A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity
Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its st...
Gespeichert in:
| Veröffentlicht in: | PloS one 2019-01, Vol.14 (1), p.e0207351-e0207351 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e0207351 |
|---|---|
| container_issue | 1 |
| container_start_page | e0207351 |
| container_title | PloS one |
| container_volume | 14 |
| creator | Sannelli, Claudia Vidaurre, Carmen Müller, Klaus-Robert Blankertz, Benjamin |
| description | Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies. |
| doi_str_mv | 10.1371/journal.pone.0207351 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2171190804</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A571057336</galeid><doaj_id>oai_doaj_org_article_add28036c879477ca1fcc3d1421fd4a6</doaj_id><sourcerecordid>A571057336</sourcerecordid><originalsourceid>FETCH-LOGICAL-c692t-4bfa409ba966b12cce4cc1000bb69100701b0e702493c7dc1d2bfe378b2881fb3</originalsourceid><addsrcrecordid>eNqNk21v0zAQxyMEYmPwDRBYQkLwosVPjRNeIJWKh0pDkzbgreXYl9RVam-2MyifnmTNphbtBbJkW-ff_e989mXZc4KnhAnybu274FQ7vfQOpphiwWbkQXZMSkYnOcXs4d7-KHsS4xrjGSvy_HF2xHBeUExnx1k3R60KDaCoVTvMAcBZ16CYOrNFv2xaIYUuvp1PKhXBoI-L5Xu0UAkaH-wflax3yNeDGXURQkTKGWRsXUMApyEi61BagQ2DBlI62Wubtk-zR7VqIzwb15Psx-dP3xdfJ6dnX5aL-elE5yVNE17ViuOyUmWeV4RqDVxrgjGuqrzsV4FJhUFgykumhdHE0KoGJoqKFgWpK3aSvdzpXrY-yrFiUVIiCClxgXlPLHeE8WotL4PdqLCVXll5Y_ChkSokq1uQyhhaYJbrQpRcCK1IrTUzhFNSG67yXuvDGK2rNmA0uBRUeyB6eOLsSjb-WuaMC85oL_BmFAj-qoOY5MZGDW2rHPjuJu-Sk5zyIe9X_6D3326kmv5xpXW17-PqQVTOZ4LgmWBsyHt6D9UPAxur--9V295-4PD2wKFnEvxOjepilMuL8_9nz34esq_32BWoNq2ib7vhl8VDkO9AHXyMAeq7IhMsh-64rYYcukOO3dG7vdh_oDun23ZgfwGgcQiE</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2171190804</pqid></control><display><type>article</type><title>A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Sannelli, Claudia ; Vidaurre, Carmen ; Müller, Klaus-Robert ; Blankertz, Benjamin</creator><contributor>Ayaz, Hasan</contributor><creatorcontrib>Sannelli, Claudia ; Vidaurre, Carmen ; Müller, Klaus-Robert ; Blankertz, Benjamin ; Ayaz, Hasan</creatorcontrib><description>Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0207351</identifier><identifier>PMID: 30682025</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adaptation ; Adolescent ; Adult ; Aged ; Algorithms ; Artificial intelligence ; Biofeedback, Psychology ; Biology and Life Sciences ; Brain research ; Brain-Computer Interfaces ; Calibration ; Classification ; Design of experiments ; EEG ; Electroencephalography ; Engineering ; Engineering and Technology ; Experimental design ; Experiments ; Feedback ; Female ; Human-computer interface ; Humans ; Illiteracy ; Imagery ; Interfaces ; Learning algorithms ; Machine learning ; Male ; Medical research ; Medicine and Health Sciences ; Mental task performance ; Middle Aged ; Motors ; Neurophysiology ; Neurosciences ; Physical Sciences ; Physiological aspects ; Research and Analysis Methods ; Rhythm ; Screening ; Sensorimotor Cortex - physiology ; Sensorimotor integration ; Sensorimotor system ; Studies ; User interfaces (Computers) ; User training ; Young Adult</subject><ispartof>PloS one, 2019-01, Vol.14 (1), p.e0207351-e0207351</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Sannelli 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 Sannelli et al 2019 Sannelli et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-4bfa409ba966b12cce4cc1000bb69100701b0e702493c7dc1d2bfe378b2881fb3</citedby><cites>FETCH-LOGICAL-c692t-4bfa409ba966b12cce4cc1000bb69100701b0e702493c7dc1d2bfe378b2881fb3</cites><orcidid>0000-0003-3740-049X</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/PMC6347432/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347432/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,2103,2929,23871,27929,27930,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30682025$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Ayaz, Hasan</contributor><creatorcontrib>Sannelli, Claudia</creatorcontrib><creatorcontrib>Vidaurre, Carmen</creatorcontrib><creatorcontrib>Müller, Klaus-Robert</creatorcontrib><creatorcontrib>Blankertz, Benjamin</creatorcontrib><title>A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.</description><subject>Adaptation</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Biofeedback, Psychology</subject><subject>Biology and Life Sciences</subject><subject>Brain research</subject><subject>Brain-Computer Interfaces</subject><subject>Calibration</subject><subject>Classification</subject><subject>Design of experiments</subject><subject>EEG</subject><subject>Electroencephalography</subject><subject>Engineering</subject><subject>Engineering and Technology</subject><subject>Experimental design</subject><subject>Experiments</subject><subject>Feedback</subject><subject>Female</subject><subject>Human-computer interface</subject><subject>Humans</subject><subject>Illiteracy</subject><subject>Imagery</subject><subject>Interfaces</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Male</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Mental task performance</subject><subject>Middle Aged</subject><subject>Motors</subject><subject>Neurophysiology</subject><subject>Neurosciences</subject><subject>Physical Sciences</subject><subject>Physiological aspects</subject><subject>Research and Analysis Methods</subject><subject>Rhythm</subject><subject>Screening</subject><subject>Sensorimotor Cortex - physiology</subject><subject>Sensorimotor integration</subject><subject>Sensorimotor system</subject><subject>Studies</subject><subject>User interfaces (Computers)</subject><subject>User training</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>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk21v0zAQxyMEYmPwDRBYQkLwosVPjRNeIJWKh0pDkzbgreXYl9RVam-2MyifnmTNphbtBbJkW-ff_e989mXZc4KnhAnybu274FQ7vfQOpphiwWbkQXZMSkYnOcXs4d7-KHsS4xrjGSvy_HF2xHBeUExnx1k3R60KDaCoVTvMAcBZ16CYOrNFv2xaIYUuvp1PKhXBoI-L5Xu0UAkaH-wflax3yNeDGXURQkTKGWRsXUMApyEi61BagQ2DBlI62Wubtk-zR7VqIzwb15Psx-dP3xdfJ6dnX5aL-elE5yVNE17ViuOyUmWeV4RqDVxrgjGuqrzsV4FJhUFgykumhdHE0KoGJoqKFgWpK3aSvdzpXrY-yrFiUVIiCClxgXlPLHeE8WotL4PdqLCVXll5Y_ChkSokq1uQyhhaYJbrQpRcCK1IrTUzhFNSG67yXuvDGK2rNmA0uBRUeyB6eOLsSjb-WuaMC85oL_BmFAj-qoOY5MZGDW2rHPjuJu-Sk5zyIe9X_6D3326kmv5xpXW17-PqQVTOZ4LgmWBsyHt6D9UPAxur--9V295-4PD2wKFnEvxOjepilMuL8_9nz34esq_32BWoNq2ib7vhl8VDkO9AHXyMAeq7IhMsh-64rYYcukOO3dG7vdh_oDun23ZgfwGgcQiE</recordid><startdate>20190125</startdate><enddate>20190125</enddate><creator>Sannelli, Claudia</creator><creator>Vidaurre, Carmen</creator><creator>Müller, Klaus-Robert</creator><creator>Blankertz, Benjamin</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>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/0000-0003-3740-049X</orcidid></search><sort><creationdate>20190125</creationdate><title>A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity</title><author>Sannelli, Claudia ; Vidaurre, Carmen ; Müller, Klaus-Robert ; Blankertz, Benjamin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-4bfa409ba966b12cce4cc1000bb69100701b0e702493c7dc1d2bfe378b2881fb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adaptation</topic><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Biofeedback, Psychology</topic><topic>Biology and Life Sciences</topic><topic>Brain research</topic><topic>Brain-Computer Interfaces</topic><topic>Calibration</topic><topic>Classification</topic><topic>Design of experiments</topic><topic>EEG</topic><topic>Electroencephalography</topic><topic>Engineering</topic><topic>Engineering and Technology</topic><topic>Experimental design</topic><topic>Experiments</topic><topic>Feedback</topic><topic>Female</topic><topic>Human-computer interface</topic><topic>Humans</topic><topic>Illiteracy</topic><topic>Imagery</topic><topic>Interfaces</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Male</topic><topic>Medical research</topic><topic>Medicine and Health Sciences</topic><topic>Mental task performance</topic><topic>Middle Aged</topic><topic>Motors</topic><topic>Neurophysiology</topic><topic>Neurosciences</topic><topic>Physical Sciences</topic><topic>Physiological aspects</topic><topic>Research and Analysis Methods</topic><topic>Rhythm</topic><topic>Screening</topic><topic>Sensorimotor Cortex - physiology</topic><topic>Sensorimotor integration</topic><topic>Sensorimotor system</topic><topic>Studies</topic><topic>User interfaces (Computers)</topic><topic>User training</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sannelli, Claudia</creatorcontrib><creatorcontrib>Vidaurre, Carmen</creatorcontrib><creatorcontrib>Müller, Klaus-Robert</creatorcontrib><creatorcontrib>Blankertz, Benjamin</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 & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</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 Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</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>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 Korea</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>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural 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>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</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>Access via ProQuest (Open Access)</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>Sannelli, Claudia</au><au>Vidaurre, Carmen</au><au>Müller, Klaus-Robert</au><au>Blankertz, Benjamin</au><au>Ayaz, Hasan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-01-25</date><risdate>2019</risdate><volume>14</volume><issue>1</issue><spage>e0207351</spage><epage>e0207351</epage><pages>e0207351-e0207351</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30682025</pmid><doi>10.1371/journal.pone.0207351</doi><tpages>e0207351</tpages><orcidid>https://orcid.org/0000-0003-3740-049X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2019-01, Vol.14 (1), p.e0207351-e0207351 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2171190804 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS) Journals Open Access; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adaptation Adolescent Adult Aged Algorithms Artificial intelligence Biofeedback, Psychology Biology and Life Sciences Brain research Brain-Computer Interfaces Calibration Classification Design of experiments EEG Electroencephalography Engineering Engineering and Technology Experimental design Experiments Feedback Female Human-computer interface Humans Illiteracy Imagery Interfaces Learning algorithms Machine learning Male Medical research Medicine and Health Sciences Mental task performance Middle Aged Motors Neurophysiology Neurosciences Physical Sciences Physiological aspects Research and Analysis Methods Rhythm Screening Sensorimotor Cortex - physiology Sensorimotor integration Sensorimotor system Studies User interfaces (Computers) User training Young Adult |
| title | A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T01%3A58%3A17IST&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=A%20large%20scale%20screening%20study%20with%20a%20SMR-based%20BCI:%20Categorization%20of%20BCI%20users%20and%20differences%20in%20their%20SMR%20activity&rft.jtitle=PloS%20one&rft.au=Sannelli,%20Claudia&rft.date=2019-01-25&rft.volume=14&rft.issue=1&rft.spage=e0207351&rft.epage=e0207351&rft.pages=e0207351-e0207351&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0207351&rft_dat=%3Cgale_plos_%3EA571057336%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=2171190804&rft_id=info:pmid/30682025&rft_galeid=A571057336&rft_doaj_id=oai_doaj_org_article_add28036c879477ca1fcc3d1421fd4a6&rfr_iscdi=true |