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Social anxiety prediction based on ERP features: A deep learning approach.
Tian, Xiaodong; Zhu, Lingkai; Zhang, Mingxian; Wang, Songling; Lu, Yi; Xu, Xiaolei; Jia, Weikuan; Zheng, Yuanjie; Song, Sutao.
Afiliación
  • Tian X; School of Information Science and Engineering, Shandong Normal University, Jinan, China.
  • Zhu L; School of Information Science and Engineering, Shandong Normal University, Jinan, China.
  • Zhang M; School of Education and Psychology, University of Jinan, Jinan, China.
  • Wang S; School of Information Science and Engineering, Shandong Normal University, Jinan, China.
  • Lu Y; School of Information Science and Engineering, Shandong Normal University, Jinan, China.
  • Xu X; School of Psychology, Shandong Normal University, Jinan, China.
  • Jia W; School of Information Science and Engineering, Shandong Normal University, Jinan, China.
  • Zheng Y; School of Information Science and Engineering, Shandong Normal University, Jinan, China.
  • Song S; School of Information Science and Engineering, Shandong Normal University, Jinan, China; School of Education and Psychology, University of Jinan, Jinan, China. Electronic address: sutao.song@sdnu.edu.cn.
J Affect Disord ; 367: 545-553, 2024 Sep 03.
Article en En | MEDLINE | ID: mdl-39236887
ABSTRACT

BACKGROUND:

Social Anxiety Disorder is traditionally diagnosed using subjective scales that may lack accuracy. Recently, EEG technology has gained importance for anxiety detection due to its ability to capture stable and objective neurophysiological activities. However, existing methods mainly focus on extracting EEG features during resting states, with limited use of psychologically features like Event-Related Potential (ERP) in task-related states for anxiety detection in deep learning frameworks.

METHODS:

We collected EEG data from 63 participants exposed to four facial expressions and extracted task-relevant features. Using the EEGNet model, we predicted social anxiety and evaluated its performance using metrics such as accuracy, F1 score, sensitivity, and specificity. We compared EEGNet's performance with Deep Convolutional Neural Network (DeepConvNet), ShallowConvNet, Bi-directional Long Short-Term Memory (BiLSTM), and SVM. To assess the generalizability of the results, we carried out the same procedure on our prior dataset.

RESULTS:

EEGNet outperformed other models, achieving 99.16 % accuracy with Late Positive Potential (LPP). ERP components yielded higher accuracy than time-domain and frequency-domain features for social anxiety recognition. Accuracy was better for neutral and negative facial stimuli. Consistency across two datasets indicates stability of findings.

LIMITATIONS:

Due to limited publicly available task-state datasets, only our own were used. Future studies could assess generalizability on larger datasets from different sources.

CONCLUSIONS:

We conducted the first test of ERP features in anxiety recognition tasks. Results show ERP features have greater potential in social anxiety recognition, with LPP exhibiting high stability and accuracy. Outcomes indicate recognizing social anxiety with negative or neutral facial stimuli is more useful.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Affect Disord Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Affect Disord Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos