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

Abdulhakim Al-Ezzi; Nidal Kamel; Amal A Al-Shargabi; Fares Al-Shargie; Alaa Al-Shargabi; Norashikin Yahya; Mohammed Isam Al-Hiyali

doi:10.3389/fpsyt.2023.1257713

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

Front Psychiatry. 2023 Jul 24:14:1257713. doi: 10.3389/fpsyt.2023.1257713. eCollection 2023.

Authors

Abdulhakim Al-Ezzi¹, Nidal Kamel², Amal A Al-Shargabi³, Fares Al-Shargie⁴, Alaa Al-Shargabi⁵, Norashikin Yahya¹, Mohammed Isam Al-Hiyali¹

Affiliations

¹ Centre for Intelligent Signal & Imaging Research (CISIR), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia.
² College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam.
³ Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.
⁴ Faculty of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates.
⁵ Department of Information Technology, Universiti Teknlogi Malaysia, Skudai, Malaysia.

Abstract

[This corrects the article DOI: 10.3389/fpsyt.2023.1155812.].

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

Publication types

Published Erratum

Grants and 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.