DIVA Meets EEG: Model Validation Using Formant-Shift Reflex

Author:

Cuadros Jhosmary123ORCID,Z-Rivera Lucía24ORCID,Castro Christian24,Whitaker Grace2,Otero Mónica56ORCID,Weinstein Alejandro24ORCID,Martínez-Montes Eduardo7ORCID,Prado Pavel8ORCID,Zañartu Matías12ORCID

Affiliation:

1. Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile

2. Advanced Center for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile

3. Grupo de Bioingeniería, Decanato de Investigación, Universidad Nacional Experimental del Táchira, San Cristóbal 5001, Venezuela

4. Escuela de Ingeniería Civil Biomédica, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2350026, Chile

5. Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago 8420524, Chile

6. Centro Basal Ciencia & Vida, Universidad San Sebastián, Santiago 8580000, Chile

7. Brain Mapping Division, Cuban Neuroscience Center, Habana 11300, Cuba

8. Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago 7510602, Chile

Abstract

The neurocomputational model ‘Directions into Velocities of Articulators’ (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions.

Funder

U.S. National Institutes of Health

Agencia Nacional de Investigación y Desarrollo de Chile

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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