A survey of brain network analysis by electroencephalographic signals
Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of fun...
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| Veröffentlicht in: | Cognitive neurodynamics 2022-02, Vol.16 (1), p.17-41 |
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| creator | Luo, Cuihua Li, Fali Li, Peiyang Yi, Chanlin Li, Chunbo Tao, Qin Zhang, Xiabing Si, Yajing Yao, Dezhong Yin, Gang Song, Pengyun Wang, Huazhang Xu, Peng |
| description | Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end. |
| doi_str_mv | 10.1007/s11571-021-09689-8 |
| format | Article |
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The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.</description><identifier>ISSN: 1871-4080</identifier><identifier>EISSN: 1871-4099</identifier><identifier>DOI: 10.1007/s11571-021-09689-8</identifier><identifier>PMID: 35126769</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Algorithms ; Artificial Intelligence ; Biochemistry ; Biomedical and Life Sciences ; Biomedicine ; Brain ; Cerebral cortex ; Cognitive ability ; Cognitive Psychology ; Computer Science ; Disorders ; EEG ; Electroencephalography ; Intelligence ; Network analysis ; Neuroplasticity ; Neurosciences ; Reconfiguration ; Research Article</subject><ispartof>Cognitive neurodynamics, 2022-02, Vol.16 (1), p.17-41</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2021</rights><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2021.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c441t-4e5d2f5f0fac918460720116e67e54f9717412820393bc56b4a6b8fca9249e263</citedby><cites>FETCH-LOGICAL-c441t-4e5d2f5f0fac918460720116e67e54f9717412820393bc56b4a6b8fca9249e263</cites><orcidid>0000-0002-7932-0386</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8807775/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918680055?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,21367,27901,27902,33721,41464,42533,43781,51294,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35126769$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Luo, Cuihua</creatorcontrib><creatorcontrib>Li, Fali</creatorcontrib><creatorcontrib>Li, Peiyang</creatorcontrib><creatorcontrib>Yi, Chanlin</creatorcontrib><creatorcontrib>Li, Chunbo</creatorcontrib><creatorcontrib>Tao, Qin</creatorcontrib><creatorcontrib>Zhang, Xiabing</creatorcontrib><creatorcontrib>Si, Yajing</creatorcontrib><creatorcontrib>Yao, Dezhong</creatorcontrib><creatorcontrib>Yin, Gang</creatorcontrib><creatorcontrib>Song, Pengyun</creatorcontrib><creatorcontrib>Wang, Huazhang</creatorcontrib><creatorcontrib>Xu, Peng</creatorcontrib><title>A survey of brain network analysis by electroencephalographic signals</title><title>Cognitive neurodynamics</title><addtitle>Cogn Neurodyn</addtitle><addtitle>Cogn Neurodyn</addtitle><description>Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Biochemistry</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Brain</subject><subject>Cerebral cortex</subject><subject>Cognitive ability</subject><subject>Cognitive Psychology</subject><subject>Computer Science</subject><subject>Disorders</subject><subject>EEG</subject><subject>Electroencephalography</subject><subject>Intelligence</subject><subject>Network analysis</subject><subject>Neuroplasticity</subject><subject>Neurosciences</subject><subject>Reconfiguration</subject><subject>Research Article</subject><issn>1871-4080</issn><issn>1871-4099</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kMtOwzAQRS0EoqXwAyxQJNYB23H82CBVVXlIldjA2nJcJ01J42AnRfl7XFIKbFiMZuS5c2d8ALhE8AZByG49QilDMcQhBOUi5kdgjHh4IlCI40PN4Qiceb-GMKUckVMwSlKEKaNiDObTyHdua_rI5lHmVFlHtWk_rHuLVK2q3pc-yvrIVEa3zppam2alKls41axKHfmyCCp_Dk7ykMzFPk_A6_38ZfYYL54fnmbTRawJQW1MTLrEeZrDXGmBOKGQYYgQNZSZlOSCIUYQ5hgmIsl0SjOiaMZzrQQmwmCaTMDd4Nt02cYstalbpyrZuHKjXC-tKuXfTl2uZGG3knPIGEuDwfXewNn3zvhWrm3ndl-QOFxEeWC0U-FBpZ313pn8sAFBuUMvB_QyoJdf6CUPQ1e_bzuMfLMOgmQQ-NCqC-N-dv9j-wlVC4-d</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Luo, Cuihua</creator><creator>Li, Fali</creator><creator>Li, Peiyang</creator><creator>Yi, Chanlin</creator><creator>Li, Chunbo</creator><creator>Tao, Qin</creator><creator>Zhang, Xiabing</creator><creator>Si, Yajing</creator><creator>Yao, Dezhong</creator><creator>Yin, Gang</creator><creator>Song, Pengyun</creator><creator>Wang, Huazhang</creator><creator>Xu, Peng</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M7P</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-7932-0386</orcidid></search><sort><creationdate>20220201</creationdate><title>A survey of brain network analysis by electroencephalographic signals</title><author>Luo, Cuihua ; Li, Fali ; Li, Peiyang ; Yi, Chanlin ; Li, Chunbo ; Tao, Qin ; Zhang, Xiabing ; Si, Yajing ; Yao, Dezhong ; Yin, Gang ; Song, Pengyun ; Wang, Huazhang ; Xu, Peng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c441t-4e5d2f5f0fac918460720116e67e54f9717412820393bc56b4a6b8fca9249e263</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Biochemistry</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Brain</topic><topic>Cerebral cortex</topic><topic>Cognitive ability</topic><topic>Cognitive Psychology</topic><topic>Computer Science</topic><topic>Disorders</topic><topic>EEG</topic><topic>Electroencephalography</topic><topic>Intelligence</topic><topic>Network analysis</topic><topic>Neuroplasticity</topic><topic>Neurosciences</topic><topic>Reconfiguration</topic><topic>Research Article</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Luo, Cuihua</creatorcontrib><creatorcontrib>Li, Fali</creatorcontrib><creatorcontrib>Li, Peiyang</creatorcontrib><creatorcontrib>Yi, Chanlin</creatorcontrib><creatorcontrib>Li, Chunbo</creatorcontrib><creatorcontrib>Tao, Qin</creatorcontrib><creatorcontrib>Zhang, Xiabing</creatorcontrib><creatorcontrib>Si, Yajing</creatorcontrib><creatorcontrib>Yao, Dezhong</creatorcontrib><creatorcontrib>Yin, Gang</creatorcontrib><creatorcontrib>Song, Pengyun</creatorcontrib><creatorcontrib>Wang, Huazhang</creatorcontrib><creatorcontrib>Xu, Peng</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Biological Science Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Cognitive neurodynamics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Luo, Cuihua</au><au>Li, Fali</au><au>Li, Peiyang</au><au>Yi, Chanlin</au><au>Li, Chunbo</au><au>Tao, Qin</au><au>Zhang, Xiabing</au><au>Si, Yajing</au><au>Yao, Dezhong</au><au>Yin, Gang</au><au>Song, Pengyun</au><au>Wang, Huazhang</au><au>Xu, Peng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A survey of brain network analysis by electroencephalographic signals</atitle><jtitle>Cognitive neurodynamics</jtitle><stitle>Cogn Neurodyn</stitle><addtitle>Cogn Neurodyn</addtitle><date>2022-02-01</date><risdate>2022</risdate><volume>16</volume><issue>1</issue><spage>17</spage><epage>41</epage><pages>17-41</pages><issn>1871-4080</issn><eissn>1871-4099</eissn><abstract>Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. 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| subjects | Algorithms Artificial Intelligence Biochemistry Biomedical and Life Sciences Biomedicine Brain Cerebral cortex Cognitive ability Cognitive Psychology Computer Science Disorders EEG Electroencephalography Intelligence Network analysis Neuroplasticity Neurosciences Reconfiguration Research Article |
| title | A survey of brain network analysis by electroencephalographic signals |
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