EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System.
EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System.
复制标题
使用基于情感 BCI 的封闭神经反馈系统进行基于脑电图的焦虑状态分类
DOI：
10.1007/s40846-020-00596-7
复制
发表时间：
2021
影响因子：
2
通讯作者：
Ming D
中科院分区：
文献类型：
--
作者：
Chen C;Yu X;Belkacem AN;Lu L;Li P;Zhang Z;Wang X;Tan W;Gao Q;Shin D;Wang C;Sha S;Zhao X;Ming D
目的焦虑症是一种以极度恐惧或担忧为特征的精神疾病，它能改变大脑中化学物质的平衡。据我们所知，目前对焦虑状态的评估仍以主观问卷为主，尚无客观的标准评估。与其他方法不同的是，我们的方法侧重于研究神经的变化，识别和分类的焦虑状态，利用脑电图（EEG）signals.MethodsWe设计了一个封闭的神经反馈实验，包括三个实验阶段，以调整被试的心理状态。在第一和第三阶段记录了34名受试者的EEG静息状态信号，而在第二阶段记录了基于EEG的正念记录。在每个阶段结束时，要求受试者填写视觉焦虑量表（VAS）。根据他们的VAS评分，受试者被分为三组：非焦虑，中度或重度焦虑groups.ResultsAfter处理每组的EEG数据，支持向量机（SVM）分类器能够分类和识别两种精神状态（非焦虑和焦虑）使用功率谱密度（PSD）的模式。采用高斯核函数和多项式核函数的最高分类精度分别为92.48 ± 0.001和92.48 ± 0.001。 1.20%和88.60 ± 1.32%。对健康人的分类准确率平均最高为95.31 ± 0.99 焦虑组为87.18 ± 1.97 结论基于脑电神经反馈的分类方法可有效地用于开发情感性脑机接口系统，用于焦虑障碍状态的检测和评估。
PurposeAnxiety disorder is one of the psychiatric disorders that involves extreme fear or worry, which can change the balance of chemicals in the brain. To the best of our knowledge, the evaluation of anxiety state is still based on some subjective questionnaires and there is no objective standard assessment yet. Unlike other methods, our approach focuses on study the neural changes to identify and classify the anxiety state using electroencephalography (EEG) signals.MethodsWe designed a closed neurofeedback experiment that contains three experimental stages to adjust subjects’ mental state. The EEG resting state signal was recorded from thirty-four subjects in the first and third stages while EEG-based mindfulness recording was recorded in the second stage. At the end of each stage, the subjects were asked to fill a Visual Analogue Scale (VAS). According to their VAS score, the subjects were classified into three groups: non-anxiety, moderate or severe anxiety groups.ResultsAfter processing the EEG data of each group, support vector machine (SVM) classifiers were able to classify and identify two mental states (non-anxiety and anxiety) using the Power Spectral Density (PSD) as patterns. The highest classification accuracies using Gaussian kernel function and polynomial kernel function are 92.48 ± 1.20% and 88.60 ± 1.32%, respectively. The highest average of the classification accuracies for healthy subjects is 95.31 ± 1.97% and for anxiety subjects is 87.18 ± 3.51%.ConclusionsThe results suggest that our proposed EEG neurofeedback-based classification approach is efficient for developing affective BCI system for detection and evaluation of anxiety disorder states.
登录
查看更多内容
影响因子：
4.4
作者：
Abedi, Maysam;Norouzi, Gholam-Hossain;Bahroudi, Abbas
通讯作者：
Bahroudi, Abbas
影响因子：
5.1
作者：
Ahani A;Wahbeh H;Nezamfar H;Miller M;Erdogmus D;Oken B
通讯作者：
Oken B
基于混合核函数SVM的点云分类
DOI：
10.1007/s12541-019-00102-3
发表时间：
2019-05-01
影响因子：
1.9
作者：
Chen, Chao;Li, Xiaomin;Shin, Duk
通讯作者：
Shin, Duk
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V
基于脑电图的抑郁症检测的普遍方法
影响因子：
2.3
作者：
Cai, Hanshu;Han, Jiashuo;Gutknecht, Jurg
通讯作者：
Gutknecht, Jurg