Machine learning for the detection of social anxiety disorder using effective connectivity and graph theory measures (vol 14, 1155812, 2023)

被引：0
|
作者：
Al-Ezzi, Abdulhakim [1 ]
Kamel, Nidal [2 ]
Al-Shargabi, Amal A. A. [3 ]
Al-Shargie, Fares [4 ]
Al-Shargabi, Alaa [5 ]
Yahya, Norashikin [1 ]
Al-Hiyali, Mohammed Isam [1 ]
机构：
[1] Univ Teknol PETRONAS, Ctr Intelligent Signal & Imaging Res CISIR, Elect & Elect Engn Dept, Bandar Seri Iskandar, Perak, Malaysia
[2] VinUniversity, Coll Engn & Comp Sci, Hanoi, Vietnam
[3] Qassim Univ, Coll Comp, Dept Informat Technol, Buraydah, Saudi Arabia
[4] Abu Dhabi Univ, Fac Engn, Abu Dhabi, U Arab Emirates
[5] Univ Teknl Malaysia, Dept Informat Technol, Skudai, Malaysia
来源：
FRONTIERS IN PSYCHIATRY | 2023年 / 14卷
关键词：
EEG; graph theory analysis; social anxiety disorders; machine learning; effective connectivity; partial directed coherence; support vector machine; event related potential;
D O I：
10.3389/fpsyt.2023.1257713
中图分类号：
R749 [精神病学];
学科分类号：
100205 ;
摘要：
引用
收藏
页数：2
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