IEEE Trans Neural Syst Rehabil Eng. 2021;29:566-575. doi: 10.1109/TNSRE.2021.3059429. Epub 2021 Mar 3.
Depression has become a leading mental disorder worldwide. Evidence has shown that subjects with depression exhibit different spatial responses in neurophysiological signals from the healthy controls when they are exposed to positive and negative stimuli.
We proposed an effective electroencephalogram-based detection method for depression classification using spatial information. A face-in-the-crowd task, including positive and negative emotional facial expressions, was presented to 30 participants, including 16 depression patients and 14 healthy controls. Differential entropy and the genetic algorithm were used for feature extraction and selection, and a support vector machine was used for classification. A task-related common spatial pattern (TCSP) was proposed to enhance the spatial differences before the feature extraction.
We achieved a leave-one-subject-out cross-validation classification result of 84% and 85.7% for positive and negative stimuli, respectively, using TCSP, which is statistically significantly higher than 81.7% and 83.2%, respectively, acquired without the TCSP (p < 0.05). We also evaluated the classification performance using individual frequency bands and found that the contribution of the gamma band was predominant. In addition, we evaluated different classifiers, including k-nearest neighbor and logistic regression, which showed similar trends in the improvement of classification by employing TCSP.
The results show that our proposed method, employing spatial information, significantly improves the accuracy of classifying depression patients.
抑郁症已成为全球范围内主要的精神障碍之一。有证据表明，与健康对照组相比，抑郁症患者在暴露于正性和负性刺激时，其神经生理信号的空间反应存在差异。
我们提出了一种基于脑电的有效抑郁分类检测方法，利用空间信息。通过呈现包括正性和负性情绪面部表情的人群面孔任务，对 30 名参与者（16 名抑郁症患者和 14 名健康对照者）进行了测试。使用差分熵和遗传算法进行特征提取和选择，支持向量机进行分类。提出了任务相关的共同空间模式（TCSP），以在特征提取之前增强空间差异。
我们使用 TCSP 实现了 84%和 85.7%的正性和负性刺激的受试者间留一法交叉验证分类结果，与不使用 TCSP 时分别获得的 81.7%和 83.2%相比，具有统计学显著差异（p<0.05）。我们还评估了个体频带的分类性能，发现伽马频段的贡献占主导地位。此外，我们评估了不同的分类器，包括 k-最近邻和逻辑回归，它们都表现出通过采用 TCSP 来提高分类准确性的相似趋势。
结果表明，我们提出的利用空间信息的方法可显著提高抑郁患者的分类准确性。
