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Jeng FC, Jeng YS. Implementation of Machine Learning on Human Frequency-Following Responses: A Tutorial. Semin Hear 2022; 43:251-274. [PMID: 36313046 PMCID: PMC9605809 DOI: 10.1055/s-0042-1756219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
The frequency-following response (FFR) provides enriched information on how acoustic stimuli are processed in the human brain. Based on recent studies, machine learning techniques have demonstrated great utility in modeling human FFRs. This tutorial focuses on the fundamental principles, algorithmic designs, and custom implementations of several supervised models (linear regression, logistic regression, k -nearest neighbors, support vector machines) and an unsupervised model ( k -means clustering). Other useful machine learning tools (Markov chains, dimensionality reduction, principal components analysis, nonnegative matrix factorization, and neural networks) are discussed as well. Each model's applicability and its pros and cons are explained. The choice of a suitable model is highly dependent on the research question, FFR recordings, target variables, extracted features, and their data types. To promote understanding, an example project implemented in Python is provided, which demonstrates practical usage of several of the discussed models on a sample dataset of six FFR features and a target response label.
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Affiliation(s)
- Fuh-Cherng Jeng
- Communication Sciences and Disorders, Ohio University, Athens, Ohio
| | - Yu-Shiang Jeng
- Computer Science and Engineering, Ohio State University, Columbus, Ohio
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Gnanateja GN, Rupp K, Llanos F, Remick M, Pernia M, Sadagopan S, Teichert T, Abel TJ, Chandrasekaran B. Frequency-Following Responses to Speech Sounds Are Highly Conserved across Species and Contain Cortical Contributions. eNeuro 2021; 8:ENEURO.0451-21.2021. [PMID: 34799409 PMCID: PMC8704423 DOI: 10.1523/eneuro.0451-21.2021] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/02/2021] [Indexed: 11/21/2022] Open
Abstract
Time-varying pitch is a vital cue for human speech perception. Neural processing of time-varying pitch has been extensively assayed using scalp-recorded frequency-following responses (FFRs), an electrophysiological signal thought to reflect integrated phase-locked neural ensemble activity from subcortical auditory areas. Emerging evidence increasingly points to a putative contribution of auditory cortical ensembles to the scalp-recorded FFRs. However, the properties of cortical FFRs and precise characterization of laminar sources are still unclear. Here we used direct human intracortical recordings as well as extracranial and intracranial recordings from macaques and guinea pigs to characterize the properties of cortical sources of FFRs to time-varying pitch patterns. We found robust FFRs in the auditory cortex across all species. We leveraged representational similarity analysis as a translational bridge to characterize similarities between the human and animal models. Laminar recordings in animal models showed FFRs emerging primarily from the thalamorecipient layers of the auditory cortex. FFRs arising from these cortical sources significantly contributed to the scalp-recorded FFRs via volume conduction. Our research paves the way for a wide array of studies to investigate the role of cortical FFRs in auditory perception and plasticity.
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Affiliation(s)
- G Nike Gnanateja
- Department of Communication Sciences and Disorders, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Kyle Rupp
- Department of Neurological Surgery, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Fernando Llanos
- Department of Linguistics, The University of Texas at Austin, Austin, Texas 78712
| | - Madison Remick
- Department of Neurological Surgery, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Marianny Pernia
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15261
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Srivatsun Sadagopan
- Department of Communication Sciences and Disorders, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15261
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania 15261
| | - Tobias Teichert
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15261
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Taylor J Abel
- Department of Neurological Surgery, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania 15213
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Bharath Chandrasekaran
- Department of Communication Sciences and Disorders, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania 15261
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