EEG Multi-Objective Feature Selection Using Temporal Extension

L. Ferariu, Corina Cimpanu, Tiberius Dumitriu, F. Ungureanu
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引用次数: 3

Abstract

Nowadays Electroencephalogram (EEG) devices allow the recording of signals that can be used to extract information necessary to identify different types of cognitive processes. In EEG classification, Feature Selection (FS) represents a pivotal phase, as these problems request the processing of a large amount of high-dimensional patterns. In this paper, FS has been solved by an embedded multi-objective genetic optimization procedure which evolves a population of potential solutions (subsets of features), subject to the simultaneous minimization of the misclassification ratio and number of selected attributes. Random Forests (RF) classifiers are adopted, due to their fast training and their compatibility with spread classes of very diverse patterns. The main contribution presented in this paper consists in introducing an inertial behavior to feature extraction. The available feature set is extended with features from previous time frames, and FS is performed on this extended set. In this context, the experimental analysis illustrates the impact of the temporal extension on FS. Additionally, two enhancements are proposed for the multi-objective optimization, to support an effective Pareto-ranking of the solutions in the expanded exploration search space. Thus, the number of trees in the embedded RF classifier is gradually increased, for reducing the computational load requested for the evaluation of the misclassification ratio, without impeding the exploration. Also, the preference for the minimization of misclassifications is set by introducing a dynamic objective function for describing the parsimony of the selected subset of attributes. The proposed FS is experimentally demonstrated on EEG data collected during mathematical tasks of gradual complexities.
基于时间扩展的脑电多目标特征选择
如今,脑电图(EEG)设备允许记录信号,这些信号可用于提取必要的信息,以识别不同类型的认知过程。在脑电分类中,特征选择(Feature Selection, FS)是一个关键阶段,因为这些问题需要处理大量的高维模式。本文采用嵌入式多目标遗传优化程序求解FS,该程序进化出一群潜在解(特征子集),同时最小化误分类率和选择属性的数量。随机森林(Random Forests, RF)分类器训练速度快,且能适应多种模式的扩展类,因此采用了随机森林分类器。本文的主要贡献在于将惯性行为引入到特征提取中。可用的特性集使用以前时间框架中的特性进行扩展,并在此扩展集上执行FS。在此背景下,实验分析说明了时间延长对FS的影响。此外,对多目标优化提出了两个改进,以支持在扩展的探索搜索空间中对解决方案进行有效的帕累托排序。因此,嵌入式RF分类器中的树数逐渐增加,以减少评估误分类率所需的计算量,而不妨碍探索。此外,通过引入描述所选属性子集的简约性的动态目标函数来设置最小化错误分类的偏好。实验证明了该方法在复杂数学任务中采集的脑电数据上的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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