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DIVA与脑电图相遇:使用共振峰转移反射进行模型验证。

DIVA Meets EEG: Model Validation Using Formant-Shift Reflex.

作者信息

Cuadros Jhosmary, Z-Rivera Lucía, Castro Christian, Whitaker Grace, Otero Mónica, Weinstein Alejandro, Martínez-Montes Eduardo, Prado Pavel, Zañartu Matías

机构信息

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

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

出版信息

Appl Sci (Basel). 2023 Jul 1;13(13). doi: 10.3390/app13137512. Epub 2023 Jun 25.

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.

摘要

神经计算模型“发音器官方向到速度”(DIVA)的开发是为了解释正常和紊乱的语音产生与习得的各个方面。DIVA的神经基质是通过功能磁共振成像(fMRI)建立的,为该模型提供了生理学验证。本研究介绍了DIVA_EEG,它是DIVA的一个扩展,利用脑电图(EEG)来利用EEG相对于fMRI的高时间分辨率和广泛可用性。为了开发DIVA_EEG,从描述不同DIVA图谱活动的原始方程中导出了类似EEG的信号。当模拟无扰动和有扰动的听觉反馈(第一共振峰扰动)时,生成了与音节发音相关的合成EEG。从合成EEG得出的皮质激活图谱与原始DIVA模型的图谱非常相似。为了验证DIVA_EEG,在改变听觉反馈范式期间采集了具有典型嗓音的个体(N = 30)的EEG。所得的经验性脑活动图谱与DIVA_EEG预测的图谱有显著重叠。与其他最近的模型扩展相结合,DIVA_EEG为构建一个完整的神经计算框架以解决嗓音和言语障碍奠定了基础,该框架可指导模型驱动的个性化干预。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aca9/10906992/f2d9b6c02d2b/nihms-1959001-f0001.jpg

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