of Information and Computer, Taiyuan University of Technology

【目的】阅读理解是人类最重要的认知能力,评价人类的阅读理解能力需要客观指标。【方法】提出一种基于脑磁图(magnetoencephalogram,MEG)虚相干脑功能连接的预测模型,使用虚相干算法构建全脑MEG功能连接,并通过单变量特征选择算法对特征进行选择,采用偏最小二乘回归(PartialLeastSquares,PLS)构建预测模型对阅读理解能力进行预测。【结果】基于MEG虚相干功能连接的偏最小二乘回归模型可以成功预测阅读理解分数;进行单变量特征选择的模型预测性能更高、预测更准确(R2[PVT-Language]=0.524,MSE[PVT-Language]=5.042;R2[ORRT-Language]=0.536,MSE[ORRT-Language]=5.142),并且发现采用与阅读理解相关的任务态数据集比静息态数据集更适合用来预测阅读理解能力,且特征选择的功能连接更精确。【结论】基于MEG虚相干功能连接的PLS预测模型可以用来客观评价人类阅读理解能力。

【Purposes】 Reading comprehension is one of the most important cognitive abilities of human beings. Objective indicators should be provided in order to evaluate human reading comprehension ability. 【Methods】 A prediction model based on magnetoencephalogram (MEG) imaginary coherent brain functional connections is proposed in this paper. The imaginary coher-ence algorithm is used to construct the whole brain MEG functional connections, and the features are selected by univariate feature selection algorithm. The reading comprehension ability is pre-dicted with partial least squares (PLS) prediction model. 【Findings】 The partial least squares re-gression model based on MEG imaginary coherent functional connections can successfully predict reading comprehension scores. For univariate feature selection model forecasting, higher performance and accuracy can be obtained (R2=0.524[PVT-Language], MSE[PVT-Language]=5.042; R2[ORRT-Language]=0.536, MSE[ORRT-Language]=5.142). The task state data set related to reading comprehension is more suitable than the resting state data set to predict reading comprehension ability, and the functional connection of feature selection is more accu-rate. 【Conclusions】 The PLS prediction model based on MEG virtual coherence functional connec-tion can be used to objectively evaluate human reading comprehension.

国家自然科学基金资助项目(61873178,61876124);山西国际科技合作项目(201803D421047);山西自然科学基金资助项目(201801D121135);青年科技研究基金资助项目(201701D221119)

赵力敏,相洁,王彬,等.基于 MEG 虚相干功能连接的阅读理解能力预测模型的研究[J].太原理工大学学报,2023,54(5):796-803.

ZHAO Limin,XIANG Jie,WANG Bin,et al.A predictive model of reading comprehension based on MEG imag-inary coherence functional connections[J].Journal of Taiyuan University of Technology,2023,54(5):796-803.