A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals

Brain-computer interface provides a new communication bridge between the human mind and devices, depending largely on the accurate classification and identification of non-invasive EEG signals. Recently, the deep learning approaches have been widely used in many fields to extract features and classi...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhiwen Zhang [verfasserIn] Feng Duan [verfasserIn] Jordi Sole-Casals [verfasserIn] Josep Dinares-Ferran [verfasserIn] Andrzej Cichocki [verfasserIn] Zhenglu Yang [verfasserIn] Zhe Sun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Motor imagery classification deep learning convolutional neural network wavelet neural network empirical mode decomposition artificial EEG frames

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 15945-15954
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:15945-15954

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2895133

Katalog-ID:	DOAJ016084322

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spelling	10.1109/ACCESS.2019.2895133 doi (DE-627)DOAJ016084322 (DE-599)DOAJa8d30bd2614242c9bbd6f391f1661d5e DE-627 ger DE-627 rakwb eng TK1-9971 Zhiwen Zhang verfasserin aut A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Brain-computer interface provides a new communication bridge between the human mind and devices, depending largely on the accurate classification and identification of non-invasive EEG signals. Recently, the deep learning approaches have been widely used in many fields to extract features and classify various types of data successfully. However, the deep learning approach requires massive data to train its neural networks, and the amount of data impacts greatly on the quality of the classifiers. This paper proposes a novel approach that combines deep learning and data augmentation for EEG classification. We applied the empirical mode decomposition on the EEG frames and mixed their intrinsic mode functions to create new artificial EEG frames, followed by transforming all EEG data into tensors as inputs of the neural network by complex Morlet wavelets. We proposed two neural networks-convolutional neural network and wavelet neural network-to train the weights and classify two classes of motor imagery signals. The wavelet neural network is a new type of neural network using wavelets to replace the convolutional layers. The experimental results show that the artificial EEG frames substantially improve the training of neural networks, and both two networks yield relatively higher classification accuracies compared to prevailing approaches. Meanwhile, we also verified the performance of our new proposed wavelet neural network model in the classification of steady-state visual evoked potentials. Motor imagery classification deep learning convolutional neural network wavelet neural network empirical mode decomposition artificial EEG frames Electrical engineering. Electronics. Nuclear engineering Feng Duan verfasserin aut Jordi Sole-Casals verfasserin aut Josep Dinares-Ferran verfasserin aut Andrzej Cichocki verfasserin aut Zhenglu Yang verfasserin aut Zhe Sun verfasserin aut In IEEE Access IEEE, 2014 7(2019), Seite 15945-15954 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:7 year:2019 pages:15945-15954 https://doi.org/10.1109/ACCESS.2019.2895133 kostenfrei https://doaj.org/article/a8d30bd2614242c9bbd6f391f1661d5e kostenfrei https://ieeexplore.ieee.org/document/8630915/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2019 15945-15954
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authorswithroles_txt_mv	Zhiwen Zhang @@aut@@ Feng Duan @@aut@@ Jordi Sole-Casals @@aut@@ Josep Dinares-Ferran @@aut@@ Andrzej Cichocki @@aut@@ Zhenglu Yang @@aut@@ Zhe Sun @@aut@@
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callnumber-first	T - Technology
author	Zhiwen Zhang
spellingShingle	Zhiwen Zhang misc TK1-9971 misc Motor imagery classification misc deep learning misc convolutional neural network misc wavelet neural network misc empirical mode decomposition misc artificial EEG frames misc Electrical engineering. Electronics. Nuclear engineering A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals
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topic_title	TK1-9971 A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals Motor imagery classification deep learning convolutional neural network wavelet neural network empirical mode decomposition artificial EEG frames
topic	misc TK1-9971 misc Motor imagery classification misc deep learning misc convolutional neural network misc wavelet neural network misc empirical mode decomposition misc artificial EEG frames misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Motor imagery classification misc deep learning misc convolutional neural network misc wavelet neural network misc empirical mode decomposition misc artificial EEG frames misc Electrical engineering. Electronics. Nuclear engineering
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title	A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals
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title_full	A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals
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title_sort	novel deep learning approach with data augmentation to classify motor imagery signals
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title_auth	A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals
abstract	Brain-computer interface provides a new communication bridge between the human mind and devices, depending largely on the accurate classification and identification of non-invasive EEG signals. Recently, the deep learning approaches have been widely used in many fields to extract features and classify various types of data successfully. However, the deep learning approach requires massive data to train its neural networks, and the amount of data impacts greatly on the quality of the classifiers. This paper proposes a novel approach that combines deep learning and data augmentation for EEG classification. We applied the empirical mode decomposition on the EEG frames and mixed their intrinsic mode functions to create new artificial EEG frames, followed by transforming all EEG data into tensors as inputs of the neural network by complex Morlet wavelets. We proposed two neural networks-convolutional neural network and wavelet neural network-to train the weights and classify two classes of motor imagery signals. The wavelet neural network is a new type of neural network using wavelets to replace the convolutional layers. The experimental results show that the artificial EEG frames substantially improve the training of neural networks, and both two networks yield relatively higher classification accuracies compared to prevailing approaches. Meanwhile, we also verified the performance of our new proposed wavelet neural network model in the classification of steady-state visual evoked potentials.
abstractGer	Brain-computer interface provides a new communication bridge between the human mind and devices, depending largely on the accurate classification and identification of non-invasive EEG signals. Recently, the deep learning approaches have been widely used in many fields to extract features and classify various types of data successfully. However, the deep learning approach requires massive data to train its neural networks, and the amount of data impacts greatly on the quality of the classifiers. This paper proposes a novel approach that combines deep learning and data augmentation for EEG classification. We applied the empirical mode decomposition on the EEG frames and mixed their intrinsic mode functions to create new artificial EEG frames, followed by transforming all EEG data into tensors as inputs of the neural network by complex Morlet wavelets. We proposed two neural networks-convolutional neural network and wavelet neural network-to train the weights and classify two classes of motor imagery signals. The wavelet neural network is a new type of neural network using wavelets to replace the convolutional layers. The experimental results show that the artificial EEG frames substantially improve the training of neural networks, and both two networks yield relatively higher classification accuracies compared to prevailing approaches. Meanwhile, we also verified the performance of our new proposed wavelet neural network model in the classification of steady-state visual evoked potentials.
abstract_unstemmed	Brain-computer interface provides a new communication bridge between the human mind and devices, depending largely on the accurate classification and identification of non-invasive EEG signals. Recently, the deep learning approaches have been widely used in many fields to extract features and classify various types of data successfully. However, the deep learning approach requires massive data to train its neural networks, and the amount of data impacts greatly on the quality of the classifiers. This paper proposes a novel approach that combines deep learning and data augmentation for EEG classification. We applied the empirical mode decomposition on the EEG frames and mixed their intrinsic mode functions to create new artificial EEG frames, followed by transforming all EEG data into tensors as inputs of the neural network by complex Morlet wavelets. We proposed two neural networks-convolutional neural network and wavelet neural network-to train the weights and classify two classes of motor imagery signals. The wavelet neural network is a new type of neural network using wavelets to replace the convolutional layers. The experimental results show that the artificial EEG frames substantially improve the training of neural networks, and both two networks yield relatively higher classification accuracies compared to prevailing approaches. Meanwhile, we also verified the performance of our new proposed wavelet neural network model in the classification of steady-state visual evoked potentials.
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title_short	A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals
url	https://doi.org/10.1109/ACCESS.2019.2895133 https://doaj.org/article/a8d30bd2614242c9bbd6f391f1661d5e https://ieeexplore.ieee.org/document/8630915/ https://doaj.org/toc/2169-3536
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author2	Feng Duan Jordi Sole-Casals Josep Dinares-Ferran Andrzej Cichocki Zhenglu Yang Zhe Sun
author2Str	Feng Duan Jordi Sole-Casals Josep Dinares-Ferran Andrzej Cichocki Zhenglu Yang Zhe Sun
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doi_str	10.1109/ACCESS.2019.2895133
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up_date	2024-07-03T18:53:19.158Z
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A Novel Deep Learning Approach With Data Augmentation to Classify Motor Imagery Signals

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