The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration. Two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. Performance of these two models is examined for analyzing 34 EEG data channels each consisting of five frequency bands and further decomposed with a filter bank. The data are collected from 64 subjects consisting of healthy controls and patients with SAD. Validity of our hypothesis that model 2 will significantly outperform model 1 is borne out in the results, with accuracy $6$--$7\%$ higher for model 2 for each machine learning algorithm we investigated. Convolutional Neural Networks (CNN) were found to provide much better performance than SVM and kNNs.
翻译:利用电脑物理学(EEG)进行分类,发现社会焦虑症(SAD)存在的问题,研究范围有限,并采用新办法加以解决,寻求利用EEG传感器空间配置的知识,研究两种分类模式,一种忽视配置(模式1),一种利用不同内插方法(模式2)利用配置(模式2),对这两种模式的性能进行分析,分析34个EEG数据渠道,每个渠道由5个频率波段组成,与过滤库进一步分离。这些数据来自64个主题,包括健康控制和患有SAD的病人。我们的假设,即2模型将大大超过模式1,其有效性在结果中得到证明,我们所调查的每部机器学习算法中,模型2的准确性比模型高6-7美元。 动态神经网络(CNN)比SVM和KNNs提供更好的性能。