Brain Activity Recognition Method Based on Attention-Based RNN Mode

Brain activity recognition based on electroencephalography (EEG) marks a major research orientation in intelligent medicine, especially in human intention prediction, human–computer control and neurological diagnosis. The literature research mainly focuses on the recognition of single-person binary...

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Autores principales:	Song Zhou, Tianhan Gao
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	brain activity recognition EEG attention-based RNN model XGBoot classifier brain–computer interface 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/3a7c6af6be544024a54f5dc37de2384e
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spelling	oai:doaj.org-article:3a7c6af6be544024a54f5dc37de2384e2021-11-11T15:24:09ZBrain Activity Recognition Method Based on Attention-Based RNN Mode10.3390/app1121104252076-3417https://doaj.org/article/3a7c6af6be544024a54f5dc37de2384e2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10425https://doaj.org/toc/2076-3417Brain activity recognition based on electroencephalography (EEG) marks a major research orientation in intelligent medicine, especially in human intention prediction, human–computer control and neurological diagnosis. The literature research mainly focuses on the recognition of single-person binary brain activity, which is limited in the more extensive and complex scenarios. Therefore, brain activity recognition in multiperson and multi-objective scenarios has aroused increasingly more attention. Another challenge is the reduction of recognition accuracy caused by the interface of external noise as well as EEG’s low signal-to-noise ratio. In addition, traditional EEG feature analysis proves to be time-intensive and it relies heavily on mature experience. The paper proposes a novel EEG recognition method to address the above issues. The basic feature of EEG is first analyzed according to the band of EEG. The attention-based RNN model is then adopted to eliminate the interference to achieve the purpose of automatic recognition of the original EEG signal. Finally, we evaluate the proposed method with public and local data sets of EEG and perform lots of tests to investigate how factors affect the results of recognition. As shown by the test results, compared with some typical EEG recognition methods, the proposed method owns better recognition accuracy and suitability in multi-objective task scenarios.Song ZhouTianhan GaoMDPI AGarticlebrain activity recognitionEEGattention-based RNN modelXGBoot classifierbrain–computer interfaceTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10425, p 10425 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	brain activity recognition EEG attention-based RNN model XGBoot classifier brain–computer interface Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	brain activity recognition EEG attention-based RNN model XGBoot classifier brain–computer interface Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Song Zhou Tianhan Gao Brain Activity Recognition Method Based on Attention-Based RNN Mode
description	Brain activity recognition based on electroencephalography (EEG) marks a major research orientation in intelligent medicine, especially in human intention prediction, human–computer control and neurological diagnosis. The literature research mainly focuses on the recognition of single-person binary brain activity, which is limited in the more extensive and complex scenarios. Therefore, brain activity recognition in multiperson and multi-objective scenarios has aroused increasingly more attention. Another challenge is the reduction of recognition accuracy caused by the interface of external noise as well as EEG’s low signal-to-noise ratio. In addition, traditional EEG feature analysis proves to be time-intensive and it relies heavily on mature experience. The paper proposes a novel EEG recognition method to address the above issues. The basic feature of EEG is first analyzed according to the band of EEG. The attention-based RNN model is then adopted to eliminate the interference to achieve the purpose of automatic recognition of the original EEG signal. Finally, we evaluate the proposed method with public and local data sets of EEG and perform lots of tests to investigate how factors affect the results of recognition. As shown by the test results, compared with some typical EEG recognition methods, the proposed method owns better recognition accuracy and suitability in multi-objective task scenarios.
format	article
author	Song Zhou Tianhan Gao
author_facet	Song Zhou Tianhan Gao
author_sort	Song Zhou
title	Brain Activity Recognition Method Based on Attention-Based RNN Mode
title_short	Brain Activity Recognition Method Based on Attention-Based RNN Mode
title_full	Brain Activity Recognition Method Based on Attention-Based RNN Mode
title_fullStr	Brain Activity Recognition Method Based on Attention-Based RNN Mode
title_full_unstemmed	Brain Activity Recognition Method Based on Attention-Based RNN Mode
title_sort	brain activity recognition method based on attention-based rnn mode
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3a7c6af6be544024a54f5dc37de2384e
work_keys_str_mv	AT songzhou brainactivityrecognitionmethodbasedonattentionbasedrnnmode AT tianhangao brainactivityrecognitionmethodbasedonattentionbasedrnnmode
_version_	1718435360252362752

Brain Activity Recognition Method Based on Attention-Based RNN Mode

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