontal brain areas.Conclusions: Scalp EEG can be successfully used in predicting naming responses through prearticulatory brain activity. Interparticipant variability in feature weights suggests that individualized models are necessary for highest accuracy. Our findings may inform future applications of EEG in reconstructing speech for individuals with and without speech impairments.

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Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network

Wang

Zhou

et al. 2022

Front. Comput. Sci.

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Predicting naming responses based on pre-articulatory electrical activity in individuals with aphasia

Cited by 7 publications

References 23 publications

Structural disconnection of the posterior medial frontal cortex reduces speech error monitoring

Structural disconnection of the posterior medial frontal cortex reduces speech error monitoring

Semantic Categorization of Naming Responses Based on Prearticulatory Electrical Brain Activity

Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network

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