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EEG Feature Selection via Stacked Deep Embedded Regression With Joint Sparsity
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-...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423875/ https://www.ncbi.nlm.nih.gov/pubmed/32848581 http://dx.doi.org/10.3389/fnins.2020.00829 |
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author | Jiang, Kui Tang, Jiaxi Wang, Yulong Qiu, Chengyu Zhang, Yuanpeng Lin, Chuang |
author_facet | Jiang, Kui Tang, Jiaxi Wang, Yulong Qiu, Chengyu Zhang, Yuanpeng Lin, Chuang |
author_sort | Jiang, Kui |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7423875 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74238752020-08-25 EEG Feature Selection via Stacked Deep Embedded Regression With Joint Sparsity Jiang, Kui Tang, Jiaxi Wang, Yulong Qiu, Chengyu Zhang, Yuanpeng Lin, Chuang Front Neurosci Neuroscience 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. Frontiers Media S.A. 2020-08-06 /pmc/articles/PMC7423875/ /pubmed/32848581 http://dx.doi.org/10.3389/fnins.2020.00829 Text en Copyright © 2020 Jiang, Tang, Wang, Qiu, Zhang and Lin. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Jiang, Kui Tang, Jiaxi Wang, Yulong Qiu, Chengyu Zhang, Yuanpeng Lin, Chuang EEG Feature Selection via Stacked Deep Embedded Regression With Joint Sparsity |
title | EEG Feature Selection via Stacked Deep Embedded Regression With Joint Sparsity |
title_full | EEG Feature Selection via Stacked Deep Embedded Regression With Joint Sparsity |
title_fullStr | EEG Feature Selection via Stacked Deep Embedded Regression With Joint Sparsity |
title_full_unstemmed | EEG Feature Selection via Stacked Deep Embedded Regression With Joint Sparsity |
title_short | EEG Feature Selection via Stacked Deep Embedded Regression With Joint Sparsity |
title_sort | eeg feature selection via stacked deep embedded regression with joint sparsity |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7423875/ https://www.ncbi.nlm.nih.gov/pubmed/32848581 http://dx.doi.org/10.3389/fnins.2020.00829 |
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