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EEG Feature Selection via Stacked Deep Embedded Regression With Joint Sparsity
Frontiers in Neuroscience ( IF 4.3 ) Pub Date : 2020-08-06 , DOI: 10.3389/fnins.2020.00829
Kui Jiang 1 , Jiaxi Tang 1 , Yulong Wang 1 , Chengyu Qiu 1 , Yuanpeng Zhang 1 , Chuang Lin 2
Affiliation  

In the field of brain-computer interface (BCI), selecting efficient and robust features is very seductive for artificial intelligence (AI)-assisted clinical diagnosis. In this study, based on an embedded feature selection model, we construct a stacked deep structure for feature selection in a layer-by-layer manner. Its promising performance is guaranteed by the stacked generalized principle that random projections added into the original features can help us to continuously open the manifold structure existing in the original feature space in a stacked way. With such benefits, the original input feature space becomes more linearly separable. We use the epilepsy EEG data provided by the University of Bonn to evaluate our model. Based on the EEG data, we construct three classification tasks. On each task, we use different feature selection models to select features and then use two classifiers to perform classification based on the selected features. Our experimental results show that features selected by our new structure are more meaningful and helpful to the classifier hence generates better performance than benchmarking models.

中文翻译:

通过具有联合稀疏性的堆叠深度嵌入回归进行 EEG 特征选择

在脑机接口(BCI)领域,选择高效且稳健的特征对于人工智能(AI)辅助的临床诊断非常具有诱惑力。在本研究中,基于嵌入式特征选择模型,我们以逐层的方式构建用于特征选择的堆叠深度结构。其良好的性能由堆叠广义原理保证,即添加到原始特征中的随机投影可以帮助我们以堆叠的方式不断打开原始特征空间中存在的流形结构。有了这些好处,原始输入特征空间变得更加线性可分。我们使用波恩大学提供的癫痫脑电图数据来评估我们的模型。基于脑电数据,我们构建了三个分类任务。在每项任务中,我们使用不同的特征选择模型来选择特征,然后使用两个分类器根据选择的特征进行分类。我们的实验结果表明,我们的新结构选择的特征对分类器更有意义,更有帮助,因此比基准模型产生更好的性能。
更新日期:2020-08-06
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