ArticleJournal of neural engineering2020
Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions.
Article in Journal of neural engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 27 citations in OpenAlex.
- Integrating multidimensional nociceptive-related cortical features for unsupervised assessment of anesthesia states in rats.iScience · 2026Article
- Electroencephalography Signatures Associated with Developmental Dyslexia Identified Using Principal Component Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Neural correlates of phonetic categorization under auditory (phoneme) and visual (grapheme) modalities.Neuroscience · 2025Article
- Get a New Perspective on EEG: Convolutional Neural Network Encoders for Parametric t-SNE.Brain sciences · 2023Article
- Unraveling Spatial-Spectral Dynamics of Speech Categorization Speed Using Convolutional Neural Networks.Brain sciences · 2022Article
- Brain functional and effective connectivity based on electroencephalography recordings: A review.Human brain mapping · 2022Review
- Article
- Auditory cortex is susceptible to lexical influence as revealed by informational vs. energetic masking of speech categorization.Brain research · 2021Article
- Lexical Influences on Categorical Speech Perception Are Driven by a Temporoparietal Circuit.Journal of cognitive neuroscience · 2021Article
- Data-driven machine learning models for decoding speech categorization from evoked brain responses.Journal of neural engineering · 2021Article
- Speech categorization is better described by induced rather than evoked neural activity.The Journal of the Acoustical Society of America · 2021Article
- Decoding Hearing-Related Changes in Older Adults' Spatiotemporal Neural Processing of Speech Using Machine Learning.Frontiers in neuroscience · 2020Article
- Early Imaging Based Predictive Modeling of Cognitive Performance Following Therapy for Childhood ALL.IEEE access : practical innovations, open solutions · 2019Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveCategorical perception (CP) is an inherent property of speech perception. The response time (RT) of listeners' perceptual speech identification is highly sensitive to individual differences. While the neural correlates of CP have been well studied in terms of the regional contributions of the brain to behavior, functional connectivity patterns that signify individual differences in listeners' speed (RT) for speech categorization is less clear. In this study, we introduce a novel approach to address these questions. APPROACH: We applied several computational approaches to the EEG, including graph mining, machine learning (i.e., support vector machine), and stability selection to investigate the unique brain states (functional neural connectivity) that predict the speed of listeners' behavioral decisions. MAIN
resultsWe infer that (i) the listeners' perceptual speed is directly related to dynamic variations in their brain connectomics, (ii) global network assortativity and efficiency distinguished fast, medium, and slow RTs, (iii) the functional network underlying speeded decisions increases in negative assortativity (i.e., became disassortative) for slower RTs, (iv) slower categorical speech decisions cause excessive use of neural resources and more aberrant information flow within the CP circuitry, (v) slower responders tended to utilize functional brain networks excessively (or inappropriately) whereas fast responders (with lower global efficiency) utilized the same neural pathways but with more restricted organization. SIGNIFICANCE: Findings show that neural classifiers (SVM) coupled with stability selection correctly classify behavioral RTs from functional connectivity alone with over 92% accuracy (AUC = 0.9). Our results corroborate previous studies by supporting the engagement of similar temporal (STG), parietal, motor, and prefrontal regions in CP using an entirely data-driven approach.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.