Individualized pattern recognition for detecting mind wandering from EEG during live lectures
The ability to detect mind wandering as it occurs is an important step towards improving our understanding of this phenomenon and studying its effects on learning and performance. Current detection methods typically rely on observable behaviour in laboratory settings, which do not capture the underl...
Gespeichert in:
| Veröffentlicht in: | PloS one 2019-09, Vol.14 (9), p.e0222276-e0222276 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e0222276 |
|---|---|
| container_issue | 9 |
| container_start_page | e0222276 |
| container_title | PloS one |
| container_volume | 14 |
| creator | Dhindsa, Kiret Acai, Anita Wagner, Natalie Bosynak, Dan Kelly, Stephen Bhandari, Mohit Petrisor, Brad Sonnadara, Ranil R |
| description | The ability to detect mind wandering as it occurs is an important step towards improving our understanding of this phenomenon and studying its effects on learning and performance. Current detection methods typically rely on observable behaviour in laboratory settings, which do not capture the underlying neural processes and may not translate well into real-world settings. We address both of these issues by recording electroencephalography (EEG) simultaneously from 15 participants during live lectures on research in orthopedic surgery. We performed traditional group-level analysis and found neural correlates of mind wandering during live lectures that are similar to those found in some laboratory studies, including a decrease in occipitoparietal alpha power and frontal, temporal, and occipital beta power. However, individual-level analysis of these same data revealed that patterns of brain activity associated with mind wandering were more broadly distributed and highly individualized than revealed in the group-level analysis.
To apply these findings to mind wandering detection, we used a data-driven method known as common spatial patterns to discover scalp topologies for each individual that reflects their differences in brain activity when mind wandering versus attending to lectures. This approach avoids reliance on known neural correlates primarily established through group-level statistics. Using this method for individual-level machine learning of mind wandering from EEG, we were able to achieve an average detection accuracy of 80-83%.
Modelling mind wandering at the individual level may reveal important details about its neural correlates that are not reflected when using traditional observational and statistical methods. Using machine learning techniques for this purpose can provide new insight into the varieties of neural activity involved in mind wandering, while also enabling real-time detection of mind wandering in naturalistic settings. |
| doi_str_mv | 10.1371/journal.pone.0222276 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2289561939</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A599263205</galeid><doaj_id>oai_doaj_org_article_d29dfe7919ca449883361983e36b7ef5</doaj_id><sourcerecordid>A599263205</sourcerecordid><originalsourceid>FETCH-LOGICAL-c758t-f6f389bd30e314d27dcadaec7e106ffbd2f3fbf138714e5db0ed2110c3c459e53</originalsourceid><addsrcrecordid>eNqNk1trFDEUxwdRbK1-A9EBQfRh11zm-iKUstaFQsHbm4RMcjKbJZOsycx6-fRmutOyI30wecjtd_4nOTknSZ5jtMS0xO-2bvCWm-XOWVgiEltZPEhOcU3JoiCIPjyanyRPQtgilNOqKB4nJxTnmBaEnCbf11bqvZYDN_oPyHTH-x68TT0I11rda2dT5XwqoQfRa9umnbYy_cmtBD8ulXddulpdpnK4WRu9h9REdvAQniaPFDcBnk3jWfL1w-rLxcfF1fXl-uL8aiHKvOoXqlC0qhtJEVCcSVJKwSUHUQJGhVKNJIqqRmFalTiDXDYIJMEYCSqyvIacniUvD7o74wKbIhMYIVWdFzEKdSTWB0I6vmU7rzvufzPHNbvZcL5l3PdaGGCS1FJBWeNa8Cyrq4rSqFFRoEVTghq9vZ-8DU0HUoDtPTcz0fmJ1RvWuj0ryoxkqIgCbyYB734MEHrW6SDAGG7BDeO9a1RFxzmJ6Kt_0PtfN1Etjw_QVrnoV4yi7Dyva1JQgsZ7L--hYpfQaRHTSOm4PzN4OzOITA-_-pYPIbD150__z15_m7Ovj9gNcNNvgjPDmG1hDmYHUHgXggd1F2SM2FgFt9FgYxWwqQqi2YvjD7ozuk17-hcBkwMv</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2289561939</pqid></control><display><type>article</type><title>Individualized pattern recognition for detecting mind wandering from EEG during live lectures</title><source>Electronic Journals Library</source><source>MEDLINE</source><source>Public Library of Science</source><source>DOAJ Directory of Open Access Journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Dhindsa, Kiret ; Acai, Anita ; Wagner, Natalie ; Bosynak, Dan ; Kelly, Stephen ; Bhandari, Mohit ; Petrisor, Brad ; Sonnadara, Ranil R</creator><contributor>D'Mello, Sidney</contributor><creatorcontrib>Dhindsa, Kiret ; Acai, Anita ; Wagner, Natalie ; Bosynak, Dan ; Kelly, Stephen ; Bhandari, Mohit ; Petrisor, Brad ; Sonnadara, Ranil R ; D'Mello, Sidney</creatorcontrib><description>The ability to detect mind wandering as it occurs is an important step towards improving our understanding of this phenomenon and studying its effects on learning and performance. Current detection methods typically rely on observable behaviour in laboratory settings, which do not capture the underlying neural processes and may not translate well into real-world settings. We address both of these issues by recording electroencephalography (EEG) simultaneously from 15 participants during live lectures on research in orthopedic surgery. We performed traditional group-level analysis and found neural correlates of mind wandering during live lectures that are similar to those found in some laboratory studies, including a decrease in occipitoparietal alpha power and frontal, temporal, and occipital beta power. However, individual-level analysis of these same data revealed that patterns of brain activity associated with mind wandering were more broadly distributed and highly individualized than revealed in the group-level analysis.
To apply these findings to mind wandering detection, we used a data-driven method known as common spatial patterns to discover scalp topologies for each individual that reflects their differences in brain activity when mind wandering versus attending to lectures. This approach avoids reliance on known neural correlates primarily established through group-level statistics. Using this method for individual-level machine learning of mind wandering from EEG, we were able to achieve an average detection accuracy of 80-83%.
Modelling mind wandering at the individual level may reveal important details about its neural correlates that are not reflected when using traditional observational and statistical methods. Using machine learning techniques for this purpose can provide new insight into the varieties of neural activity involved in mind wandering, while also enabling real-time detection of mind wandering in naturalistic settings.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0222276</identifier><identifier>PMID: 31513622</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Artificial intelligence ; Attention - physiology ; Biology and Life Sciences ; Brain ; Brain - diagnostic imaging ; Brain research ; Comprehension - physiology ; Computer and Information Sciences ; Correlation analysis ; EEG ; Electroencephalography ; Electroencephalography - methods ; Female ; Humans ; Learning algorithms ; Localization ; Machine Learning ; Male ; Medical research ; Medicine and Health Sciences ; Methods ; Model accuracy ; Neurophysiology ; Neurosciences ; Orthopedic surgery ; Orthopedics ; Pattern recognition ; Physical Sciences ; Public speaking ; Recording ; Research and Analysis Methods ; Research methodology ; Social Sciences ; Statistical analysis ; Statistical methods ; Studies ; Surgery ; Thinking - physiology ; Topology ; Young Adult</subject><ispartof>PloS one, 2019-09, Vol.14 (9), p.e0222276-e0222276</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Dhindsa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Dhindsa et al 2019 Dhindsa et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c758t-f6f389bd30e314d27dcadaec7e106ffbd2f3fbf138714e5db0ed2110c3c459e53</citedby><cites>FETCH-LOGICAL-c758t-f6f389bd30e314d27dcadaec7e106ffbd2f3fbf138714e5db0ed2110c3c459e53</cites><orcidid>0000-0003-4849-732X ; 0000-0002-9901-2946</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/PMC6742406/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6742406/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31513622$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>D'Mello, Sidney</contributor><creatorcontrib>Dhindsa, Kiret</creatorcontrib><creatorcontrib>Acai, Anita</creatorcontrib><creatorcontrib>Wagner, Natalie</creatorcontrib><creatorcontrib>Bosynak, Dan</creatorcontrib><creatorcontrib>Kelly, Stephen</creatorcontrib><creatorcontrib>Bhandari, Mohit</creatorcontrib><creatorcontrib>Petrisor, Brad</creatorcontrib><creatorcontrib>Sonnadara, Ranil R</creatorcontrib><title>Individualized pattern recognition for detecting mind wandering from EEG during live lectures</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The ability to detect mind wandering as it occurs is an important step towards improving our understanding of this phenomenon and studying its effects on learning and performance. Current detection methods typically rely on observable behaviour in laboratory settings, which do not capture the underlying neural processes and may not translate well into real-world settings. We address both of these issues by recording electroencephalography (EEG) simultaneously from 15 participants during live lectures on research in orthopedic surgery. We performed traditional group-level analysis and found neural correlates of mind wandering during live lectures that are similar to those found in some laboratory studies, including a decrease in occipitoparietal alpha power and frontal, temporal, and occipital beta power. However, individual-level analysis of these same data revealed that patterns of brain activity associated with mind wandering were more broadly distributed and highly individualized than revealed in the group-level analysis.
To apply these findings to mind wandering detection, we used a data-driven method known as common spatial patterns to discover scalp topologies for each individual that reflects their differences in brain activity when mind wandering versus attending to lectures. This approach avoids reliance on known neural correlates primarily established through group-level statistics. Using this method for individual-level machine learning of mind wandering from EEG, we were able to achieve an average detection accuracy of 80-83%.
Modelling mind wandering at the individual level may reveal important details about its neural correlates that are not reflected when using traditional observational and statistical methods. Using machine learning techniques for this purpose can provide new insight into the varieties of neural activity involved in mind wandering, while also enabling real-time detection of mind wandering in naturalistic settings.</description><subject>Adult</subject><subject>Artificial intelligence</subject><subject>Attention - physiology</subject><subject>Biology and Life Sciences</subject><subject>Brain</subject><subject>Brain - diagnostic imaging</subject><subject>Brain research</subject><subject>Comprehension - physiology</subject><subject>Computer and Information Sciences</subject><subject>Correlation analysis</subject><subject>EEG</subject><subject>Electroencephalography</subject><subject>Electroencephalography - methods</subject><subject>Female</subject><subject>Humans</subject><subject>Learning algorithms</subject><subject>Localization</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Model accuracy</subject><subject>Neurophysiology</subject><subject>Neurosciences</subject><subject>Orthopedic surgery</subject><subject>Orthopedics</subject><subject>Pattern recognition</subject><subject>Physical Sciences</subject><subject>Public speaking</subject><subject>Recording</subject><subject>Research and Analysis Methods</subject><subject>Research methodology</subject><subject>Social Sciences</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Studies</subject><subject>Surgery</subject><subject>Thinking - physiology</subject><subject>Topology</subject><subject>Young Adult</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk1trFDEUxwdRbK1-A9EBQfRh11zm-iKUstaFQsHbm4RMcjKbJZOsycx6-fRmutOyI30wecjtd_4nOTknSZ5jtMS0xO-2bvCWm-XOWVgiEltZPEhOcU3JoiCIPjyanyRPQtgilNOqKB4nJxTnmBaEnCbf11bqvZYDN_oPyHTH-x68TT0I11rda2dT5XwqoQfRa9umnbYy_cmtBD8ulXddulpdpnK4WRu9h9REdvAQniaPFDcBnk3jWfL1w-rLxcfF1fXl-uL8aiHKvOoXqlC0qhtJEVCcSVJKwSUHUQJGhVKNJIqqRmFalTiDXDYIJMEYCSqyvIacniUvD7o74wKbIhMYIVWdFzEKdSTWB0I6vmU7rzvufzPHNbvZcL5l3PdaGGCS1FJBWeNa8Cyrq4rSqFFRoEVTghq9vZ-8DU0HUoDtPTcz0fmJ1RvWuj0ryoxkqIgCbyYB734MEHrW6SDAGG7BDeO9a1RFxzmJ6Kt_0PtfN1Etjw_QVrnoV4yi7Dyva1JQgsZ7L--hYpfQaRHTSOm4PzN4OzOITA-_-pYPIbD150__z15_m7Ovj9gNcNNvgjPDmG1hDmYHUHgXggd1F2SM2FgFt9FgYxWwqQqi2YvjD7ozuk17-hcBkwMv</recordid><startdate>20190912</startdate><enddate>20190912</enddate><creator>Dhindsa, Kiret</creator><creator>Acai, Anita</creator><creator>Wagner, Natalie</creator><creator>Bosynak, Dan</creator><creator>Kelly, Stephen</creator><creator>Bhandari, Mohit</creator><creator>Petrisor, Brad</creator><creator>Sonnadara, Ranil R</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4849-732X</orcidid><orcidid>https://orcid.org/0000-0002-9901-2946</orcidid></search><sort><creationdate>20190912</creationdate><title>Individualized pattern recognition for detecting mind wandering from EEG during live lectures</title><author>Dhindsa, Kiret ; Acai, Anita ; Wagner, Natalie ; Bosynak, Dan ; Kelly, Stephen ; Bhandari, Mohit ; Petrisor, Brad ; Sonnadara, Ranil R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c758t-f6f389bd30e314d27dcadaec7e106ffbd2f3fbf138714e5db0ed2110c3c459e53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>Artificial intelligence</topic><topic>Attention - physiology</topic><topic>Biology and Life Sciences</topic><topic>Brain</topic><topic>Brain - diagnostic imaging</topic><topic>Brain research</topic><topic>Comprehension - physiology</topic><topic>Computer and Information Sciences</topic><topic>Correlation analysis</topic><topic>EEG</topic><topic>Electroencephalography</topic><topic>Electroencephalography - methods</topic><topic>Female</topic><topic>Humans</topic><topic>Learning algorithms</topic><topic>Localization</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Medical research</topic><topic>Medicine and Health Sciences</topic><topic>Methods</topic><topic>Model accuracy</topic><topic>Neurophysiology</topic><topic>Neurosciences</topic><topic>Orthopedic surgery</topic><topic>Orthopedics</topic><topic>Pattern recognition</topic><topic>Physical Sciences</topic><topic>Public speaking</topic><topic>Recording</topic><topic>Research and Analysis Methods</topic><topic>Research methodology</topic><topic>Social Sciences</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Studies</topic><topic>Surgery</topic><topic>Thinking - physiology</topic><topic>Topology</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dhindsa, Kiret</creatorcontrib><creatorcontrib>Acai, Anita</creatorcontrib><creatorcontrib>Wagner, Natalie</creatorcontrib><creatorcontrib>Bosynak, Dan</creatorcontrib><creatorcontrib>Kelly, Stephen</creatorcontrib><creatorcontrib>Bhandari, Mohit</creatorcontrib><creatorcontrib>Petrisor, Brad</creatorcontrib><creatorcontrib>Sonnadara, Ranil R</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medicine (ProQuest)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database (ProQuest)</collection><collection>Technology Research Database</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>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Database (1962 - current)</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>Biological Sciences</collection><collection>Agriculture Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>ProQuest Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</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>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dhindsa, Kiret</au><au>Acai, Anita</au><au>Wagner, Natalie</au><au>Bosynak, Dan</au><au>Kelly, Stephen</au><au>Bhandari, Mohit</au><au>Petrisor, Brad</au><au>Sonnadara, Ranil R</au><au>D'Mello, Sidney</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Individualized pattern recognition for detecting mind wandering from EEG during live lectures</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-09-12</date><risdate>2019</risdate><volume>14</volume><issue>9</issue><spage>e0222276</spage><epage>e0222276</epage><pages>e0222276-e0222276</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The ability to detect mind wandering as it occurs is an important step towards improving our understanding of this phenomenon and studying its effects on learning and performance. Current detection methods typically rely on observable behaviour in laboratory settings, which do not capture the underlying neural processes and may not translate well into real-world settings. We address both of these issues by recording electroencephalography (EEG) simultaneously from 15 participants during live lectures on research in orthopedic surgery. We performed traditional group-level analysis and found neural correlates of mind wandering during live lectures that are similar to those found in some laboratory studies, including a decrease in occipitoparietal alpha power and frontal, temporal, and occipital beta power. However, individual-level analysis of these same data revealed that patterns of brain activity associated with mind wandering were more broadly distributed and highly individualized than revealed in the group-level analysis.
To apply these findings to mind wandering detection, we used a data-driven method known as common spatial patterns to discover scalp topologies for each individual that reflects their differences in brain activity when mind wandering versus attending to lectures. This approach avoids reliance on known neural correlates primarily established through group-level statistics. Using this method for individual-level machine learning of mind wandering from EEG, we were able to achieve an average detection accuracy of 80-83%.
Modelling mind wandering at the individual level may reveal important details about its neural correlates that are not reflected when using traditional observational and statistical methods. Using machine learning techniques for this purpose can provide new insight into the varieties of neural activity involved in mind wandering, while also enabling real-time detection of mind wandering in naturalistic settings.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31513622</pmid><doi>10.1371/journal.pone.0222276</doi><tpages>e0222276</tpages><orcidid>https://orcid.org/0000-0003-4849-732X</orcidid><orcidid>https://orcid.org/0000-0002-9901-2946</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2019-09, Vol.14 (9), p.e0222276-e0222276 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2289561939 |
| source | Electronic Journals Library; MEDLINE; Public Library of Science; DOAJ Directory of Open Access Journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adult Artificial intelligence Attention - physiology Biology and Life Sciences Brain Brain - diagnostic imaging Brain research Comprehension - physiology Computer and Information Sciences Correlation analysis EEG Electroencephalography Electroencephalography - methods Female Humans Learning algorithms Localization Machine Learning Male Medical research Medicine and Health Sciences Methods Model accuracy Neurophysiology Neurosciences Orthopedic surgery Orthopedics Pattern recognition Physical Sciences Public speaking Recording Research and Analysis Methods Research methodology Social Sciences Statistical analysis Statistical methods Studies Surgery Thinking - physiology Topology Young Adult |
| title | Individualized pattern recognition for detecting mind wandering from EEG during live lectures |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-29T10%3A04%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Individualized%20pattern%20recognition%20for%20detecting%20mind%20wandering%20from%20EEG%20during%20live%20lectures&rft.jtitle=PloS%20one&rft.au=Dhindsa,%20Kiret&rft.date=2019-09-12&rft.volume=14&rft.issue=9&rft.spage=e0222276&rft.epage=e0222276&rft.pages=e0222276-e0222276&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0222276&rft_dat=%3Cgale_plos_%3EA599263205%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2289561939&rft_id=info:pmid/31513622&rft_galeid=A599263205&rft_doaj_id=oai_doaj_org_article_d29dfe7919ca449883361983e36b7ef5&rfr_iscdi=true |