A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control
Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not ne...
Saved in:
| Published in | Sensors (Basel, Switzerland) Vol. 22; no. 15; p. 5802 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
03.08.2022
MDPI |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work. |
|---|---|
| AbstractList | Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work.Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work. Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work. |
| Author | Camilleri, Kenneth Fabri, Simon Padfield, Natasha Bugeja, Marvin Camilleri, Tracey |
| AuthorAffiliation | 1 Centre for Biomedical Cybernetics, University of Malta, MSD 2080 Msida, Malta; kenneth.camilleri@um.edu.mt 2 Department of Systems and Control Engineering, University of Malta, MSD 2080 Msida, Malta; tracey.camilleri@um.edu.mt (T.C.); simon.fabri@um.edu.mt (S.F.); marvin.bugeja@um.edu.mt (M.B.) |
| AuthorAffiliation_xml | – name: 1 Centre for Biomedical Cybernetics, University of Malta, MSD 2080 Msida, Malta; kenneth.camilleri@um.edu.mt – name: 2 Department of Systems and Control Engineering, University of Malta, MSD 2080 Msida, Malta; tracey.camilleri@um.edu.mt (T.C.); simon.fabri@um.edu.mt (S.F.); marvin.bugeja@um.edu.mt (M.B.) |
| Author_xml | – sequence: 1 givenname: Natasha orcidid: 0000-0001-5533-4807 surname: Padfield fullname: Padfield, Natasha – sequence: 2 givenname: Kenneth orcidid: 0000-0003-0436-6408 surname: Camilleri fullname: Camilleri, Kenneth – sequence: 3 givenname: Tracey surname: Camilleri fullname: Camilleri, Tracey – sequence: 4 givenname: Simon surname: Fabri fullname: Fabri, Simon – sequence: 5 givenname: Marvin orcidid: 0000-0001-6632-2369 surname: Bugeja fullname: Bugeja, Marvin |
| BookMark | eNplkU1v3CAQhlGVqvloD_0HlnppD04wGIMvlZLNJl0pSqQqdzSG8YaVDVvwbpV_X7abRE16AsEzD8M7x-TAB4-EfK7oKectPUuMVUIoyt6Ro6pmdakYowf_7A_JcUorShnnXH0gh1y0QvKGHpHb82IWxnXEB_TJbbH4iVuHv4vQF3NvwxJ92KRiPr8uLyChLS5mi1T0IRaXjx5GZ4rLzBvMEj_FMHwk73sYEn56Wk_I_dX8fvajvLm7XszOb0pT181UNnWHpueCUQFGNX3fWMktYM1rYJ1pFNiuMkJJQ7lqWUexQ2prhb2Axgp-QhZ7rQ2w0uvoRoiPOoDTfw9CXGqIkzMDaqisASEZY5ifxg6QN8gUpdkrWMWz6_vetd50I1qD-SMwvJK-vvHuQS_DVrdcUtXsmvn6JIjh1wbTpEeXDA4DeMzhaSYpq1QtVZvRL2_QVdhEn5PaUVTKVrYqU2d7ysSQUsReGzfB5HYRgxt0RfVu7Ppl7Lni25uK5_b_Z_8Av02rtg |
| CitedBy_id | crossref_primary_10_3390_s23042069 crossref_primary_10_1080_17483107_2023_2211602 crossref_primary_10_1016_j_bspc_2023_105324 crossref_primary_10_1007_s11042_023_18096_6 crossref_primary_10_3390_s25071992 crossref_primary_10_1016_j_neurol_2022_12_008 crossref_primary_10_3390_s23135836 crossref_primary_10_1002_jey2_12011 crossref_primary_10_3390_technologies12120261 crossref_primary_10_1007_s42600_024_00372_5 crossref_primary_10_1039_D4TC00574K crossref_primary_10_1007_s11517_024_03147_3 crossref_primary_10_1007_s11517_025_03295_0 crossref_primary_10_3389_fbioe_2024_1454262 crossref_primary_10_1080_2326263X_2024_2379009 crossref_primary_10_1088_1361_6579_acd51b crossref_primary_10_1016_j_rcim_2023_102610 crossref_primary_10_1109_TIM_2025_3546388 crossref_primary_10_1080_10255842_2023_2275244 crossref_primary_10_3390_neurosci5020012 crossref_primary_10_1002_qute_202300185 crossref_primary_10_1016_j_medntd_2025_100353 crossref_primary_10_1080_10447318_2023_2203006 crossref_primary_10_3390_app142311173 crossref_primary_10_3390_s23094477 |
| Cites_doi | 10.1109/ACCESS.2021.3100700 10.1109/TBME.2004.827086 10.1109/TSMCC.2012.2226444 10.3389/fnhum.2019.00461 10.1002/tee.22742 10.3389/fnsys.2015.00064 10.3390/electronics9010174 10.1088/1741-2552/aace8c 10.26599/TST.2018.9010111 10.1109/TNSRE.2003.814454 10.1109/MSMC.2019.2958200 10.3389/fnhum.2018.00246 10.1109/ACCESS.2018.2886708 10.1007/s12369-018-0501-8 10.1109/TNSRE.2003.814435 10.1109/ACCESS.2018.2886271 10.3389/fbioe.2020.00735 10.1088/1741-2552/ab377d 10.1109/TNSRE.2020.2974056 10.1109/TMRB.2019.2949865 10.1016/j.jneumeth.2013.11.015 10.1016/S1388-2457(99)00141-8 10.1088/1741-2552/abe20f 10.1109/ACCESS.2020.2984538 10.1515/bams-2021-0095 10.1016/j.neucom.2014.09.078 10.1016/j.eswa.2017.07.033 10.3390/s19061423 10.1063/1.5006461 10.1109/TRO.2012.2201310 10.3389/fnbot.2017.00006 10.1016/j.bspc.2022.103634 10.1109/ACCESS.2021.3071599 10.1016/j.robot.2019.02.014 10.1016/j.neunet.2014.01.006 10.1109/TASE.2019.2911667 10.1186/1743-0003-10-111 10.1109/TNSRE.2015.2439298 10.1109/TSMC.2014.2313317 10.1615/CritRevBiomedEng.v30.i456.80 10.3390/s21062020 10.1016/j.neucom.2018.04.087 10.1016/j.cmpb.2018.06.012 10.1088/1741-2552/aae069 10.1109/ACCESS.2018.2853628 10.1088/1741-2552/ab0328 10.3389/fnins.2012.00039 10.1016/j.eswa.2020.113285 10.1515/bams-2019-0053 10.3389/fnins.2012.00055 10.1109/TNSRE.2016.2646763 10.1109/JAS.2017.7510616 10.1038/srep38565 10.1126/scirobotics.aaw6844 10.1109/ACCESS.2021.3062329 10.1016/j.compbiomed.2014.04.020 10.3390/s20247309 10.3390/s16122050 10.1109/TRO.2019.2943072 10.1111/j.1464-0597.2004.00161.x 10.1186/s12938-019-0633-6 10.1142/S1793536909000199 10.1109/TFUZZ.2016.2637403 10.1007/s10846-017-0477-x 10.1007/s42600-021-00196-7 10.1109/TSMC.2015.2506618 10.1109/TSMC.2019.2955478 10.1109/TBME.2004.827088 10.1109/JPROC.2015.2419736 10.1109/JIOT.2019.2963288 10.1088/1741-2560/10/4/046003 10.1109/TNSRE.2020.2981659 10.1126/scirobotics.aag3296 10.1002/hbm.23730 10.1109/TNSRE.2019.2915801 10.1016/j.eswa.2015.01.031 10.3389/fnins.2018.00943 10.1109/TASE.2019.2956110 10.1109/5.939829 10.1109/TNSRE.2018.2881215 10.1109/TAMD.2014.2387271 10.1109/TCDS.2018.2875052 10.1109/EMBC.2018.8512193 10.1088/1741-2552/aaf12e 10.1109/ACCESS.2020.2991812 10.1142/S0129065717500332 10.1109/I2C2.2017.8321831 |
| ContentType | Journal Article |
| Copyright | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU COVID DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/s22155802 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College Coronavirus Research Database ProQuest Central Health Research Premium Collection (ProQuest) Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Proquest Medical Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_a1dca57222e446ebae36e28008925213 PMC9370865 10_3390_s22155802 |
| GrantInformation_xml | – fundername: University of Malta grantid: ERDF.01.124 – fundername: European Union through the European Regional Development Fund |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFKRA AFZYC ALIPV ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. 7XB 8FK AZQEC COVID DWQXO K9. PJZUB PKEHL PPXIY PQEST PQUKI PRINS 7X8 5PM PUEGO |
| ID | FETCH-LOGICAL-c446t-64becf35205ac86ff6d73dae434a2bc68adb1c587c03892b0ebe0d48ef5a6d53 |
| IEDL.DBID | DOA |
| ISSN | 1424-8220 |
| IngestDate | Wed Aug 27 01:30:05 EDT 2025 Thu Aug 21 13:57:50 EDT 2025 Mon Jul 21 11:36:16 EDT 2025 Fri Jul 25 20:08:10 EDT 2025 Tue Jul 01 02:42:05 EDT 2025 Thu Apr 24 22:57:25 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 15 |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c446t-64becf35205ac86ff6d73dae434a2bc68adb1c587c03892b0ebe0d48ef5a6d53 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 |
| ORCID | 0000-0001-6632-2369 0000-0001-5533-4807 0000-0003-0436-6408 |
| OpenAccessLink | https://doaj.org/article/a1dca57222e446ebae36e28008925213 |
| PMID | 35957360 |
| PQID | 2700779798 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_a1dca57222e446ebae36e28008925213 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9370865 proquest_miscellaneous_2702184789 proquest_journals_2700779798 crossref_citationtrail_10_3390_s22155802 crossref_primary_10_3390_s22155802 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20220803 |
| PublicationDateYYYYMMDD | 2022-08-03 |
| PublicationDate_xml | – month: 8 year: 2022 text: 20220803 day: 3 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Chen (ref_29) 2020; 32 Liu (ref_63) 2019; 11 ref_97 LaFleur (ref_27) 2013; 10 ref_95 Sabudin (ref_100) 2016; 11 Khan (ref_28) 2017; 11 Xu (ref_85) 2013; 16 Renkens (ref_17) 2004; 51 Tonin (ref_13) 2019; 36 Ortiz (ref_61) 2020; 8 Ang (ref_79) 2012; 6 Yu (ref_1) 2018; 26 Edelman (ref_42) 2019; 4 (ref_16) 2003; 11 Wang (ref_44) 2018; 6 Zhang (ref_71) 2016; 24 ref_23 ref_22 Hortal (ref_74) 2015; 151 Do (ref_75) 2013; 10 Pfurtscheller (ref_19) 2003; 11 Li (ref_66) 2019; 16 Junwei (ref_25) 2019; 7 Cardoso (ref_56) 2020; 28 Wang (ref_62) 2019; 16 Ji (ref_58) 2021; 116 ref_78 Gordleeva (ref_43) 2020; 8 Pfurtscheller (ref_83) 1999; 110 Ai (ref_54) 2019; 16 Raza (ref_88) 2019; 343 Zuo (ref_90) 2021; 18 Pfurtscheller (ref_15) 2001; 89 Zheng (ref_89) 2022; 76 Chae (ref_73) 2012; 28 Menon (ref_65) 2020; 7 Wankhade (ref_9) 2020; 16 Scherer (ref_18) 2010; 4 Banach (ref_24) 2021; 17 ref_82 ref_80 Li (ref_68) 2014; 224 Leeb (ref_5) 2015; 103 ref_86 Kucukyildiz (ref_33) 2017; 87 Jafarifarmand (ref_55) 2019; 27 Zecca (ref_32) 2002; 30 Ang (ref_46) 2017; 25 Cardoso (ref_36) 2022; 38 Liu (ref_45) 2017; 88 Schirrmeister (ref_94) 2017; 38 Moldoveanu (ref_51) 2019; 7 Espinal (ref_12) 2021; 9 Spychala (ref_37) 2020; 13 Kobayashi (ref_57) 2018; 13 Abiri (ref_10) 2019; 16 Shi (ref_30) 2015; 42 Kim (ref_26) 2014; 51 Perdikis (ref_4) 2020; 6 Wang (ref_8) 2021; 9 Meng (ref_38) 2016; 6 Page (ref_6) 2021; 10 Tangermann (ref_21) 2012; 6 Jeong (ref_41) 2020; 28 He (ref_84) 2016; 46 Frisoli (ref_70) 2012; 42 Gao (ref_40) 2021; 18 Rubio (ref_98) 2004; 53 Gandhi (ref_67) 2014; 44 Yokota (ref_53) 2019; 10 Alhakeem (ref_60) 2020; 8 Bhattacharyya (ref_72) 2017; 4 Zhuang (ref_52) 2021; 51 Lawhern (ref_93) 2018; 15 Ehrlich (ref_77) 2019; 11 Schicktanz (ref_3) 2015; 9 Hekmatmanesh (ref_7) 2021; 9 ref_35 Dornhege (ref_20) 2004; 51 ref_31 Sereshkeh (ref_99) 2017; 27 Baig (ref_91) 2017; 90 ref_39 Tang (ref_2) 2020; 149 Zaidan (ref_11) 2018; 164 Ehrlich (ref_92) 2018; 15 Xu (ref_50) 2019; 115 Bozinovski (ref_81) 2015; 7 Lo (ref_87) 2009; 1 Kasabov (ref_96) 2014; 52 Zhang (ref_49) 2019; 24 Hong (ref_76) 2018; 12 Kim (ref_69) 2019; 18 Cao (ref_14) 2018; 26 (ref_47) 2018; 2018 ref_48 Li (ref_59) 2018; 12 Soekadar (ref_34) 2016; 1 Li (ref_64) 2019; 1 |
| References_xml | – volume: 9 start-page: 110173 year: 2021 ident: ref_7 article-title: Review of the State-of-the-Art of Brain-Controlled Vehicles publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3100700 – volume: 51 start-page: 1026 year: 2004 ident: ref_17 article-title: Noninvasive Brain-Actuated Control of a Mobile Robot by Human EEG publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2004.827086 – volume: 42 start-page: 1169 year: 2012 ident: ref_70 article-title: A New Gaze-BCI-Driven Control of an Upper Limb Exoskeleton for Rehabilitation in Real-World Tasks publication-title: IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. doi: 10.1109/TSMCC.2012.2226444 – volume: 13 start-page: 461 year: 2020 ident: ref_37 article-title: Exploring Self-Paced Embodiable Neurofeedback for Post-Stroke Motor Rehabilitation publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2019.00461 – volume: 13 start-page: 1795 year: 2018 ident: ref_57 article-title: BCI-Based Control of Electric Wheelchair Using Fractal Characteristics of EEG publication-title: IEEJ Trans. Electr. Electron. Eng. doi: 10.1002/tee.22742 – ident: ref_39 – volume: 9 start-page: 64 year: 2015 ident: ref_3 article-title: Qualitative Assessment of Patients’ Attitudes and Expectations toward BCIs and Implications for Future Technology Development publication-title: Front. Syst. Neurosci. doi: 10.3389/fnsys.2015.00064 – ident: ref_48 doi: 10.3390/electronics9010174 – volume: 15 start-page: 056013 year: 2018 ident: ref_93 article-title: EEGNet: A Compact Convolutional Neural Network for EEG-Based Brain-Computer Interfaces publication-title: J. Neural Eng. doi: 10.1088/1741-2552/aace8c – volume: 24 start-page: 360 year: 2019 ident: ref_49 article-title: Asynchronous Brain-Computer Interface Shared Control of Robotic Grasping publication-title: Tsinghua Sci. Technol. doi: 10.26599/TST.2018.9010111 – volume: 11 start-page: 177 year: 2003 ident: ref_19 article-title: Graz-BCI: State of the Art and Clinical Applications publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2003.814454 – volume: 6 start-page: 12 year: 2020 ident: ref_4 article-title: Brain-Machine Interfaces: A Tale of Two Learners publication-title: IEEE Syst. Man Cybern. Mag. doi: 10.1109/MSMC.2019.2958200 – volume: 12 start-page: 246 year: 2018 ident: ref_76 article-title: Feature Extraction and Classification Methods for Hybrid FNIRS-EEG Brain-Computer Interfaces publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2018.00246 – volume: 7 start-page: 2439 year: 2019 ident: ref_25 article-title: Brain Computer Interface for Neurodegenerative Person Using Electroencephalogram publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2886708 – volume: 11 start-page: 271 year: 2019 ident: ref_77 article-title: A Feasibility Study for Validating Robot Actions Using EEG-Based Error-Related Potentials publication-title: Int. J. Soc. Robot. doi: 10.1007/s12369-018-0501-8 – volume: 11 start-page: 10801 year: 2016 ident: ref_100 article-title: Potential Field Methods and Their Inherent Approaches for Path Planning publication-title: ARPN J. Eng. Appl. Sci. – volume: 11 start-page: 159 year: 2003 ident: ref_16 article-title: Asynchronous BCI and Local Neural Classifiers: An Overview of the Adaptive Brain Interface Project publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2003.814435 – volume: 7 start-page: 8151 year: 2019 ident: ref_51 article-title: The TRAVEE System for a Multimodal Neuromotor Rehabilitation publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2886271 – ident: ref_97 – volume: 8 start-page: 735 year: 2020 ident: ref_61 article-title: Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton publication-title: Front. Bioeng. Biotechnol. doi: 10.3389/fbioe.2020.00735 – volume: 10 start-page: 89 year: 2021 ident: ref_6 article-title: The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews publication-title: BMJ – volume: 16 start-page: 066012 year: 2019 ident: ref_62 article-title: A BCI Based Visual-Haptic Neurofeedback Training Improves Cortical Activations and Classification Performance during Motor Imagery publication-title: J. Neural Eng. doi: 10.1088/1741-2552/ab377d – volume: 28 start-page: 988 year: 2020 ident: ref_56 article-title: A Low-Cost Lower-Limb Brain-Machine Interface Triggered by Pedaling Motor Imagery for Post-Stroke Patients Rehabilitation publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2020.2974056 – volume: 1 start-page: 218 year: 2019 ident: ref_64 article-title: Hybrid Brain/Muscle Signals Powered Wearable Walking Exoskeleton Enhancing Motor Ability in Climbing Stairs Activity publication-title: IEEE Trans. Med. Robot. Bionics doi: 10.1109/TMRB.2019.2949865 – volume: 224 start-page: 26 year: 2014 ident: ref_68 article-title: Brain-Machine Interface Control of a Manipulator Using Small-World Neural Network and Shared Control Strategy publication-title: J. Neurosci. Methods doi: 10.1016/j.jneumeth.2013.11.015 – volume: 110 start-page: 1842 year: 1999 ident: ref_83 article-title: Event-Related EEG/MEG Synchronization and Desynchronization: Basic Principles publication-title: Clin. Neurophysiol. doi: 10.1016/S1388-2457(99)00141-8 – volume: 18 start-page: 026018 year: 2021 ident: ref_90 article-title: Cluster Decomposing and Multi-Objective Optimization Based-Ensemble Learning Framework for Motor Imagery-Based Brain-Computer Interfaces publication-title: J. Neural Eng. doi: 10.1088/1741-2552/abe20f – volume: 8 start-page: 64266 year: 2020 ident: ref_60 article-title: Wheelchair Free Hands Navigation Using Robust DWT-AR Features Extraction Method with Muscle Brain Signals publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2984538 – volume: 17 start-page: 165 year: 2021 ident: ref_24 article-title: Brain-Computer Interface for Electric Wheelchair Based on Alpha Waves of EEG Signal publication-title: Bio-Algorithms Med-Syst. doi: 10.1515/bams-2021-0095 – volume: 151 start-page: 116 year: 2015 ident: ref_74 article-title: SVM-Based Brain-Machine Interface for Controlling a Robot Arm through Four Mental Tasks publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.09.078 – volume: 90 start-page: 184 year: 2017 ident: ref_91 article-title: Differential Evolution Algorithm as a Tool for Optimal Feature Subset Selection in Motor Imagery EEG publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.07.033 – ident: ref_78 doi: 10.3390/s19061423 – volume: 88 start-page: 104302 year: 2017 ident: ref_45 article-title: A Brain-Controlled Lower-Limb Exoskeleton for Human Gait Training publication-title: Rev. Sci. Instrum. doi: 10.1063/1.5006461 – volume: 28 start-page: 1131 year: 2012 ident: ref_73 article-title: Toward Brain-Actuated Humanoid Robots: Asynchronous Direct Control Using an EEG-Based BCI publication-title: IEEE Trans. Robot. doi: 10.1109/TRO.2012.2201310 – volume: 11 start-page: 6 year: 2017 ident: ref_28 article-title: Hybrid EEG-FNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control publication-title: Front. Neurorobotics doi: 10.3389/fnbot.2017.00006 – volume: 76 start-page: 103634 year: 2022 ident: ref_89 article-title: Ensemble Learning Method Based on Temporal, Spatial Features with Multi-Scale Filter Banks for Motor Imagery EEG Classification publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2022.103634 – volume: 9 start-page: 55920 year: 2021 ident: ref_8 article-title: Brain-Controlled Wheelchair Review: From Wet Electrode to Dry Electrode, From Single Modal to Hybrid Modal, From Synchronous to Asynchronous publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3071599 – volume: 115 start-page: 121 year: 2019 ident: ref_50 article-title: Shared Control of a Robotic Arm Using Non-Invasive Brain–Computer Interface and Computer Vision Guidance publication-title: Robot. Auton. Syst. doi: 10.1016/j.robot.2019.02.014 – volume: 52 start-page: 62 year: 2014 ident: ref_96 article-title: NeuCube: A Spiking Neural Network Architecture for Mapping, Learning and Understanding of Spatio-Temporal Brain Data publication-title: Neural Netw. doi: 10.1016/j.neunet.2014.01.006 – ident: ref_22 – volume: 16 start-page: 1664 year: 2019 ident: ref_66 article-title: Development of a Human-Robot Hybrid Intelligent System Based on Brain Teleoperation and Deep Learning SLAM publication-title: IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2019.2911667 – ident: ref_95 – volume: 10 start-page: 111 year: 2013 ident: ref_75 article-title: Brain-Computer Interface Controlled Robotic Gait Orthosis publication-title: J. Neuroeng. Rehabil. doi: 10.1186/1743-0003-10-111 – volume: 4 start-page: 34 year: 2010 ident: ref_18 article-title: Temporal Coding of Brain Patterns for Direct Limb Control in Humans publication-title: Front. Neurosci. – volume: 24 start-page: 128 year: 2016 ident: ref_71 article-title: Control of a Wheelchair in an Indoor Environment Based on a Brain-Computer Interface and Automated Navigation publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2015.2439298 – volume: 44 start-page: 1278 year: 2014 ident: ref_67 article-title: EEG-Based Mobile Robot Control through an Adaptive Brain-Robot Interface publication-title: IEEE Trans. Syst. Man Cybern. Syst. doi: 10.1109/TSMC.2014.2313317 – volume: 30 start-page: 459 year: 2002 ident: ref_32 article-title: Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal publication-title: Crit. Rev. Biomed. Eng. doi: 10.1615/CritRevBiomedEng.v30.i456.80 – volume: 16 start-page: 3543 year: 2013 ident: ref_85 article-title: Online Classification of Single Trial Motor Imagery Electroencephalogram Signals publication-title: Int. Inf. Inst. (Tokyo) Inf. – ident: ref_31 doi: 10.3390/s21062020 – volume: 343 start-page: 154 year: 2019 ident: ref_88 article-title: Covariate Shift Estimation Based Adaptive Ensemble Learning for Handling Non-Stationarity in Motor Imagery Related EEG-Based Brain-Computer Interface publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.04.087 – volume: 164 start-page: 221 year: 2018 ident: ref_11 article-title: A Review of Disability EEG Based Wheelchair Control System: Coherent Taxonomy, Open Challenges and Recommendations publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2018.06.012 – volume: 15 start-page: 066014 year: 2018 ident: ref_92 article-title: Human-Agent Co-Adaptation Using Error-Related Potentials publication-title: J. Neural Eng. doi: 10.1088/1741-2552/aae069 – volume: 6 start-page: 38524 year: 2018 ident: ref_44 article-title: Implementation of a Brain-Computer Interface on a Lower-Limb Exoskeleton publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2853628 – volume: 16 start-page: 026032 year: 2019 ident: ref_54 article-title: Feature Extraction of Four-Class Motor Imagery EEG Signals Based on Functional Brain Network publication-title: J. Neural Eng. doi: 10.1088/1741-2552/ab0328 – volume: 6 start-page: 39 year: 2012 ident: ref_79 article-title: Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b publication-title: Front. Neurosci. doi: 10.3389/fnins.2012.00039 – volume: 149 start-page: 113285 year: 2020 ident: ref_2 article-title: Motor Imagery EEG Recognition Based on Conditional Optimization Empirical Mode Decomposition and Multi-Scale Convolutional Neural Network publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.113285 – volume: 16 start-page: 20190053 year: 2020 ident: ref_9 article-title: An Empirical Survey of Electroencephalography-Based Brain-Computer Interfaces publication-title: Bio-Algorithms Med-Syst. doi: 10.1515/bams-2019-0053 – volume: 6 start-page: 55 year: 2012 ident: ref_21 article-title: Review of the BCI Competition IV publication-title: Front. Neurosci. doi: 10.3389/fnins.2012.00055 – volume: 2018 start-page: 1624637 year: 2018 ident: ref_47 article-title: Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients publication-title: J. Healthc. Eng. – ident: ref_23 – volume: 25 start-page: 392 year: 2017 ident: ref_46 article-title: EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2016.2646763 – volume: 4 start-page: 639 year: 2017 ident: ref_72 article-title: Motor Imagery and Error Related Potential Induced Position Control of a Robotic Arm publication-title: IEEE/CAA J. Autom. Sin. doi: 10.1109/JAS.2017.7510616 – volume: 6 start-page: 38565 year: 2016 ident: ref_38 article-title: Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks publication-title: Sci. Rep. doi: 10.1038/srep38565 – volume: 4 start-page: eaaw6844 year: 2019 ident: ref_42 article-title: Noninvasive Neuroimaging Enhances Continuous Neural Tracking for Robotic Device Control publication-title: Sci. Robot. doi: 10.1126/scirobotics.aaw6844 – volume: 9 start-page: 35766 year: 2021 ident: ref_12 article-title: Biologically-Inspired Legged Robot Locomotion Controlled with a BCI by Means of Cognitive Monitoring publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3062329 – volume: 51 start-page: 82 year: 2014 ident: ref_26 article-title: Quadcopter Flight Control Using a Low-Cost Hybrid Interface with EEG-Based Classification and Eye Tracking publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2014.04.020 – ident: ref_35 doi: 10.3390/s20247309 – ident: ref_86 doi: 10.3390/s16122050 – volume: 36 start-page: 78 year: 2019 ident: ref_13 article-title: The Role of the Control Framework for Continuous Teleoperation of a Brain–Machine Interface-Driven Mobile Robot publication-title: IEEE Trans. Robot. doi: 10.1109/TRO.2019.2943072 – volume: 53 start-page: 61 year: 2004 ident: ref_98 article-title: Evaluation of Subjective Mental Workload: A Comparison of SWAT, NASA-TLX, and Workload Profile Methods publication-title: Appl. Psychol. doi: 10.1111/j.1464-0597.2004.00161.x – volume: 18 start-page: 14 year: 2019 ident: ref_69 article-title: Vision-Aided Brain-Machine Interface Training System for Robotic Arm Control and Clinical Application on Two Patients with Cervical Spinal Cord Injury publication-title: BioMedical Eng. Online doi: 10.1186/s12938-019-0633-6 – volume: 1 start-page: 461 year: 2009 ident: ref_87 article-title: The nonlinear and nonstationary properties in eeg signals: Probing the complex fluctuations by hilbert–huang transform publication-title: Adv. Adapt. Data Anal. doi: 10.1142/S1793536909000199 – volume: 26 start-page: 101 year: 2018 ident: ref_14 article-title: A Self-Adaptive Online Brain-Machine Interface of a Humanoid Robot Through a General Type-2 Fuzzy Inference System publication-title: IEEE Trans. Fuzzy Syst. doi: 10.1109/TFUZZ.2016.2637403 – volume: 87 start-page: 247 year: 2017 ident: ref_33 article-title: Design and Implementation of a Multi Sensor Based Brain Computer Interface for a Robotic Wheelchair publication-title: J. Intell. Robot. Syst. Theory Appl. doi: 10.1007/s10846-017-0477-x – volume: 38 start-page: 439 year: 2022 ident: ref_36 article-title: BCI Based on Pedal End-Effector Triggered through Pedaling Imagery to Promote Excitability over the Feet Motor Area publication-title: Res. Biomed. Eng. doi: 10.1007/s42600-021-00196-7 – volume: 46 start-page: 936 year: 2016 ident: ref_84 article-title: A Wireless BCI and BMI System for Wearable Robots publication-title: IEEE Trans. Syst. Man Cybern. Syst. doi: 10.1109/TSMC.2015.2506618 – volume: 51 start-page: 5392 year: 2021 ident: ref_52 article-title: Ensemble Learning Based Brain-Computer Interface System for Ground Vehicle Control publication-title: IEEE Trans. Syst. Man Cybern. Syst. doi: 10.1109/TSMC.2019.2955478 – volume: 51 start-page: 993 year: 2004 ident: ref_20 article-title: Boosting Bit Rates in Noninvasive EEG Single-Trial Classifications by Feature Combination and Multiclass Paradigms publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2004.827088 – volume: 103 start-page: 969 year: 2015 ident: ref_5 article-title: Towards Independence: A BCI Telepresence Robot for People with Severe Motor Disabilities publication-title: Proc. IEEE doi: 10.1109/JPROC.2015.2419736 – volume: 10 start-page: 114 year: 2019 ident: ref_53 article-title: Brain-Controlled for Changing Modular Robot Configuration by Employing Neurosky’s Headset publication-title: (IJACSA) Int. J. Adv. Comput. Sci. Appl. – volume: 116 start-page: 1 year: 2021 ident: ref_58 article-title: A Closed-Loop Brain-Computer Interface with Augmented Reality Feedback for Industrial Human-Robot Collaboration publication-title: Int. J. Adv. Manuf. Technol. – volume: 7 start-page: 5874 year: 2020 ident: ref_65 article-title: SDN-Powered Humanoid with Edge Computing for Assisting Paralyzed Patients publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2019.2963288 – volume: 10 start-page: 046003 year: 2013 ident: ref_27 article-title: Quadcopter Control in Three-Dimensional Space Using a Noninvasive Motor Imagery-Based Brain-Computer Interface publication-title: J. Neural Eng. doi: 10.1088/1741-2560/10/4/046003 – volume: 28 start-page: 1226 year: 2020 ident: ref_41 article-title: Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2020.2981659 – volume: 32 start-page: 991 year: 2020 ident: ref_29 article-title: Quadcopter Robot Control Based on Hybrid Brain-Computer Interface System publication-title: Sens. Mater. – volume: 1 start-page: eaag3296 year: 2016 ident: ref_34 article-title: Hybrid EEG/EOG-Based Brain/Neural Hand Exoskeleton Restores Fully Independent Daily Living Activities after Quadriplegia publication-title: Sci. Robot. doi: 10.1126/scirobotics.aag3296 – volume: 38 start-page: 5391 year: 2017 ident: ref_94 article-title: Deep Learning with Convolutional Neural Networks for EEG Decoding and Visualization publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.23730 – volume: 27 start-page: 1200 year: 2019 ident: ref_55 article-title: EEG Artifacts Handling in a Real Practical Brain-Computer Interface Controlled Vehicle publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2019.2915801 – volume: 42 start-page: 4196 year: 2015 ident: ref_30 article-title: Brain Computer Interface System Based on Indoor Semi-Autonomous Navigation and Motor Imagery for Unmanned Aerial Vehicle Control publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2015.01.031 – volume: 12 start-page: 1 year: 2018 ident: ref_59 article-title: An Approach for Brain-Controlled Prostheses Based on a Facial Expression Paradigm publication-title: Front. Neurosci. doi: 10.3389/fnins.2018.00943 – volume: 18 start-page: 530 year: 2021 ident: ref_40 article-title: EEG-Based Volitional Control of Prosthetic Legs for Walking in Different Terrains publication-title: IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2019.2956110 – volume: 89 start-page: 1123 year: 2001 ident: ref_15 article-title: Motor Imagery and Direct Brain-Computer Communication publication-title: Proc. IEEE doi: 10.1109/5.939829 – volume: 26 start-page: 2367 year: 2018 ident: ref_1 article-title: An Asynchronous Control Paradigm Based on Sequential Motor Imagery and Its Application in Wheelchair Navigation publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2018.2881215 – volume: 7 start-page: 39 year: 2015 ident: ref_81 article-title: Mental States, EEG Manifestations, and Mentally Emulated Digital Circuits for Brain-Robot Interaction publication-title: IEEE Trans. Auton. Ment. Dev. doi: 10.1109/TAMD.2014.2387271 – volume: 11 start-page: 414 year: 2019 ident: ref_63 article-title: Motor-Imagery-Based Teleoperation of a Dual-Arm Robot Performing Manipulation Tasks publication-title: IEEE Trans. Cogn. Dev. Syst. doi: 10.1109/TCDS.2018.2875052 – ident: ref_82 doi: 10.1109/EMBC.2018.8512193 – volume: 16 start-page: 011001 year: 2019 ident: ref_10 article-title: A Comprehensive Review of EEG-Based Brain-Computer Interface Paradigms publication-title: J. Neural Eng. doi: 10.1088/1741-2552/aaf12e – volume: 8 start-page: 84070 year: 2020 ident: ref_43 article-title: Real-Time EEG-EMG Human-Machine Interface-Based Control System for a Lower-Limb Exoskeleton publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2991812 – volume: 27 start-page: 1750033 year: 2017 ident: ref_99 article-title: Online EEG Classification of Covert Speech for Brain-Computer Interfacing publication-title: Int. J. Neural Syst. doi: 10.1142/S0129065717500332 – ident: ref_80 doi: 10.1109/I2C2.2017.8321831 |
| SSID | ssj0023338 |
| Score | 2.509225 |
| SecondaryResourceType | review_article |
| Snippet | Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important... Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important... |
| SourceID | doaj pubmedcentral proquest crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 5802 |
| SubjectTerms | Brain research brain–computer interface (BCI) brain–machine interface (BMI) control electroencephalogram (EEG) Electroencephalography endogenous Human subjects Interfaces Metadata motor imagery (MI) Review |
| SummonAdditionalLinks | – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwEB5BucABtTxEoK0M4sDFahrHrxPqbrcUJHoq0t4ixw9aCSVld_n_zCTebSMhrvFIScaP-ebhbwA-WpskWQKOptJyunrJKZnErbNDWs3Ega7p-5W6_FF_W8plDritc1nl9kwcDurQe4qRn1CCVGurrfl895tT1yjKruYWGo_hCVGXUUmXXt47XAL9r5FNSKBrf7Ku0L5JkyMoWxs0UPVP8OW0OvKBubnYh-cZJ7KzcWIP4FHsXsCzB-yBL-HqjNFuXsWbsQidjXF-1ie26EI_sq-yxeILn6GpCmw2_7pmiFHZ-diFnp1HOibYfKxWfwXXF4vr-SXP7RG4Rx9uw1WN-k8IoErpvFEpqaBFcLEWtatar4wL7amXRnsi0avaEuerDLWJSToVpHgNe13fxTfAtJMqqtaUdWtqL-rWJ2eTkbHSqapKV8Cnrb4an6nDqYPFrwZdCFJts1NtAR92oncjX8a_hGak9J0AUVwPD_rVzybvmMadBu-kRvwS8Xdj66JQsUJ8i_-CmEMUcLidsibvu3Vzv0oKeL8bxh1DaRDXRdQ7yZBfq40tQE-mevJB05Hu9mbg3kY0h06gfPv_l7-DpxVdk6DSEnEIe5vVn3iE4GXTHg8r9C-reO7W priority: 102 providerName: ProQuest – databaseName: Scholars Portal Journals: Open Access dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NT9xADLUQXMoBAS0iLaBp1QOXlDCZzwNC7LKUIsEJJG7RZDIDSCjb7i5S---xk2xEJI69ZnzI2OP4OfY8A3y3NkqKBCmGSpvS1cuUikmpdbYpq5nQ0DVd36jLO3F1L-9XYDljs1Pg_N3UjuZJ3c2ef_z98-8UHf6EMk5M2Y_mHOOWNEQpuYYBSZN_Xou-mMBzTMNaUqGh-CAUNYz9A5g5bJJ8E3UuNmGjg4vsrLXvFqyEehvW35AIfoSbM0ZOPQuPbS86a3_3s2lkk7qatiSsbDL5mY4wYlVsNP41ZwhV2Xk7jJ6dB_pasHHbtP4Jbi8mt-PLtJuSkHpM5RapEmiGiDgqk84bFaOqdF65IHLheOmVcVV57KXRnrj0eJmh2bJKmBClU5XMd2C1ntZhF5h2UgVVmkyURvhclD46G40MXEfOM5fA4VJfhe8YxGmQxXOBmQSptuhVm8C3XvR3S5vxntCIlN4LENN182A6eyg6xyncceWd1AhjAm43lC7kKnCEubgXhB55AntLkxXL01NQNV1rq61J4Gu_jI5D1RBXB9Q7yVB6q41NQA9MPXih4Ur99NhQcCOow1xQfv4fO_gCHzjdqaA-lHwPVhezl7CPSGdRHjTn-BUCEfy6 priority: 102 providerName: Scholars Portal |
| Title | A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control |
| URI | https://www.proquest.com/docview/2700779798 https://www.proquest.com/docview/2702184789 https://pubmed.ncbi.nlm.nih.gov/PMC9370865 https://doaj.org/article/a1dca57222e446ebae36e28008925213 |
| Volume | 22 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9wwEB7a9NIeStMHdZssaumhFxNH1vOY3XiTFLKUksLejCxLJFC8Jbv5_5mxvMsaAr30ooM1B3nG8jejGX0D8M3aKAkJcoRKm9PVy5ySSbl1tk-rmdDTNV0v1OVv8WMpl3utvqgmLNEDJ8WduNPWO6kRxgJGLqFxoVSBo5tjLEfo6Xk-EfO2wdQQapUYeSUeoRKD-pM1R2STZjg72aJPT9I_8izHdZF7QDN_A68HD5GdpZUdwrPQvYVXe7yB72Bxxmgf34fbVH7O0gk_W0VWde0q8a6yqrrIpwhSLZvOrtYMvVN2nvrPs_NAPwg2S3Xq7-FmXt3MLvOhMULuUQebXAnUfETXqZDOGxWjanXZuiBK4XjjlXFtc-ql0Z7o83hToKWKVpgQpVOtLD_AQbfqwkdg2kkVVGMK0RjhS9H46Gw0MnAdOS9cBt-3-qr9QBpOvSv-1Bg8kGrrnWoz-LoT_ZuYMp4SmpLSdwJEbt0_QJPXg8nrf5k8g6Otyephx61rSqBrbbU1GXzZTeNeoQSI6wLqnWQootXGZqBHph4taDzT3d32rNvox2H4Jz_9jzf4DC85XaOg0pPyCA429w_hGJ2bTTOB53qpcTTziwm8mFaLn78m_beN47UwjxYo-rA |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB5V5QAcEE8RKGAQSFyipk4c2weEuo-yS9s9LdLeIsd2KBJKyu4ixI_iPzITJ9tGQtx6jS0lHntmvsmMvwF4q3UlyBPE6Cp1TFcvY0omxdroNq2mfEvXdL7IZ1-yzyux2oM__V0YKqvsbWJrqF1j6R_5ISVIpdRSq4-XP2LqGkXZ1b6FRjgWp_73LwzZNh_mE9zfd5yfTJfjWdx1FYgthj7bOM_wsyvEHYkwVuVVlTuZOuOzNDO8tLkyrjyyQklL3HO8THCZicuUr4TJHTWJQIt_C_1uQgolV1fxXYrhXiAvSlOdHG44ulOhuh82vctrOwMM4OywGPOadzu5D_c6WMqOwzl6AHu-fgh3r5EVPoLFMSPjsfYXoeadhbQCayo2rV0TyF7ZdPopHqFndGw0nm8YQmI2CU3v2cSTVWLjUBz_GJY3IbcnsF83tX8KTBqR-7xUSVaqzKZZaSujKyU8lxXniYngfS-vwnZM5dQw43uBEQuJttiJNoI3u6mXgZ7jX5NGJPTdBGLUbh80669Fp6CFOXLWCIlwyeNyfWl8mnuOcBrXghAnjeCg37KiU_NNcXUoI3i9G0YFpayLqT3KneZQGC2VjkAOtnrwQcOR-ttFS_WN4BFjTvHs_y9_Bbdny_Oz4my-OH0Odzjd0KCqlvQA9rfrn_4F4qZt-bI9rQyKG9aOv103LPQ |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB5VRUJwQDxFoIBBIHGJNmvHsX1AqPuiS2HFoUh7ixzHppUgKbuLUH8a_46ZPLZdCXHrNbaUeOyZ-SYz_gbgtTFBkieI0VWamK5expRMio01TVpN-4au6fMiO_qaflzK5R786e_CUFllbxMbQ13Wjv6RDyhBqpRRRg9CVxbxZTJ7f_4zpg5SlGnt22m0R-TYX_zG8G39bj7BvX7D-Wx6Mj6Kuw4DscMwaBNnKS4hIAZJpHU6CyErlSitT0VqeeEybcti6KRWjnjoeJHgkpMy1T5Im5XUMAKt_w0l5JBUTC0vYz2BoV9LZCSESQZrjq5V6u7nTe_-mi4BO9B2tzDziqeb3YU7HURlh-2Zugd7vroPt68QFz6AxSEjQ7Lyp239O2tTDKwObFqVdUv8yqbTD_EIvWTJRuP5miE8ZpOLyv44c2ziyUKxcVso_xBOrkNuj2C_qiv_GJiyMvNZoZO00KkTaeGCNUFLz1XgPLERvO3llbuOtZyaZ3zPMXoh0eZb0Ubwajv1vKXq-NekEQl9O4HYtZsH9epb3ilrboels1IhdPK4XF9YLzLPEVrjWhDuiAgO-i3LO5Vf55cHNIKX22FUVsrA2Mqj3GkOhdRKmwjUzlbvfNDuSHV22tB-I5DE-FM--f_LX8BN1Iv803xx_BRucbqsQQUu4gD2N6tf_hlCqE3xvDmsDPJrVo6_CYYxKg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Comprehensive+Review+of+Endogenous+EEG-Based+BCIs+for+Dynamic+Device+Control&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Natasha+Padfield&rft.au=Kenneth+Camilleri&rft.au=Tracey+Camilleri&rft.au=Simon+Fabri&rft.date=2022-08-03&rft.pub=MDPI+AG&rft.eissn=1424-8220&rft.volume=22&rft.issue=15&rft.spage=5802&rft_id=info:doi/10.3390%2Fs22155802&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_a1dca57222e446ebae36e28008925213 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |