Prediction of working memory ability based on EEG by functional data analysis
Prediction of working memory ability based on EEG by functional data analysis
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基于脑电图的功能数据分析预测工作记忆能力
DOI：
10.1016/j.jneumeth.2019.108552
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发表时间：
2020-03-01
影响因子：
3
通讯作者：
Ji, Linhong
中科院分区：
文献类型：
--
作者：
Zhang, Yuanyuan;Wang, Chienkai;Ji, Linhong
背景：对于脑电信号的处理，总是需要快速而准确的算法。由于高采样率，脑电信号通常带有大量的采样点，这使得用脑电进行认知研究时很难预测工作记忆能力。新方法：经过精心设计的实验，函数线性模型为涉及脑电信号预测器的回归提供了一个简单的框架。在线性结构中使用数据驱动的基础自然会扩展标准的线性回归模型。结果：利用套索特征选择方法从8个额叶电极中提取了关键特征，R平方为0.72，表明实际观察和样本外预测具有较强的线性关联性。与现有方法相比：目前还没有任何方法可以通过脑电信号从N-back任务测试中预测工作记忆能力；结论：数据分析表明，通过脑电信号处理，建立工作记忆能力与大脑额叶活动预测关系的多元函数线性回归模型是可行和准确的。
Background: There is always a demand for fast and accurate algorithms for EEG signal processing. Owing to the high sample rate, EEG signals usually come with a large number of sample points, making it difficult to predict the working memory ability in cognitive research with EEG.New Method: Following well-designed experiments, the functional linear model provides a simple framework for regressions involving EEG signal predictors. The use of a data-driven basis in a linear structure naturally extends the standard linear regression model. The proposed approach utilizes B-spline approximation of functional principal components that greatly facilitates implementation.Results: Using LASSO feature selection, critical features have been extracted from eight frontal electrodes, and the R-square of 0.72 indicates rather strong linear association of actual observations and out-of-sample predictions.Comparison with Existing Methods: There does not seem to be any existing methods of predicting working memory ability from N-back task tests via EEG signals; the data-driven functional linear regression method proposed in this work is, to the best of our knowledge, the first of its kind.Conclusions: The data analytics suggest that a multiple functional linear regression model for the predictive relationship between working memory ability and frontal activity of the brain is both feasible and accurate via EEG signal processing.