Algorithms for Estimating Time-Locked Neural Response Components in Cortical Processing of Continuous Speech

Objective: The Temporal Response Function (TRF) is a linear model of neural activity time-locked to continuous stimuli, including continuous speech. TRFs based on speech envelopes typically have distinct components that have provided remarkable insights into the cortical processing of speech. Howeve...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering Vol. 70; no. 1; pp. 88 - 96
Main Authors: Kulasingham, Joshua P., Simon, Jonathan Z.
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
attention
auditory
Boosting
Brain modeling
cocktail party
deconvolution
EEG
Electroencephalography
ERP
Estimation
Magnetoencephalography
Magnetoencephalography - methods
matching pursuit
MEG
Models, Neurological
Performance measurement
Prediction algorithms
Response functions
reverse correlation
Speech
Speech Perception - physiology
Speech processing
Surfaces
ISSN:0018-9294, 1558-2531, 1558-2531
Online Access:Get full text
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Summary:Objective: The Temporal Response Function (TRF) is a linear model of neural activity time-locked to continuous stimuli, including continuous speech. TRFs based on speech envelopes typically have distinct components that have provided remarkable insights into the cortical processing of speech. However, current methods may lead to less than reliable estimates of single-subject TRF components. Here, we compare two established methods, in TRF component estimation, and also propose novel algorithms that utilize prior knowledge of these components, bypassing the full TRF estimation. Methods: We compared two established algorithms, ridge and boosting, and two novel algorithms based on Subspace Pursuit (SP) and Expectation Maximization (EM), which directly estimate TRF components given plausible assumptions regarding component characteristics. Single-channel, multi-channel, and source-localized TRFs were fit on simulations and real magnetoencephalographic data. Performance metrics included model fit and component estimation accuracy. Results: Boosting and ridge have comparable performance in component estimation. The novel algorithms outperformed the others in simulations, but not on real data, possibly due to the plausible assumptions not actually being met. Ridge had slightly better model fits on real data compared to boosting, but also more spurious TRF activity. Conclusion: Results indicate that both smooth (ridge) and sparse (boosting) algorithms perform comparably at TRF component estimation. The SP and EM algorithms may be accurate, but rely on assumptions of component characteristics. Significance: This systematic comparison establishes the suitability of widely used and novel algorithms for estimating robust TRF components, which is essential for improved subject-specific investigations into the cortical processing of speech.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2022.3185005