Brain wave classification using long short-term memory network based OPTICAL predictor

Abstract Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and...

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Autores principales:	Shiu Kumar, Alok Sharma, Tatsuhiko Tsunoda
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/827ee8e228ac46cc89d33874beb953c8
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spelling	oai:doaj.org-article:827ee8e228ac46cc89d33874beb953c82021-12-02T15:07:53ZBrain wave classification using long short-term memory network based OPTICAL predictor10.1038/s41598-019-45605-12045-2322https://doaj.org/article/827ee8e228ac46cc89d33874beb953c82019-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-45605-1https://doaj.org/toc/2045-2322Abstract Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL.Shiu KumarAlok SharmaTatsuhiko TsunodaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-13 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shiu Kumar Alok Sharma Tatsuhiko Tsunoda Brain wave classification using long short-term memory network based OPTICAL predictor
description	Abstract Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL.
format	article
author	Shiu Kumar Alok Sharma Tatsuhiko Tsunoda
author_facet	Shiu Kumar Alok Sharma Tatsuhiko Tsunoda
author_sort	Shiu Kumar
title	Brain wave classification using long short-term memory network based OPTICAL predictor
title_short	Brain wave classification using long short-term memory network based OPTICAL predictor
title_full	Brain wave classification using long short-term memory network based OPTICAL predictor
title_fullStr	Brain wave classification using long short-term memory network based OPTICAL predictor
title_full_unstemmed	Brain wave classification using long short-term memory network based OPTICAL predictor
title_sort	brain wave classification using long short-term memory network based optical predictor
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/827ee8e228ac46cc89d33874beb953c8
work_keys_str_mv	AT shiukumar brainwaveclassificationusinglongshorttermmemorynetworkbasedopticalpredictor AT aloksharma brainwaveclassificationusinglongshorttermmemorynetworkbasedopticalpredictor AT tatsuhikotsunoda brainwaveclassificationusinglongshorttermmemorynetworkbasedopticalpredictor
_version_	1718388326694649856

Brain wave classification using long short-term memory network based OPTICAL predictor

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