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Improvement of motor imagery electroencephalogram decoding by iterative weighted Sparse-Group Lasso

计算机科学 运动表象 模式识别(心理学) 人工智能 解码方法 判别式 特征选择 脑-机接口 特征(语言学) 支持向量机 聚类分析 机器学习 脑电图 算法 哲学 精神科 语言学 心理学
作者
Bin Liu,Fuwang Wang,Shiwei Wang,Junxiang Chen,Guilin Wen,Rongrong Fu
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:238: 122286-122286
标识
DOI:10.1016/j.eswa.2023.122286
摘要

Discriminative feature selection is vital for enhancing motor imagery decoding performance in electroencephalogram (EEG) signals. However, existing feature optimization methods have not sufficiently explored the intrinsic attribute distribution of features and their associations with the target class, which could result in spurious correlations between optimized features and class labels, yielding suboptimal performance. Therefore, this study proposed an iterative Weighted Sparse-Group Lasso (iWSGL) model for optimizing Common Spatial Pattern (CSP)-based high-dimensional features, thus further enhancing the decoding accuracy of motor imagery. Specifically, the affinity propagation (AP) clustering algorithm was utilized to adaptively partition the high-dimensional features into multiple groups based on the underlying relationships among them. To evaluate the significance of individual feature within each group and the overall significance of the groups themselves, a weight calculation method was proposed based on conditional entropy. With the weights and feature structural information, a weighted sparse regression model was devised within the iterative Sparse-Group Lasso (iSGL) framework to jointly optimize the CSP-based high-dimensional features. The performance of the proposed method was validated on three datasets using the support vector machine (SVM). The experimental results exhibited the exceptional superiority of the proposed method over the current CSP and its variants, demonstrating its remarkable performance. These findings imply that the proposed model can offer a novel optimization strategy for enhancing pattern recognition of brain intentions in Brain-Computer Interface (BCI) applications.
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