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CORRECTION article

Front. Psychiatry, 24 July 2023
Sec. Psychopathology
Volume 14 - 2023 | https://doi.org/10.3389/fpsyt.2023.1257713
This article is part of the Research Topic From Assessment to Intervention: Role of Consumer Technology and Neurotech in Preventive Mental Health View all 5 articles

Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures

This article is a correction to:

  1. Review of EEG, ERP, and Brain Connectivity Estimators as Predictive Biomarkers of Social Anxiety Disorder

    1. Read original article
\r\nAbdulhakim Al-EzziAbdulhakim Al-Ezzi1Nidal Kamel &#x;Nidal Kamel2*†Amal A. Al-Shargabi Amal A. Al-Shargabi3*Fares Al-ShargieFares Al-Shargie4Alaa Al-Shargabi&#x;Alaa Al-Shargabi5†Norashikin Yahya Norashikin Yahya1*Mohammed Isam Al-HiyaliMohammed Isam Al-Hiyali1
  • 1Centre for Intelligent Signal & Imaging Research (CISIR), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia
  • 2College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam
  • 3Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
  • 4Faculty of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates
  • 5Department of Information Technology, Universiti Teknlogi Malaysia, Skudai, Malaysia

A corrigendum on
Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures

by Al-Ezzi, A., Kamel, N., Al-Shargabi, A. A., Al-Shargie, F., Al-Shargabi, A., Yahya, N., and Al-Hiyali, M. I. (2023). Front. Psychiatry 14:1155812. doi: 10.3389/fpsyt.2023.1155812

In the published article, there was an error in the Funding statement. The correct Funding and Acknowledgment statements appear below.

Funding

This research was supported by the Ministry of Education, Malaysia under the Higher Institute Center of Excellence (HiCOE) scheme awarded to the Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS.

Acknowledgments

Researchers would like to thank the Deanship of Scientific Research, Qassim University for funding the publication of this project. Researchers also would like to thank the Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS for their support by providing EEG data.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: EEG, graph theory analysis, social anxiety disorders, machine learning, effective connectivity, partial directed coherence, support vector machine, event related potential

Citation: Al-Ezzi A, Kamel N, Al-Shargabi AA, Al-Shargie F, Al-Shargabi A, Yahya N and Al-Hiyali MI (2023) Corrigendum: Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures. Front. Psychiatry 14:1257713. doi: 10.3389/fpsyt.2023.1257713

Received: 12 July 2023; Accepted: 13 July 2023;
Published: 24 July 2023.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2023 Al-Ezzi, Kamel, Al-Shargabi, Al-Shargie, Al-Shargabi, Yahya and Al-Hiyali. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nidal Kamel, nidal.k@vinuni.edu.vn; Amal A. Al-Shargabi, alshargabi@qu.edu.sa; Norashikin Yahya, norashikin_yahya@utp.edu.my

†ORCID: Nidal Kamel orcid.org/0000-0002-9638-6379
Alaa Al-Shargabi orcid.org/0000-0001-6454-5913

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.