Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm

The effective decoding of motor imagination EEG signals depends on significant temporal, spatial, and frequency features. For example, the motor imagination of the single limbs is embodied in the μ (8–13 Hz) rhythm and β (13–30 Hz) rhythm in frequency features. However, the significant temporal feat...

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Autores principales:	Jun Yang, Zhengmin Ma, Tao Shen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electroencephalogram decoding motor imagery common space pattern sliding window Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/94ad339006984180bfc994859f6dafc4
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id	oai:doaj.org-article:94ad339006984180bfc994859f6dafc4
record_format	dspace
spelling	oai:doaj.org-article:94ad339006984180bfc994859f6dafc42021-11-11T15:19:21ZMulti-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm10.3390/app1121102942076-3417https://doaj.org/article/94ad339006984180bfc994859f6dafc42021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10294https://doaj.org/toc/2076-3417The effective decoding of motor imagination EEG signals depends on significant temporal, spatial, and frequency features. For example, the motor imagination of the single limbs is embodied in the μ (8–13 Hz) rhythm and β (13–30 Hz) rhythm in frequency features. However, the significant temporal features are not necessarily manifested in the whole motor imagination process. This paper proposes a Multi-Time and Frequency band Common Space Pattern (MTF-CSP)-based feature extraction and EEG decoding method. The MTF-CSP learns effective motor imagination features from a weak Electroencephalogram (EEG), extracts the most effective time and frequency features, and identifies the motor imagination patterns. Specifically, multiple sliding window signals are cropped from the original signals. The multi-frequency band Common Space Pattern (CSP) features extracted from each sliding window signal are fed into multiple Support Vector Machine (SVM) classifiers with the same parameters. The Effective Duration (ED) algorithm and the Average Score (AS) algorithm are proposed to identify the recognition results of multiple time windows. The proposed method is trained and evaluated on the EEG data of nine subjects in the 2008 BCI-2a competition dataset, including a train dataset and a test dataset collected in other sessions. As a result, the average cross-session recognition accuracy of 78.7% was obtained on nine subjects, with a sliding window length of 1 s, a step length of 0.4 s, and the six windows. Experimental results showed the proposed MTF-CSP outperforming the compared machine learning and CSP-based methods using the original signals or other features such as time-frequency picture features in terms of accuracy. Further, it is shown that the performance of the AS algorithm is significantly better than that of the Max Voting algorithm adopted in other studies.Jun YangZhengmin MaTao ShenMDPI AGarticleelectroencephalogram decodingmotor imagerycommon space patternsliding windowTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10294, p 10294 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electroencephalogram decoding motor imagery common space pattern sliding window Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	electroencephalogram decoding motor imagery common space pattern sliding window Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Jun Yang Zhengmin Ma Tao Shen Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
description	The effective decoding of motor imagination EEG signals depends on significant temporal, spatial, and frequency features. For example, the motor imagination of the single limbs is embodied in the μ (8–13 Hz) rhythm and β (13–30 Hz) rhythm in frequency features. However, the significant temporal features are not necessarily manifested in the whole motor imagination process. This paper proposes a Multi-Time and Frequency band Common Space Pattern (MTF-CSP)-based feature extraction and EEG decoding method. The MTF-CSP learns effective motor imagination features from a weak Electroencephalogram (EEG), extracts the most effective time and frequency features, and identifies the motor imagination patterns. Specifically, multiple sliding window signals are cropped from the original signals. The multi-frequency band Common Space Pattern (CSP) features extracted from each sliding window signal are fed into multiple Support Vector Machine (SVM) classifiers with the same parameters. The Effective Duration (ED) algorithm and the Average Score (AS) algorithm are proposed to identify the recognition results of multiple time windows. The proposed method is trained and evaluated on the EEG data of nine subjects in the 2008 BCI-2a competition dataset, including a train dataset and a test dataset collected in other sessions. As a result, the average cross-session recognition accuracy of 78.7% was obtained on nine subjects, with a sliding window length of 1 s, a step length of 0.4 s, and the six windows. Experimental results showed the proposed MTF-CSP outperforming the compared machine learning and CSP-based methods using the original signals or other features such as time-frequency picture features in terms of accuracy. Further, it is shown that the performance of the AS algorithm is significantly better than that of the Max Voting algorithm adopted in other studies.
format	article
author	Jun Yang Zhengmin Ma Tao Shen
author_facet	Jun Yang Zhengmin Ma Tao Shen
author_sort	Jun Yang
title	Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
title_short	Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
title_full	Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
title_fullStr	Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
title_full_unstemmed	Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
title_sort	multi-time and multi-band csp motor imagery eeg feature classification algorithm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/94ad339006984180bfc994859f6dafc4
work_keys_str_mv	AT junyang multitimeandmultibandcspmotorimageryeegfeatureclassificationalgorithm AT zhengminma multitimeandmultibandcspmotorimageryeegfeatureclassificationalgorithm AT taoshen multitimeandmultibandcspmotorimageryeegfeatureclassificationalgorithm
_version_	1718435379061719040

Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm

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