Li Xinxin, Bu Lingguo
School of Software, Shandong University, Jinan, 250101.
Zhongguo Yi Liao Qi Xie Za Zhi. 2024 Mar 30;48(2):132-137. doi: 10.12455/j.issn.1671-7104.240012.
The study developed a memory task training system using functional near-infrared spectroscopy (fNIRS) and neurofeedback mechanisms, and acquired and analyzed subjects' EEG signals. The results showed that subjects participating in the neurofeedback task had higher correlated brain network node degrees and average cluster coefficients in the right hemisphere brain region of the prefrontal lobe, with relatively lower dispersion of mediator centrality. In addition, the subjects' left hemisphere brain region of the prefrontal lobe section had increased centrality in the neurofeedback task. Classification of brain data by the channel network model and the support vector machine model showed that the classification accuracy of both models was higher in the task state and resting state than in the feedback task and the control task, and the classification accuracy of the channel network model was higher. The results suggested that subjects in the neurofeedback task had distinct brain data features and that these features could be effectively recognized.
该研究开发了一种使用功能近红外光谱(fNIRS)和神经反馈机制的记忆任务训练系统,并采集和分析了受试者的脑电信号。结果表明,参与神经反馈任务的受试者在前额叶叶右半球脑区具有更高的相关脑网络节点度和平均聚类系数,中介中心性的离散度相对较低。此外,受试者前额叶叶左半球脑区在神经反馈任务中的中心性有所增加。通过通道网络模型和支持向量机模型对脑数据进行分类,结果表明,两种模型在任务状态和静息状态下的分类准确率均高于反馈任务和对照任务,且通道网络模型的分类准确率更高。结果表明,神经反馈任务中的受试者具有独特的脑数据特征,并且这些特征可以被有效识别。