A brain–computer interface for the continuous, real-time monitoring of working memory load in real-world environments
We developed a brain–computer interface (BCI) able to continuously monitor working memory (WM) load in real-time (considering the last 2.5 s of brain activity). The BCI is based on biomarkers derived from spectral properties of non-invasive electroencephalography (EEG), subsequently classified by a...
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| Veröffentlicht in: | Cognitive neurodynamics 2020-06, Vol.14 (3), p.301-321 |
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| container_title | Cognitive neurodynamics |
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| creator | Mora-Sánchez, Aldo Pulini, Alfredo-Aram Gaume, Antoine Dreyfus, Gérard Vialatte, François-Benoît |
| description | We developed a brain–computer interface (BCI) able to continuously monitor working memory (WM) load in real-time (considering the last 2.5 s of brain activity). The BCI is based on biomarkers derived from spectral properties of non-invasive electroencephalography (EEG), subsequently classified by a linear discriminant analysis classifier. The BCI was trained on a visual WM task, tested in a real-time visual WM task, and further validated in a real-time cross task (mental arithmetic). Throughout each trial of the cross task, subjects were given real or sham feedback about their WM load. At the end of the trial, subjects were asked whether the feedback provided was real or sham. The high rate of correct answers provided by the subjects validated not only the global behaviour of the WM-load feedback, but also its real-time dynamics. On average, subjects were able to provide a correct answer 82% of the time, with one subject having 100% accuracy. Possible cognitive and motor confounding factors were disentangled to support the claim that our EEG-based markers correspond indeed to WM. |
| doi_str_mv | 10.1007/s11571-020-09573-x |
| format | Article |
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The BCI is based on biomarkers derived from spectral properties of non-invasive electroencephalography (EEG), subsequently classified by a linear discriminant analysis classifier. The BCI was trained on a visual WM task, tested in a real-time visual WM task, and further validated in a real-time cross task (mental arithmetic). Throughout each trial of the cross task, subjects were given real or sham feedback about their WM load. At the end of the trial, subjects were asked whether the feedback provided was real or sham. The high rate of correct answers provided by the subjects validated not only the global behaviour of the WM-load feedback, but also its real-time dynamics. On average, subjects were able to provide a correct answer 82% of the time, with one subject having 100% accuracy. Possible cognitive and motor confounding factors were disentangled to support the claim that our EEG-based markers correspond indeed to WM.</description><identifier>ISSN: 1871-4080</identifier><identifier>EISSN: 1871-4099</identifier><identifier>DOI: 10.1007/s11571-020-09573-x</identifier><identifier>PMID: 32399073</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Algorithms ; Artificial Intelligence ; Biochemistry ; Biomarkers ; Biomedical and Life Sciences ; Biomedicine ; Brain ; Cognition & reasoning ; Cognitive ability ; Cognitive load ; Cognitive Psychology ; Communication ; Computer applications ; Computer Science ; Confounding (Statistics) ; Discriminant analysis ; EEG ; Electroencephalography ; Feedback ; Human-computer interface ; Implants ; Mathematics ; Memory ; Neurosciences ; Phonology ; Real time ; Research Article ; Short term memory ; Speech ; Time ; Visual tasks</subject><ispartof>Cognitive neurodynamics, 2020-06, Vol.14 (3), p.301-321</ispartof><rights>Springer Nature B.V. 2020</rights><rights>Springer Nature B.V. 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-9c1dd599cf60c68b2de70678651bc7733f11a99aed919138298d35598a0c288f3</citedby><cites>FETCH-LOGICAL-c474t-9c1dd599cf60c68b2de70678651bc7733f11a99aed919138298d35598a0c288f3</cites><orcidid>0000-0002-3456-714X</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/PMC7203264/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918680107?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,21367,27901,27902,33721,33722,41464,42533,43781,51294,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32399073$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mora-Sánchez, Aldo</creatorcontrib><creatorcontrib>Pulini, Alfredo-Aram</creatorcontrib><creatorcontrib>Gaume, Antoine</creatorcontrib><creatorcontrib>Dreyfus, Gérard</creatorcontrib><creatorcontrib>Vialatte, François-Benoît</creatorcontrib><title>A brain–computer interface for the continuous, real-time monitoring of working memory load in real-world environments</title><title>Cognitive neurodynamics</title><addtitle>Cogn Neurodyn</addtitle><addtitle>Cogn Neurodyn</addtitle><description>We developed a brain–computer interface (BCI) able to continuously monitor working memory (WM) load in real-time (considering the last 2.5 s of brain activity). The BCI is based on biomarkers derived from spectral properties of non-invasive electroencephalography (EEG), subsequently classified by a linear discriminant analysis classifier. The BCI was trained on a visual WM task, tested in a real-time visual WM task, and further validated in a real-time cross task (mental arithmetic). Throughout each trial of the cross task, subjects were given real or sham feedback about their WM load. At the end of the trial, subjects were asked whether the feedback provided was real or sham. The high rate of correct answers provided by the subjects validated not only the global behaviour of the WM-load feedback, but also its real-time dynamics. On average, subjects were able to provide a correct answer 82% of the time, with one subject having 100% accuracy. Possible cognitive and motor confounding factors were disentangled to support the claim that our EEG-based markers correspond indeed to WM.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Biochemistry</subject><subject>Biomarkers</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Brain</subject><subject>Cognition & reasoning</subject><subject>Cognitive ability</subject><subject>Cognitive load</subject><subject>Cognitive Psychology</subject><subject>Communication</subject><subject>Computer applications</subject><subject>Computer Science</subject><subject>Confounding (Statistics)</subject><subject>Discriminant analysis</subject><subject>EEG</subject><subject>Electroencephalography</subject><subject>Feedback</subject><subject>Human-computer interface</subject><subject>Implants</subject><subject>Mathematics</subject><subject>Memory</subject><subject>Neurosciences</subject><subject>Phonology</subject><subject>Real time</subject><subject>Research Article</subject><subject>Short term memory</subject><subject>Speech</subject><subject>Time</subject><subject>Visual tasks</subject><issn>1871-4080</issn><issn>1871-4099</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9UU1PHSEUJaZNta_9Ay4akm666NQLzAywaWKM1iYmbuya8BhGsQM8YcaPXf-D_9BfUp5jX2sXbuCGc-453HsQ2iXwhQDwvUxIw0kFFCqQDWfV7RbaIaI81SDlq00tYBu9zfkSoGkFqd-gbUaZlMDZDrrZx8ukXXj4dW-iX02jTdiFcvbaWNzHhMcLi00MowtTnPJnnKweqtF5i30MbozJhXMce3wT08916a2P6Q4PUXdFaaYXbOiwDdcuxeBtGPM79LrXQ7bvn-4F-nF0eHZwXJ2cfvt-sH9SmZrXYyUN6bpGStO3YFqxpJ3l0HLRNmRpOGesJ0RLqW0niSRMUCk61jRSaDBUiJ4t0NdZdzUtve1M8U56UKvkvE53KmqnniPBXajzeK04BUbbugh8ehJI8WqyeVTeZWOHQQdb9qFoDbRmjBb3Bfr4H_UyTimU8VSBRSuAlJ0vEJ1ZJsWck-03nyGg1rmqOVdVclWPuarb0vTh3zE2LX-CLAQ2E_JqHYhNf71fkP0NRryx1A</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Mora-Sánchez, Aldo</creator><creator>Pulini, Alfredo-Aram</creator><creator>Gaume, Antoine</creator><creator>Dreyfus, Gérard</creator><creator>Vialatte, François-Benoît</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7T9</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>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3456-714X</orcidid></search><sort><creationdate>20200601</creationdate><title>A brain–computer interface for the continuous, real-time monitoring of working memory load in real-world environments</title><author>Mora-Sánchez, Aldo ; Pulini, Alfredo-Aram ; Gaume, Antoine ; Dreyfus, Gérard ; Vialatte, François-Benoît</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-9c1dd599cf60c68b2de70678651bc7733f11a99aed919138298d35598a0c288f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Biochemistry</topic><topic>Biomarkers</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedicine</topic><topic>Brain</topic><topic>Cognition & reasoning</topic><topic>Cognitive ability</topic><topic>Cognitive load</topic><topic>Cognitive Psychology</topic><topic>Communication</topic><topic>Computer applications</topic><topic>Computer Science</topic><topic>Confounding (Statistics)</topic><topic>Discriminant analysis</topic><topic>EEG</topic><topic>Electroencephalography</topic><topic>Feedback</topic><topic>Human-computer interface</topic><topic>Implants</topic><topic>Mathematics</topic><topic>Memory</topic><topic>Neurosciences</topic><topic>Phonology</topic><topic>Real time</topic><topic>Research Article</topic><topic>Short term memory</topic><topic>Speech</topic><topic>Time</topic><topic>Visual tasks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mora-Sánchez, Aldo</creatorcontrib><creatorcontrib>Pulini, Alfredo-Aram</creatorcontrib><creatorcontrib>Gaume, Antoine</creatorcontrib><creatorcontrib>Dreyfus, Gérard</creatorcontrib><creatorcontrib>Vialatte, François-Benoît</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Linguistics and Language Behavior Abstracts (LLBA)</collection><collection>ProQuest 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)</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>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</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>Biological Sciences</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>MEDLINE - Academic</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>Mora-Sánchez, Aldo</au><au>Pulini, Alfredo-Aram</au><au>Gaume, Antoine</au><au>Dreyfus, Gérard</au><au>Vialatte, François-Benoît</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A brain–computer interface for the continuous, real-time monitoring of working memory load in real-world environments</atitle><jtitle>Cognitive neurodynamics</jtitle><stitle>Cogn Neurodyn</stitle><addtitle>Cogn Neurodyn</addtitle><date>2020-06-01</date><risdate>2020</risdate><volume>14</volume><issue>3</issue><spage>301</spage><epage>321</epage><pages>301-321</pages><issn>1871-4080</issn><eissn>1871-4099</eissn><abstract>We developed a brain–computer interface (BCI) able to continuously monitor working memory (WM) load in real-time (considering the last 2.5 s of brain activity). The BCI is based on biomarkers derived from spectral properties of non-invasive electroencephalography (EEG), subsequently classified by a linear discriminant analysis classifier. The BCI was trained on a visual WM task, tested in a real-time visual WM task, and further validated in a real-time cross task (mental arithmetic). Throughout each trial of the cross task, subjects were given real or sham feedback about their WM load. At the end of the trial, subjects were asked whether the feedback provided was real or sham. The high rate of correct answers provided by the subjects validated not only the global behaviour of the WM-load feedback, but also its real-time dynamics. On average, subjects were able to provide a correct answer 82% of the time, with one subject having 100% accuracy. Possible cognitive and motor confounding factors were disentangled to support the claim that our EEG-based markers correspond indeed to WM.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><pmid>32399073</pmid><doi>10.1007/s11571-020-09573-x</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-3456-714X</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Cognitive neurodynamics, 2020-06, Vol.14 (3), p.301-321 |
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| subjects | Algorithms Artificial Intelligence Biochemistry Biomarkers Biomedical and Life Sciences Biomedicine Brain Cognition & reasoning Cognitive ability Cognitive load Cognitive Psychology Communication Computer applications Computer Science Confounding (Statistics) Discriminant analysis EEG Electroencephalography Feedback Human-computer interface Implants Mathematics Memory Neurosciences Phonology Real time Research Article Short term memory Speech Time Visual tasks |
| title | A brain–computer interface for the continuous, real-time monitoring of working memory load in real-world environments |
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