Complex brain activity analysis and recognition based on multiagent methods

Hao Lan Zhang; Jiming LIU; Margaret G. Dowens

doi:10.1002/cpe.5855

Complex brain activity analysis and recognition based on multiagent methods

Hao Lan Zhang^*, Jiming LIU, Margaret G. Dowens

^*Corresponding author for this work

Department of Computer Science

Research output: Contribution to journal › Journal article › peer-review

3 Citations (Scopus)

Abstract

Brain activity recognition research has been a challenging area for many decades since Hans Berger described electroencephalogram (EEG) in 1929. Many previous researches cannot successfully identify EEG status due to dynamic brain activities and complicated brain correlation. This article adopts multiagent-based methods to analyze EEG datasets, which can enhance the analytical efficiency through incorporating autonomous, self-coordination characteristics of agents. Intelligent agents are autonomous applications that can improve system compatibility. The preliminary results indicate that the combination of time-dependency correlation method with multiagent method is an efficient solution for brain activity recognition.

Original language	English
Article number	e5855
Journal	Concurrency Computation Practice and Experience
Volume	34
Issue number	8
Early online date	13 Jun 2020
DOIs	https://doi.org/10.1002/cpe.5855
Publication status	Published - 10 Apr 2022

Scopus Subject Areas

Software
Theoretical Computer Science
Computer Science Applications
Computer Networks and Communications
Computational Theory and Mathematics

User-Defined Keywords

AOCs
brain informatics
EEG pattern recognition
multiagents

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10.1002/cpe.5855

Cite this

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title = "Complex brain activity analysis and recognition based on multiagent methods",

abstract = "Brain activity recognition research has been a challenging area for many decades since Hans Berger described electroencephalogram (EEG) in 1929. Many previous researches cannot successfully identify EEG status due to dynamic brain activities and complicated brain correlation. This article adopts multiagent-based methods to analyze EEG datasets, which can enhance the analytical efficiency through incorporating autonomous, self-coordination characteristics of agents. Intelligent agents are autonomous applications that can improve system compatibility. The preliminary results indicate that the combination of time-dependency correlation method with multiagent method is an efficient solution for brain activity recognition.",

keywords = "AOCs, brain informatics, EEG pattern recognition, multiagents",

author = "Zhang, {Hao Lan} and Jiming LIU and Dowens, {Margaret G.}",

note = "Funding Information: This work is supported by Zhejiang Natural Science Fund (LY19F030010), Zhejiang Philosophy and Social Sciences Fund (20NDJC216YB), Ningbo Natural Science Fund (No.2019A610083), Ningbo Innovation Team (No.2016C11024), National Natural Science Fund of China (No. 61572022). Funding Information: National Natural Science Foundation of China, 61572022; Natural Science Foundation of Zhejiang Province, LY19F030010; Ningbo Innovation Team, No.2016C11024; Ningbo Natural Science Fund, No.2019A610083; Zhejiang Philosophy and Social Sciences Fund, 20NDJC216YB Funding information ",

year = "2022",

month = apr,

day = "10",

doi = "10.1002/cpe.5855",

language = "English",

volume = "34",

journal = "Concurrency Computation Practice and Experience",

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T1 - Complex brain activity analysis and recognition based on multiagent methods

AU - Zhang, Hao Lan

AU - LIU, Jiming

AU - Dowens, Margaret G.

N1 - Funding Information: This work is supported by Zhejiang Natural Science Fund (LY19F030010), Zhejiang Philosophy and Social Sciences Fund (20NDJC216YB), Ningbo Natural Science Fund (No.2019A610083), Ningbo Innovation Team (No.2016C11024), National Natural Science Fund of China (No. 61572022). Funding Information: National Natural Science Foundation of China, 61572022; Natural Science Foundation of Zhejiang Province, LY19F030010; Ningbo Innovation Team, No.2016C11024; Ningbo Natural Science Fund, No.2019A610083; Zhejiang Philosophy and Social Sciences Fund, 20NDJC216YB Funding information

PY - 2022/4/10

Y1 - 2022/4/10

N2 - Brain activity recognition research has been a challenging area for many decades since Hans Berger described electroencephalogram (EEG) in 1929. Many previous researches cannot successfully identify EEG status due to dynamic brain activities and complicated brain correlation. This article adopts multiagent-based methods to analyze EEG datasets, which can enhance the analytical efficiency through incorporating autonomous, self-coordination characteristics of agents. Intelligent agents are autonomous applications that can improve system compatibility. The preliminary results indicate that the combination of time-dependency correlation method with multiagent method is an efficient solution for brain activity recognition.

AB - Brain activity recognition research has been a challenging area for many decades since Hans Berger described electroencephalogram (EEG) in 1929. Many previous researches cannot successfully identify EEG status due to dynamic brain activities and complicated brain correlation. This article adopts multiagent-based methods to analyze EEG datasets, which can enhance the analytical efficiency through incorporating autonomous, self-coordination characteristics of agents. Intelligent agents are autonomous applications that can improve system compatibility. The preliminary results indicate that the combination of time-dependency correlation method with multiagent method is an efficient solution for brain activity recognition.

KW - AOCs

KW - brain informatics

KW - EEG pattern recognition

KW - multiagents

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U2 - 10.1002/cpe.5855

DO - 10.1002/cpe.5855

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JF - Concurrency Computation Practice and Experience

IS - 8

M1 - e5855

ER -

Complex brain activity analysis and recognition based on multiagent methods

Abstract

Scopus Subject Areas

User-Defined Keywords

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