School of Information Science and Engineering, Shandong Normal University, Jinan, China.
School of Education and Psychology, University of Jinan, Jinan, China.
J Affect Disord. 2024 Dec 15;367:545-553. doi: 10.1016/j.jad.2024.09.006. Epub 2024 Sep 3.
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.
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.
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.
Due to limited publicly available task-state datasets, only our own were used. Future studies could assess generalizability on larger datasets from different sources.
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.
传统上，社交焦虑症是使用可能缺乏准确性的主观量表来诊断的。最近，由于能够捕获稳定和客观的神经生理活动，脑电图（EEG）技术在焦虑检测方面变得越来越重要。然而，现有的方法主要侧重于在静息状态下提取 EEG 特征，而在深度学习框架中，很少利用与任务相关的状态下的心理特征，如事件相关电位（ERP）来检测焦虑。
我们从 63 名暴露于四种面部表情的参与者中采集了 EEG 数据，并提取了与任务相关的特征。我们使用 EEGNet 模型预测社交焦虑，并使用准确性、F1 分数、灵敏度和特异性等指标来评估其性能。我们将 EEGNet 的性能与深度卷积神经网络（DeepConvNet）、浅层卷积神经网络（ShallowConvNet）、双向长短期记忆（BiLSTM）和支持向量机（SVM）进行了比较。为了评估结果的泛化能力，我们在自己的先前数据集上进行了相同的过程。
EEGNet 表现优于其他模型，在晚期正电位（LPP）时达到了 99.16%的准确性。对于社交焦虑识别，ERP 成分比时域和频域特征具有更高的准确性。对于中性和负性面部刺激，准确性更好。两个数据集的一致性表明结果稳定。
由于可用的任务状态数据集有限，我们仅使用了自己的数据集。未来的研究可以在来自不同来源的更大数据集上评估泛化能力。
我们首次测试了 ERP 特征在焦虑识别任务中的应用。结果表明，ERP 特征在社交焦虑识别中具有更大的潜力，LPP 表现出较高的稳定性和准确性。结果表明，识别带有负面或中性面部刺激的社交焦虑更有用。
