Loading…
Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback
Optimizing neurofeedback (NF) and brain-computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user's control ability from neurophysiological or psychological measu...
Saved in:
Published in: | Sensors (Basel, Switzerland) Switzerland), 2020-03, Vol.20 (6), p.1620 |
---|---|
Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c469t-705d94113140b1e3d4ed8c1645f0804489687d52c71ba85c8772113bdc5acf923 |
---|---|
cites | cdi_FETCH-LOGICAL-c469t-705d94113140b1e3d4ed8c1645f0804489687d52c71ba85c8772113bdc5acf923 |
container_end_page | |
container_issue | 6 |
container_start_page | 1620 |
container_title | Sensors (Basel, Switzerland) |
container_volume | 20 |
creator | Daeglau, Mareike Wallhoff, Frank Debener, Stefan Condro, Ignatius Sapto Kranczioch, Cornelia Zich, Catharina |
description | Optimizing neurofeedback (NF) and brain-computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user's control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8-30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants' MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factors. |
doi_str_mv | 10.3390/s20061620 |
format | article |
fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_8127e1408d1b4da29faf60b41e076720</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_8127e1408d1b4da29faf60b41e076720</doaj_id><sourcerecordid>2378613227</sourcerecordid><originalsourceid>FETCH-LOGICAL-c469t-705d94113140b1e3d4ed8c1645f0804489687d52c71ba85c8772113bdc5acf923</originalsourceid><addsrcrecordid>eNpdkktvEzEURi0Eog9Y8AeQJTawCPg1tmcDqqKQRipQIVhbHvtO4jAzTu2Zov77uk2JWjZ-XB-d-8m6CL2h5CPnNfmUGSGSSkaeoWMqmJhpxsjzR-cjdJLzlhDGOdcv0RFnVHOmq2OU5xvbdTCsAZ85B7sR_Be8Gny4Dn6yHb6E1MbU28EBXtowZFyu-Fscy7rq7RrSDb5M1o2hAH_DuMHnU6Fj8PhnbGIp48Viib_DlGIL4Bvr_rxCL1rbZXj9sJ-i318Xv-bns4sfy9X87GLmhKzHmSKVrwWlnArSUOBegNeOSlG1RBMhdC218hVzijZWV04rxQrdeFdZ19aMn6LV3uuj3ZpdCr1NNybaYO4LMa2NTSVhB0ZTpqD00Z42wltWt7aVpBEUiJKKkeL6vHftpqYH72AYk-2eSJ--DGFj1vHaKCokre8E7x8EKV5NkEfTh-yg6-wAccqGcaV1rStZF_Tdf-g2TmkoX3VPScoZU4X6sKdcijknaA9hKDF3Y2EOY1HYt4_TH8h_c8BvAWhvsP8</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2378613227</pqid></control><display><type>article</type><title>Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback</title><source>PubMed Central (Open Access)</source><source>Publicly Available Content Database</source><creator>Daeglau, Mareike ; Wallhoff, Frank ; Debener, Stefan ; Condro, Ignatius Sapto ; Kranczioch, Cornelia ; Zich, Catharina</creator><creatorcontrib>Daeglau, Mareike ; Wallhoff, Frank ; Debener, Stefan ; Condro, Ignatius Sapto ; Kranczioch, Cornelia ; Zich, Catharina</creatorcontrib><description>Optimizing neurofeedback (NF) and brain-computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user's control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8-30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants' MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factors.</description><identifier>ISSN: 1424-8220</identifier><identifier>EISSN: 1424-8220</identifier><identifier>DOI: 10.3390/s20061620</identifier><identifier>PMID: 32183285</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Adult ; bci ; Biofeedback ; Brain-Computer Interfaces ; Classification ; Collaboration ; Competition ; Electroencephalography ; Electroencephalography - methods ; erd/s ; Experiments ; Feasibility studies ; Female ; Human-computer interface ; Humanoid ; Humans ; Imagery ; Imagery, Psychotherapy - methods ; individual differences ; Male ; mobile eeg ; Motivation ; motor imagery ; neurofeedback ; Neurofeedback - methods ; Paradigms ; Preferences ; robot ; Robotics - trends ; Robots ; Sensorimotor Cortex - physiology ; Signal processing ; Signal Processing, Computer-Assisted ; Young Adult</subject><ispartof>Sensors (Basel, Switzerland), 2020-03, Vol.20 (6), p.1620</ispartof><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 by the authors. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c469t-705d94113140b1e3d4ed8c1645f0804489687d52c71ba85c8772113bdc5acf923</citedby><cites>FETCH-LOGICAL-c469t-705d94113140b1e3d4ed8c1645f0804489687d52c71ba85c8772113bdc5acf923</cites><orcidid>0000-0002-0585-6225 ; 0000-0001-8814-5535</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2378613227/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2378613227?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25751,27922,27923,37010,37011,44588,53789,53791,74896</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32183285$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Daeglau, Mareike</creatorcontrib><creatorcontrib>Wallhoff, Frank</creatorcontrib><creatorcontrib>Debener, Stefan</creatorcontrib><creatorcontrib>Condro, Ignatius Sapto</creatorcontrib><creatorcontrib>Kranczioch, Cornelia</creatorcontrib><creatorcontrib>Zich, Catharina</creatorcontrib><title>Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback</title><title>Sensors (Basel, Switzerland)</title><addtitle>Sensors (Basel)</addtitle><description>Optimizing neurofeedback (NF) and brain-computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user's control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8-30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants' MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factors.</description><subject>Adult</subject><subject>bci</subject><subject>Biofeedback</subject><subject>Brain-Computer Interfaces</subject><subject>Classification</subject><subject>Collaboration</subject><subject>Competition</subject><subject>Electroencephalography</subject><subject>Electroencephalography - methods</subject><subject>erd/s</subject><subject>Experiments</subject><subject>Feasibility studies</subject><subject>Female</subject><subject>Human-computer interface</subject><subject>Humanoid</subject><subject>Humans</subject><subject>Imagery</subject><subject>Imagery, Psychotherapy - methods</subject><subject>individual differences</subject><subject>Male</subject><subject>mobile eeg</subject><subject>Motivation</subject><subject>motor imagery</subject><subject>neurofeedback</subject><subject>Neurofeedback - methods</subject><subject>Paradigms</subject><subject>Preferences</subject><subject>robot</subject><subject>Robotics - trends</subject><subject>Robots</subject><subject>Sensorimotor Cortex - physiology</subject><subject>Signal processing</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Young Adult</subject><issn>1424-8220</issn><issn>1424-8220</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkktvEzEURi0Eog9Y8AeQJTawCPg1tmcDqqKQRipQIVhbHvtO4jAzTu2Zov77uk2JWjZ-XB-d-8m6CL2h5CPnNfmUGSGSSkaeoWMqmJhpxsjzR-cjdJLzlhDGOdcv0RFnVHOmq2OU5xvbdTCsAZ85B7sR_Be8Gny4Dn6yHb6E1MbU28EBXtowZFyu-Fscy7rq7RrSDb5M1o2hAH_DuMHnU6Fj8PhnbGIp48Viib_DlGIL4Bvr_rxCL1rbZXj9sJ-i318Xv-bns4sfy9X87GLmhKzHmSKVrwWlnArSUOBegNeOSlG1RBMhdC218hVzijZWV04rxQrdeFdZ19aMn6LV3uuj3ZpdCr1NNybaYO4LMa2NTSVhB0ZTpqD00Z42wltWt7aVpBEUiJKKkeL6vHftpqYH72AYk-2eSJ--DGFj1vHaKCokre8E7x8EKV5NkEfTh-yg6-wAccqGcaV1rStZF_Tdf-g2TmkoX3VPScoZU4X6sKdcijknaA9hKDF3Y2EOY1HYt4_TH8h_c8BvAWhvsP8</recordid><startdate>20200314</startdate><enddate>20200314</enddate><creator>Daeglau, Mareike</creator><creator>Wallhoff, Frank</creator><creator>Debener, Stefan</creator><creator>Condro, Ignatius Sapto</creator><creator>Kranczioch, Cornelia</creator><creator>Zich, Catharina</creator><general>MDPI AG</general><general>MDPI</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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-0585-6225</orcidid><orcidid>https://orcid.org/0000-0001-8814-5535</orcidid></search><sort><creationdate>20200314</creationdate><title>Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback</title><author>Daeglau, Mareike ; Wallhoff, Frank ; Debener, Stefan ; Condro, Ignatius Sapto ; Kranczioch, Cornelia ; Zich, Catharina</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c469t-705d94113140b1e3d4ed8c1645f0804489687d52c71ba85c8772113bdc5acf923</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adult</topic><topic>bci</topic><topic>Biofeedback</topic><topic>Brain-Computer Interfaces</topic><topic>Classification</topic><topic>Collaboration</topic><topic>Competition</topic><topic>Electroencephalography</topic><topic>Electroencephalography - methods</topic><topic>erd/s</topic><topic>Experiments</topic><topic>Feasibility studies</topic><topic>Female</topic><topic>Human-computer interface</topic><topic>Humanoid</topic><topic>Humans</topic><topic>Imagery</topic><topic>Imagery, Psychotherapy - methods</topic><topic>individual differences</topic><topic>Male</topic><topic>mobile eeg</topic><topic>Motivation</topic><topic>motor imagery</topic><topic>neurofeedback</topic><topic>Neurofeedback - methods</topic><topic>Paradigms</topic><topic>Preferences</topic><topic>robot</topic><topic>Robotics - trends</topic><topic>Robots</topic><topic>Sensorimotor Cortex - physiology</topic><topic>Signal processing</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Daeglau, Mareike</creatorcontrib><creatorcontrib>Wallhoff, Frank</creatorcontrib><creatorcontrib>Debener, Stefan</creatorcontrib><creatorcontrib>Condro, Ignatius Sapto</creatorcontrib><creatorcontrib>Kranczioch, Cornelia</creatorcontrib><creatorcontrib>Zich, Catharina</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection (ProQuest Medical & Health Databases)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</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</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</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 Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</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>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Sensors (Basel, Switzerland)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Daeglau, Mareike</au><au>Wallhoff, Frank</au><au>Debener, Stefan</au><au>Condro, Ignatius Sapto</au><au>Kranczioch, Cornelia</au><au>Zich, Catharina</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback</atitle><jtitle>Sensors (Basel, Switzerland)</jtitle><addtitle>Sensors (Basel)</addtitle><date>2020-03-14</date><risdate>2020</risdate><volume>20</volume><issue>6</issue><spage>1620</spage><pages>1620-</pages><issn>1424-8220</issn><eissn>1424-8220</eissn><abstract>Optimizing neurofeedback (NF) and brain-computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user's control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8-30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants' MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factors.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>32183285</pmid><doi>10.3390/s20061620</doi><orcidid>https://orcid.org/0000-0002-0585-6225</orcidid><orcidid>https://orcid.org/0000-0001-8814-5535</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1424-8220 |
ispartof | Sensors (Basel, Switzerland), 2020-03, Vol.20 (6), p.1620 |
issn | 1424-8220 1424-8220 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_8127e1408d1b4da29faf60b41e076720 |
source | PubMed Central (Open Access); Publicly Available Content Database |
subjects | Adult bci Biofeedback Brain-Computer Interfaces Classification Collaboration Competition Electroencephalography Electroencephalography - methods erd/s Experiments Feasibility studies Female Human-computer interface Humanoid Humans Imagery Imagery, Psychotherapy - methods individual differences Male mobile eeg Motivation motor imagery neurofeedback Neurofeedback - methods Paradigms Preferences robot Robotics - trends Robots Sensorimotor Cortex - physiology Signal processing Signal Processing, Computer-Assisted Young Adult |
title | Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T11%3A56%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Challenge%20Accepted?%20Individual%20Performance%20Gains%20for%20Motor%20Imagery%20Practice%20with%20Humanoid%20Robotic%20EEG%20Neurofeedback&rft.jtitle=Sensors%20(Basel,%20Switzerland)&rft.au=Daeglau,%20Mareike&rft.date=2020-03-14&rft.volume=20&rft.issue=6&rft.spage=1620&rft.pages=1620-&rft.issn=1424-8220&rft.eissn=1424-8220&rft_id=info:doi/10.3390/s20061620&rft_dat=%3Cproquest_doaj_%3E2378613227%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c469t-705d94113140b1e3d4ed8c1645f0804489687d52c71ba85c8772113bdc5acf923%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2378613227&rft_id=info:pmid/32183285&rfr_iscdi=true |