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Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network
BACKGROUND: Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. METHODS: Alternatively, this paper applies a deep recur...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6162908/ https://www.ncbi.nlm.nih.gov/pubmed/30268089 http://dx.doi.org/10.1186/s12859-018-2365-1 |
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author | Luo, Tian-jian Zhou, Chang-le Chao, Fei |
author_facet | Luo, Tian-jian Zhou, Chang-le Chao, Fei |
author_sort | Luo, Tian-jian |
collection | PubMed |
description | BACKGROUND: Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. METHODS: Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. RESULTS: Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. CONCLUSION: By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals. |
format | Online Article Text |
id | pubmed-6162908 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61629082018-10-04 Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network Luo, Tian-jian Zhou, Chang-le Chao, Fei BMC Bioinformatics Research Article BACKGROUND: Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. METHODS: Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. RESULTS: Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. CONCLUSION: By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals. BioMed Central 2018-09-29 /pmc/articles/PMC6162908/ /pubmed/30268089 http://dx.doi.org/10.1186/s12859-018-2365-1 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Luo, Tian-jian Zhou, Chang-le Chao, Fei Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network |
title | Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network |
title_full | Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network |
title_fullStr | Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network |
title_full_unstemmed | Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network |
title_short | Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network |
title_sort | exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6162908/ https://www.ncbi.nlm.nih.gov/pubmed/30268089 http://dx.doi.org/10.1186/s12859-018-2365-1 |
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