Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface
We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. The EEG-based BCI headset was designed from commercial off-the-shelf...
Saved in:
Published in | Sensors (Basel, Switzerland) Vol. 23; no. 13; p. 5930 |
---|---|
Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
MDPI AG
26.06.2023
MDPI |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications.
The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation.
The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home.
The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications. |
---|---|
AbstractList | Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain–computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user’s hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal–to–noise ratio (SNR) and common–mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device’s use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications. Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain–computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user’s hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal–to–noise ratio (SNR) and common–mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device’s use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications. We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home. The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications. |
Audience | Academic |
Author | Contreras-Vidal, Jose L Craik, Alexander Feng, Jeff Francisco, Gerard E Edquilang, David Wong, Sarah González-España, Juan José Alamir, Ayman Sánchez Rodríguez, Lianne |
AuthorAffiliation | 2 Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry—University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA 4 Department of Electrical Engineering, Jazan University, Jazan 45142, Saudi Arabia 1 Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA 6 Department of Physical Medicine & Rehabilitation, University of Texas Health McGovern Medical School, Houston, TX 77030, USA 7 The Institute for Rehabilitation and Research (TIRR) Memorial Hermann Hospital, Houston, TX 77030, USA 3 Department of Biomedical Engineering, University of Houston, Houston, TX 77004, USA 5 Department of Industrial Design, University of Houston, Houston, TX 77004, USA |
AuthorAffiliation_xml | – name: 7 The Institute for Rehabilitation and Research (TIRR) Memorial Hermann Hospital, Houston, TX 77030, USA – name: 2 Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry—University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA – name: 3 Department of Biomedical Engineering, University of Houston, Houston, TX 77004, USA – name: 6 Department of Physical Medicine & Rehabilitation, University of Texas Health McGovern Medical School, Houston, TX 77030, USA – name: 1 Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA – name: 5 Department of Industrial Design, University of Houston, Houston, TX 77004, USA – name: 4 Department of Electrical Engineering, Jazan University, Jazan 45142, Saudi Arabia |
Author_xml | – sequence: 1 givenname: Alexander orcidid: 0000-0003-3871-1839 surname: Craik fullname: Craik, Alexander organization: Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA – sequence: 2 givenname: Juan José orcidid: 0000-0003-0134-7762 surname: González-España fullname: González-España, Juan José organization: Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA – sequence: 3 givenname: Ayman orcidid: 0009-0006-8598-2200 surname: Alamir fullname: Alamir, Ayman organization: Department of Electrical Engineering, Jazan University, Jazan 45142, Saudi Arabia – sequence: 4 givenname: David surname: Edquilang fullname: Edquilang, David organization: Department of Industrial Design, University of Houston, Houston, TX 77004, USA – sequence: 5 givenname: Sarah orcidid: 0009-0008-0911-1088 surname: Wong fullname: Wong, Sarah organization: Department of Industrial Design, University of Houston, Houston, TX 77004, USA – sequence: 6 givenname: Lianne orcidid: 0009-0003-5021-8577 surname: Sánchez Rodríguez fullname: Sánchez Rodríguez, Lianne organization: Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA – sequence: 7 givenname: Jeff orcidid: 0000-0001-5777-1250 surname: Feng fullname: Feng, Jeff organization: Department of Industrial Design, University of Houston, Houston, TX 77004, USA – sequence: 8 givenname: Gerard E orcidid: 0000-0002-5681-1916 surname: Francisco fullname: Francisco, Gerard E organization: The Institute for Rehabilitation and Research (TIRR) Memorial Hermann Hospital, Houston, TX 77030, USA – sequence: 9 givenname: Jose L orcidid: 0000-0002-6499-1208 surname: Contreras-Vidal fullname: Contreras-Vidal, Jose L organization: Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37447780$$D View this record in MEDLINE/PubMed |
BookMark | eNptkk1vEzEQhi1URD_gwB9AK3GBwxZ_7dp7Qm0aSqRUXICr5bXHwdHGDvYuiH9fh5TQIGTJHs0889oznnN0EmIAhF4SfMlYh99lyghrOoafoDPCKa8lpfjkkX2KznNeY0wZY_IZOmWCcyEkPkN3N5D9KlQ62OqrHrzVo4-hiq7S1TL-rGcxj9Vd7P0A1Xx-W1_rDLa6TtqHEttspxFStQhld9rAc_TU6SHDi4fzAn35MP88-1gvP90uZlfL2jRMjLVtOiKI6xqnpYW-p1j0GmjPnWu0EZI5LIqbWMat4ZwI2Za3t8XiwA0QdoEWe10b9Vptk9_o9EtF7dVvR0wrpdPozQAKeBGmLSGWN1w0prOib6ArjpbbjrZF6_1eazv1G7AGwpj0cCR6HAn-m1rFH4pgxltKZVF486CQ4vcJ8qg2PhsYBh0gTlkVRFK-63lBX_-DruOUQunVjmq5YILIv9RKlwp8cLFcbHai6ko0ksvyjaJQl_-hyrKw8aZMiCufdpzwdp9gUsw5gTsUSbDaDZI6DFJhXz3uyoH8MznsHrV4wGs |
CitedBy_id | crossref_primary_10_2478_msr_2024_0009 |
Cites_doi | 10.1109/ICHCI51889.2020.00056 10.1088/1741-2552/ab260c 10.1016/S0013-4694(97)00106-5 10.1161/STROKEAHA.116.016304 10.3389/fnins.2012.00151 10.1201/9781315375212 10.1007/s12152-019-09409-4 10.1109/IEEECONF44664.2019.9048990 10.1109/TCDS.2021.3090217 10.1109/EMBC.2012.6346806 10.1109/TBME.2004.827072 10.1088/1741-2560/13/2/026013 10.1109/EMBC.2019.8857575 10.1109/OJEMB.2021.3059161 10.1007/s002210000382 10.3389/fcomp.2022.860619 10.1109/2.60881 10.1155/2015/346217 10.1109/VS-GAMES.2016.7590339 10.1016/j.snb.2016.10.005 10.3390/mi11070635 10.1088/1741-2560/8/6/066009 10.1088/1741-2552/ab2b61 10.3389/fnhum.2017.00078 10.1088/1741-2552/aaa8c0 10.1016/j.apergo.2016.06.002 10.1088/1741-2552/aaf12e 10.1007/978-3-030-80091-8_1 10.1088/1741-2560/13/2/023001 10.1016/j.ergon.2018.01.002 10.1016/0013-4694(80)90216-3 10.3390/e18090272 10.1016/j.neuroimage.2022.119774 10.1101/2020.04.26.20077529 10.1038/s41598-018-32283-8 10.1088/1741-2552/ab0ab5 10.1088/1741-2560/12/3/031001 10.1002/hbm.20571 10.1109/SMC.2019.8914210 10.3390/s19050987 10.1371/journal.pone.0038931 10.1016/0013-4694(80)90040-1 10.1088/1741-2560/11/4/046018 10.1002/hbm.21248 10.14714/CP58.270 10.1016/0013-4694(78)90107-4 10.1109/TNSRE.2021.3113888 10.1109/EMBC.2013.6609559 10.1109/ICICS.2015.7459835 10.1016/j.clinph.2007.07.028 10.1016/S1388-2457(03)00093-2 10.3389/fnins.2016.00122 10.1109/CNE.2007.369707 10.1371/journal.pone.0244820 10.1109/IEMBS.2011.6091549 10.1109/ACCESS.2021.3100700 10.1080/21646821.2016.1245559 10.1016/j.apergo.2016.02.008 |
ContentType | Journal Article |
Copyright | COPYRIGHT 2023 MDPI AG 2023 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. 2023 by the authors. 2023 |
Copyright_xml | – notice: COPYRIGHT 2023 MDPI AG – notice: 2023 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: 2023 by the authors. 2023 |
DBID | NPM AAYXX CITATION 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PIMPY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
DOI | 10.3390/s23135930 |
DatabaseName | PubMed CrossRef ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials ProQuest Databases ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Publicly Available Content Database 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 | PubMed CrossRef Publicly Available Content Database ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Central China ProQuest Hospital Collection (Alumni) ProQuest Central ProQuest Health & Medical Complete Health Research Premium Collection ProQuest Medical Library ProQuest One Academic UKI Edition Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest One Academic ProQuest Medical Library (Alumni) ProQuest Central (Alumni) MEDLINE - Academic |
DatabaseTitleList | Publicly Available Content Database CrossRef PubMed |
Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X7 name: Health & Medical Collection url: https://search.proquest.com/healthcomplete sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering |
EISSN | 1424-8220 |
ExternalDocumentID | oai_doaj_org_article_e4e2b2611d45475c9d7b5e961164d926 A758483337 10_3390_s23135930 37447780 |
Genre | Journal Article |
GeographicLocations | United States Texas |
GeographicLocations_xml | – name: Texas – name: United States |
GrantInformation_xml | – fundername: National Science Foundation grantid: 1827769 – fundername: National Science Foundation grantid: 1650536 – fundername: Jazan University – fundername: NSF Industry–University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) center grantid: 2137255 – fundername: National Science Foundation (NSF) Partnership for Innovations (PFI) and Research Experiences for Undergraduates (REU) grantid: 1827769 |
GroupedDBID | --- 123 2WC 3V. 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH ABDBF ABJCF ABUWG ADBBV AENEX AFKRA AFZYC ALIPV ALMA_UNASSIGNED_HOLDINGS ARAPS BENPR BPHCQ BVXVI CCPQU CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IAO ITC KB. KQ8 L6V M1P M48 M7S MODMG M~E NPM OK1 P2P P62 PDBOC PIMPY PQQKQ PROAC PSQYO RIG RNS RPM TUS UKHRP XSB ~8M AAYXX CITATION BGLVJ 7XB 8FK AZQEC DWQXO K9. PQEST PQUKI PRINS 7X8 5PM |
ID | FETCH-LOGICAL-c537t-d59171f95fa8debb207bae2b4ff5ac783f07ebb1d34dc44178600264414e4ce13 |
IEDL.DBID | RPM |
ISSN | 1424-8220 |
IngestDate | Sun Sep 29 07:13:02 EDT 2024 Tue Sep 17 21:30:33 EDT 2024 Fri Jun 28 12:35:51 EDT 2024 Tue Sep 24 19:33:55 EDT 2024 Fri Feb 23 00:02:24 EST 2024 Fri Feb 02 04:42:20 EST 2024 Thu Sep 26 16:15:42 EDT 2024 Sat Sep 28 08:18:53 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 13 |
Keywords | neurodiagnostics electroencephalography rehabilitation brain–computer interfaces mobile EEG motor intent detection |
Language | English |
License | 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-c537t-d59171f95fa8debb207bae2b4ff5ac783f07ebb1d34dc44178600264414e4ce13 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ORCID | 0009-0008-0911-1088 0009-0003-5021-8577 0009-0006-8598-2200 0000-0001-5777-1250 0000-0002-6499-1208 0000-0003-0134-7762 0000-0002-5681-1916 0000-0003-3871-1839 |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346228/ |
PMID | 37447780 |
PQID | 2836473718 |
PQPubID | 2032333 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_e4e2b2611d45475c9d7b5e961164d926 pubmedcentral_primary_oai_pubmedcentral_nih_gov_10346228 proquest_miscellaneous_2838243744 proquest_journals_2836473718 gale_infotracmisc_A758483337 gale_infotracacademiconefile_A758483337 crossref_primary_10_3390_s23135930 pubmed_primary_37447780 |
PublicationCentury | 2000 |
PublicationDate | 20230626 |
PublicationDateYYYYMMDD | 2023-06-26 |
PublicationDate_xml | – month: 6 year: 2023 text: 20230626 day: 26 |
PublicationDecade | 2020 |
PublicationPlace | Switzerland |
PublicationPlace_xml | – name: Switzerland – name: Basel |
PublicationTitle | Sensors (Basel, Switzerland) |
PublicationTitleAlternate | Sensors (Basel) |
PublicationYear | 2023 |
Publisher | MDPI AG MDPI |
Publisher_xml | – name: MDPI AG – name: MDPI |
References | Lacko (ref_48) 2017; 58 Kilicarslan (ref_5) 2016; 13 Bowsher (ref_26) 2016; 13 Niso (ref_41) 2022; 269 Xing (ref_36) 2018; 8 ref_14 ref_58 ref_57 ref_12 Hekmatmanesh (ref_19) 2021; 9 ref_56 Craik (ref_7) 2019; 16 ref_11 ref_55 Karlson (ref_50) 2006; 1 ref_54 Roy (ref_8) 2019; 16 ref_51 Niazi (ref_71) 2011; 8 ref_18 ref_17 ref_15 ref_59 Hairston (ref_44) 2014; 11 Verwulgen (ref_45) 2018; 64 Zander (ref_67) 2017; 11 ref_61 Lu (ref_70) 2012; 33 Ellena (ref_49) 2016; 55 (ref_1) 2020; 13 ref_24 He (ref_33) 2018; 15 ref_23 ref_22 Paek (ref_25) 2021; 2 ref_66 ref_21 ref_65 ref_20 ref_64 ref_63 ref_62 ref_29 ref_28 Shibasaki (ref_73) 1980; 49 ref_35 Goncharova (ref_76) 2003; 114 ref_79 ref_34 Mooney (ref_27) 1990; 23 ref_32 ref_31 ref_75 Kuhlman (ref_78) 1978; 44 ref_30 Nuwer (ref_60) 1998; 106 Steele (ref_10) 2021; 29 ref_39 Wang (ref_53) 2012; 6 Bhagat (ref_81) 2016; 10 ref_38 Kilicarslan (ref_6) 2019; 16 Schalk (ref_40) 2004; 51 Hallett (ref_69) 1994; 34 Nijholt (ref_2) 2022; 4 Goldenholz (ref_4) 2009; 30 Zhou (ref_13) 2021; 14 ref_47 Bangor (ref_68) 2009; 4 Siemionow (ref_72) 2000; 133 Arpaia (ref_37) 2021; 71 ref_43 Bundy (ref_16) 2017; 48 ref_42 Barry (ref_77) 2007; 118 Acharya (ref_52) 2016; 56 (ref_3) 2015; 12 Shakeel (ref_74) 2015; 2015 Abiri (ref_9) 2019; 16 Li (ref_46) 2017; 241 Schoppenhorst (ref_80) 1980; 48 |
References_xml | – ident: ref_21 doi: 10.1109/ICHCI51889.2020.00056 – volume: 16 start-page: 051001 year: 2019 ident: ref_8 article-title: Deep learning-based electroencephalography analysis: A systematic review publication-title: J. Neural Eng. doi: 10.1088/1741-2552/ab260c contributor: fullname: Roy – volume: 106 start-page: 259 year: 1998 ident: ref_60 article-title: IFCN standards for digital recording of clinical EEG publication-title: Electroencephalogr. Clin. Neurophysiol. doi: 10.1016/S0013-4694(97)00106-5 contributor: fullname: Nuwer – volume: 48 start-page: 1908 year: 2017 ident: ref_16 article-title: Contralesional brain—Computer interface control of a powered exoskeleton for motor recovery in chronic stroke survivors publication-title: Stroke doi: 10.1161/STROKEAHA.116.016304 contributor: fullname: Bundy – volume: 6 start-page: 151 year: 2012 ident: ref_53 article-title: Multi-class motor imagery EEG decoding for brain-computer interfaces publication-title: Front. Neurosci. doi: 10.3389/fnins.2012.00151 contributor: fullname: Wang – ident: ref_39 – ident: ref_47 doi: 10.1201/9781315375212 – volume: 13 start-page: 163 year: 2020 ident: ref_1 article-title: The history of BCI: From a vision for the future to real support for personhood in people with locked-in syndrome publication-title: Neuroethics doi: 10.1007/s12152-019-09409-4 – ident: ref_42 – ident: ref_61 – ident: ref_35 – ident: ref_58 – ident: ref_11 doi: 10.1109/IEEECONF44664.2019.9048990 – volume: 1 start-page: 86 year: 2006 ident: ref_50 article-title: Understanding single-handed mobile device interaction publication-title: Handb. Res. User Interface Des. Eval. Mob. Technol. contributor: fullname: Karlson – volume: 14 start-page: 799 year: 2021 ident: ref_13 article-title: Cognitive workload recognition using eeg signals and machine learning: A review publication-title: IEEE Trans. Cogn. Dev. Syst. doi: 10.1109/TCDS.2021.3090217 contributor: fullname: Zhou – ident: ref_75 doi: 10.1109/EMBC.2012.6346806 – volume: 51 start-page: 1034 year: 2004 ident: ref_40 article-title: BCI2000: A general-purpose brain-computer interface (BCI) system publication-title: IEEE Trans. Biomed. Eng. doi: 10.1109/TBME.2004.827072 contributor: fullname: Schalk – volume: 13 start-page: 026013 year: 2016 ident: ref_5 article-title: A robust adaptive de-noising framework for real-time artifact removal in scalp eeg measurements publication-title: J. Neural Eng. doi: 10.1088/1741-2560/13/2/026013 contributor: fullname: Kilicarslan – ident: ref_14 doi: 10.1109/EMBC.2019.8857575 – volume: 71 start-page: 4002209 year: 2021 ident: ref_37 article-title: Metrological characterization of consumer-grade equipment for wearable brain—Computer interfaces and extended reality publication-title: IEEE Trans. Instrum. Meas. contributor: fullname: Arpaia – volume: 2 start-page: 84 year: 2021 ident: ref_25 article-title: A roadmap towards standards for neurally controlled end effectors publication-title: IEEE Open J. Eng. Med. Biol. doi: 10.1109/OJEMB.2021.3059161 contributor: fullname: Paek – volume: 133 start-page: 303 year: 2000 ident: ref_72 article-title: Relationship between motor activity-related cortical potential and voluntary muscle activation publication-title: Exp. Brain Res. doi: 10.1007/s002210000382 contributor: fullname: Siemionow – volume: 4 start-page: 860619 year: 2022 ident: ref_2 article-title: Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-Being) Applications publication-title: Front. Comput. Sci. doi: 10.3389/fcomp.2022.860619 contributor: fullname: Nijholt – volume: 23 start-page: 59 year: 1990 ident: ref_27 article-title: Strategies for supporting application portability publication-title: Computer doi: 10.1109/2.60881 contributor: fullname: Mooney – volume: 2015 start-page: 346217 year: 2015 ident: ref_74 article-title: A review of techniques for detection of movement intention using movement-related cortical potentials publication-title: Comput. Math. Methods Med. doi: 10.1155/2015/346217 contributor: fullname: Shakeel – ident: ref_22 doi: 10.1109/VS-GAMES.2016.7590339 – ident: ref_66 – volume: 241 start-page: 1244 year: 2017 ident: ref_46 article-title: Towards gel-free electrodes: A systematic study of electrode-skin impedance publication-title: Sens. Actuators Chem. doi: 10.1016/j.snb.2016.10.005 contributor: fullname: Li – ident: ref_55 doi: 10.3390/mi11070635 – ident: ref_62 – volume: 8 start-page: 066009 year: 2011 ident: ref_71 article-title: Detection of movement intention from single-trial movement-related cortical potentials publication-title: J. Neural Eng. doi: 10.1088/1741-2560/8/6/066009 contributor: fullname: Niazi – volume: 16 start-page: 056027 year: 2019 ident: ref_6 article-title: Characterization and real-time removal of motion artifacts from EEG signals publication-title: J. Neural Eng. doi: 10.1088/1741-2552/ab2b61 contributor: fullname: Kilicarslan – volume: 11 start-page: 78 year: 2017 ident: ref_67 article-title: Evaluation of a dry eeg system for application of passive brain-computer interfaces in autonomous driving publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2017.00078 contributor: fullname: Zander – volume: 4 start-page: 114 year: 2009 ident: ref_68 article-title: Determining what individual SUS scores mean: Adding an adjective rating scale publication-title: J. Usability Stud. contributor: fullname: Bangor – ident: ref_20 – ident: ref_59 – volume: 15 start-page: 021004 year: 2018 ident: ref_33 article-title: Brain–machine interfaces for controlling lower-limb powered robotic systems publication-title: J. Neural Eng. doi: 10.1088/1741-2552/aaa8c0 contributor: fullname: He – volume: 58 start-page: 128 year: 2017 ident: ref_48 article-title: Ergonomic design of an eeg headset using 3d anthropometry publication-title: Appl. Ergon. doi: 10.1016/j.apergo.2016.06.002 contributor: fullname: Lacko – ident: ref_28 – volume: 16 start-page: 011001 year: 2019 ident: ref_9 article-title: A comprehensive review of EEG-based brain—Computer interface paradigms publication-title: J. Neural Eng. doi: 10.1088/1741-2552/aaf12e contributor: fullname: Abiri – ident: ref_30 – ident: ref_56 doi: 10.1007/978-3-030-80091-8_1 – ident: ref_24 – ident: ref_34 – volume: 13 start-page: 023001 year: 2016 ident: ref_26 article-title: Others Brain–computer interface devices for patients with paralysis and amputation: A meeting report publication-title: J. Neural Eng. doi: 10.1088/1741-2560/13/2/023001 contributor: fullname: Bowsher – volume: 64 start-page: 108 year: 2018 ident: ref_45 article-title: A new data structure and workflow for using 3d anthropometry in the design of wearable products publication-title: Int. J. Ind. Ergon. doi: 10.1016/j.ergon.2018.01.002 contributor: fullname: Verwulgen – volume: 49 start-page: 213 year: 1980 ident: ref_73 article-title: Components of the movement-related cortical potential and their scalp topography publication-title: Electroencephalogr. Clin. Neurophysiol. doi: 10.1016/0013-4694(80)90216-3 contributor: fullname: Shibasaki – ident: ref_12 doi: 10.3390/e18090272 – volume: 269 start-page: 119774 year: 2022 ident: ref_41 article-title: Wireless eeg: An survey of systems and studies publication-title: NeuroImage doi: 10.1016/j.neuroimage.2022.119774 contributor: fullname: Niso – ident: ref_17 doi: 10.1101/2020.04.26.20077529 – volume: 8 start-page: 14708 year: 2018 ident: ref_36 article-title: A high-speed SSVEP-based BCI using dry EEG electrodes publication-title: Sci. Rep. doi: 10.1038/s41598-018-32283-8 contributor: fullname: Xing – volume: 16 start-page: 031001 year: 2019 ident: ref_7 article-title: Deep learning for electroencephalogram (eeg) classification tasks: A review publication-title: J. Neural Eng. doi: 10.1088/1741-2552/ab0ab5 contributor: fullname: Craik – ident: ref_63 – volume: 12 start-page: 031001 year: 2015 ident: ref_3 article-title: EEG artifact removal—State-of-the-art and guidelines publication-title: J. Neural Eng. doi: 10.1088/1741-2560/12/3/031001 – volume: 30 start-page: 1077 year: 2009 ident: ref_4 article-title: Mapping the signal-to-noise-ratios of cortical sources in magnetoencephalography and electroencephalography publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.20571 contributor: fullname: Goldenholz – ident: ref_23 doi: 10.1109/SMC.2019.8914210 – ident: ref_32 doi: 10.3390/s19050987 – ident: ref_79 doi: 10.1371/journal.pone.0038931 – volume: 48 start-page: 25 year: 1980 ident: ref_80 article-title: The significance of coherence estimates in determining central alpha and mu activities publication-title: Electroencephalogr. Clin. Neurophysiol. doi: 10.1016/0013-4694(80)90040-1 contributor: fullname: Schoppenhorst – volume: 11 start-page: 046018 year: 2014 ident: ref_44 article-title: Usability of four commercially-oriented eeg systems publication-title: J. Neural Eng. doi: 10.1088/1741-2560/11/4/046018 contributor: fullname: Hairston – volume: 33 start-page: 824 year: 2012 ident: ref_70 article-title: Movement related cortical potentials of cued versus self-initiated movements: Double dissociated modulation by dorsal premotor cortex versus supplementary motor area rTMS publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.21248 contributor: fullname: Lu – ident: ref_65 doi: 10.14714/CP58.270 – volume: 44 start-page: 83 year: 1978 ident: ref_78 article-title: Functional topography of the human mu rhythm publication-title: Electroencephalogr. Clin. Neurophysiol. doi: 10.1016/0013-4694(78)90107-4 contributor: fullname: Kuhlman – volume: 29 start-page: 2037 year: 2021 ident: ref_10 article-title: A mixed filtering approach for real-time seizure state tracking using multi-channel electroencephalography data publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2021.3113888 contributor: fullname: Steele – ident: ref_29 – ident: ref_54 doi: 10.1109/EMBC.2013.6609559 – ident: ref_18 doi: 10.1109/ICICS.2015.7459835 – volume: 118 start-page: 2765 year: 2007 ident: ref_77 article-title: EEG differences between eyes-closed and eyes-open resting conditions publication-title: Clin. Neurophysiol. doi: 10.1016/j.clinph.2007.07.028 contributor: fullname: Barry – volume: 114 start-page: 1580 year: 2003 ident: ref_76 article-title: EMG contamination of EEG: Spectral and topographical characteristics publication-title: Clin. Neurophysiol. doi: 10.1016/S1388-2457(03)00093-2 contributor: fullname: Goncharova – ident: ref_15 – ident: ref_64 – ident: ref_43 – volume: 10 start-page: 122 year: 2016 ident: ref_81 article-title: Design and optimization of an eeg-based brain machine interface (bmi) to an upper-limb exoskeleton for stroke survivors publication-title: Front. Neurosci. doi: 10.3389/fnins.2016.00122 contributor: fullname: Bhagat – ident: ref_57 – ident: ref_51 doi: 10.1109/CNE.2007.369707 – ident: ref_31 doi: 10.1371/journal.pone.0244820 – ident: ref_38 doi: 10.1109/IEMBS.2011.6091549 – volume: 34 start-page: 5 year: 1994 ident: ref_69 article-title: Movement-related cortical potentials publication-title: Electromyogr. Clin. Neurophysiol. contributor: fullname: Hallett – volume: 9 start-page: 110173 year: 2021 ident: ref_19 article-title: Review of the state-of-the-art of brain-controlled vehicles publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3100700 contributor: fullname: Hekmatmanesh – volume: 56 start-page: 253 year: 2016 ident: ref_52 article-title: American clinical neurophysiology society guideline 3: A proposal for standard montages to be used in clinical eeg publication-title: Neurodiagn. J. doi: 10.1080/21646821.2016.1245559 contributor: fullname: Acharya – volume: 55 start-page: 194 year: 2016 ident: ref_49 article-title: The helmet fit index—An intelligent tool for fit assessment and design customisation publication-title: Appl. Ergon. doi: 10.1016/j.apergo.2016.02.008 contributor: fullname: Ellena |
SSID | ssj0023338 |
Score | 2.464822 |
Snippet | We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop... Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for... Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for... |
SourceID | doaj pubmedcentral proquest gale crossref pubmed |
SourceType | Open Website Open Access Repository Aggregation Database Index Database |
StartPage | 5930 |
SubjectTerms | Brackets brain–computer interfaces Closed loops Coding Computer software industry Electrodes Electroencephalography Eye movements Form factors Head movement Headsets Human-computer interface Hypertext HyperText Markup Language Inertial platforms Interfaces Internet of Things Interoperability Laboratories Low cost Medical research Meetings Microprocessors mobile EEG motor intent detection neurodiagnostics Physiology Portability rehabilitation Reliability Sensors Signal processing Software Support vector machines Usability Vertical orientation |
SummonAdditionalLinks | – databaseName: DOAJ: Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwEB2hnuCA-CZQkEFInKzGsR07x27ZUiEKF4p6s_wVwSVB7FZc-x_6D_klzCTZVSIOXLjajmS_sT3zkskbgDfWKi9qkzhGvy1XISkeKlPxUnmdpQ82BiKK55_qswv14VJfzkp9UU7YKA88AneUVa4ChvkikfSUjk0yQecGG2qVmmoU2xZ6R6YmqiWReY06QhJJ_dEGoxipG0p1nnmfQaT_76t45ouWeZIzx3N6D-5OESM7Hmd6H27l7gHcmekIPoTP74Y8DOa7xL5iYD3WSWJ9yzz72P_iJ_1my877gDcAW6_f8xW6rsRWVB3i9_XNrrADG94Otj7mR3Bxuv5ycsanSgk8amm2PGlkXaJtdOttyiFUpQkecVNtq300VralwWaRpEqRio5Z-hxHoZDKKmYhH8NB13f5KbBBDkfLSkSJ3E-nBmOSXEZfGy_x5hQFvN4h6H6MghgOiQTB7PYwF7AibPcDSMN6aEDLusmy7l-WLeAtWcbRSUP4o59-GMB5kmaVO0aqoyxa2hRwuBiJJyQuu3e2ddMJ3ThcZq2MRNdcwKt9Nz1JWWdd7q-GMZYEG5Uq4Mm4FfZLolZjLC7VLjbJYs3Lnu77t0G_W5RS1Yjzs_-B0nO4XeF-p-y1qj6Eg-3Pq_wC46RteDkciT_LTQ2l priority: 102 providerName: Directory of Open Access Journals – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwEB6VcoFDxZuUggxC4mTtJrZj54S6ZcsKUbhQtLfIrxQuSemm4sp_4B_yS5jJY7sREtexI9kz43nYk28AXhsjbZrrwDH6rbh0QXKX6YzPpVVRWGe8o0Tx7FO-Opcf1mq9B6vxXxgqqxxtYmeoQ-PpjnyGbjCXWqApnVlHtwC-nb29_MGpfxS9sw7NNG7B7TTDsAI1W69vUi-BmViPKyQwyZ9tMKoRqqDS5x1v1IH2_2uad3zTtG5yxxGd3oODIYJkx73I78NerB_A3R1cwYfw-V1Xl8FsHdhXDLT7vkmsqZhlH5uf_KTZtOyscWgR2HL5ni_QlQW2oG4Rf379Hhs9sO62sLI-PoLz0-WXkxUfOidwr4RueVCYhaVVoSprQnQum2tnY-ZkVSnrtRHVXCM5DUIGT03IDD3PUWgko_QxFY9hv27q-BRYB4-jRJZ6gbmgCgXGKHHuba6tQEuaJvBq5GB52QNklJhYEJvLLZsTWBBvtxMI07ojNFcX5XBEyihxhZjQpYFAxpQvgnYqFkjIZSiyPIE3JJmSTh7J3w4_EOA6CcOqPMbURxqUtE7gaDITT4yfDo-yLYcTuylv9CuBl9th-pKq0OrYXHdzDAE4SpnAk14VtlsiqtYGt2omSjLZ83Sk_v6tw_NO50LmyOfD_6_rGdyhXvdUp5blR7DfXl3H5xgRte5Fp-x_AWZRCd8 priority: 102 providerName: ProQuest – databaseName: Scholars Portal Open Access Journals dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwELaqcoEDKu-UggxC4mRI_IidQ4W6ZUuFWLiwqLfIr0ClKoHdrYBb_wP_kF_CTF7aCA5cx7aUGY8988X2N4Q8M0baLNeBQfZbMemCZI5rzlJpVRTWGe8QKC7e56dL-fZMne2QocZmb8D1P6Ed1pNari5e_Pj28xUs-ENEnADZX64hRxGqEIDcr3EpJDr6Qo6HCVyItqA1vuliEA_TjmBoOnQSllr2_r_36K0gNb1AuRWRTvbIzT6VpEfd3N8iO7G-TW5sEQzeIR9etxc0qK0D_QQZd1dAiTYVtfRd850dN-sNXTQOtgY6n79hM4hpgc6wbMTvq19DxQfa_jasrI93yfJk_vH4lPUlFJhXQm9YUADHsqpQlTUhOsdT7WzkTlaVsl4bUaUaxFkQMnisRmbwnA5zJBmlj5m4R3brpo4PCG15cpTgmRcAClUoIFmJqbe5tgK21CwhTwcLll87powSEAaauRzNnJAZ2nbsgOTWraBZfS77tVJGCV8IyC4LyDamfBG0U7EAQS5DwfOEPMeZKdEpwPze9i8J4DuRzKo8AgwkDcy6TsjBpCcsHT9tHua2HDyvBDVzqQXE7IQ8GZtxJF5Hq2Nz2fYxyOQoZULud64wqoRSrQ2oaiZOMtF52lKff2mJvbMUnBfsvP8_pnxIrnPwbby2xvMDsrtZXcZHkCBt3OPW_f8A0cQKyg priority: 102 providerName: Scholars Portal |
Title | Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface |
URI | https://www.ncbi.nlm.nih.gov/pubmed/37447780 https://www.proquest.com/docview/2836473718/abstract/ https://search.proquest.com/docview/2838243744 https://pubmed.ncbi.nlm.nih.gov/PMC10346228 https://doaj.org/article/e4e2b2611d45475c9d7b5e961164d926 |
Volume | 23 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB615QIHxJtAWRmExCndjR-xc-wuu60QWypE0d4ivwKVaFJ1t-LKf-Af8ksYO8lqI25ccrAdyR7PeOZzJt8AvFWK6yyXLsXot0q5cTw1VNJ0wrXwTBtlTQCKy7P89IJ_WInVHuT9vzAxad-ay6P6x9VRffk95lZeX9lxnyc2Pl_OsgnjOaVqvA_7krEeo3cwiyHqajmEGAL68RojGCYKFmq-Mcm5lIECcscJRa7-f0_kHZc0TJfc8T-LB3C_CxzJcTvBh7Dn60dwb4dO8DF8eh_TMYiuHfmK8XVbLok0FdHkY_MznTXrDVk2Bg8CMp-fpFP0YI5MQ5GIP79-9_UdSLwkrLT1T-BiMf8yO027ggmpFUxuUicQfGVVISqtnDeGTqTRnhpeVUJbqVg1kdicOcadDbXHVPgqFyIi7rn1GXsKB3VT--dAIiuOYDSzDCGgcAWGJn5idS41wwM0S-BNL8HyuuXFKBFPBImXW4knMA2y3Q4IVNaxobn5VnYbWnqOM0Qcl7nALSZs4aQRvsCGnLuC5gm8CztTBoND8Vvd_TeA8wzUVeUxIh6ucNNlAoeDkWgodtjd723ZGeq6xGXmXDL00Am83naHN0PyWe2b2zhGBd5GzhN41qrCdkm9RiWgBkoyWPOwB7U60nj3Wvzi_199CXcpKnxIXaP5IRxsbm79KwySNmaElrGS-FSLkxHcmc7Pzj-P4oUDPpdcjaLN_AVYxBSk |
link.rule.ids | 230,315,733,786,790,870,891,2115,2236,12083,12792,21416,24346,27957,27958,31754,31755,33408,33409,33779,33780,43345,43635,43840,53827,53829,74102,74392,74659 |
linkProvider | National Library of Medicine |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3NbtQwELZgewAOiN8SKGAQEierSWzHzgl1y5YFdheEWtSb5b9AL0nb3Yor78Ab8iTMJNllIySuY0eyx_bMfPbkG0JeaS1sVqjAIPqtmHBBMJernKXCysit094hUJwviumJ-HAqT_sLt2WfVrm2ia2hDo3HO_J9cIOFUBxM6ZvzC4ZVo_B1tS-hcZ3sCA5QZUR2xpPF5y8byMUBgXV8QhzA_f4SohkuS0x53vJCLVn_vyZ5yycN8yW3HNDRHXK7jxzpQbfUd8m1WN8jt7b4BO-TT2_bfAxq60C_QoDd1UuiTUUtnTU_2GGzXNF548AS0MnkHRuDCwt0jFUifv_8tS7wQNtbwsr6-ICcHE2OD6esr5jAvORqxYIE9JVVpaysDtG5PFXOxtyJqpLWK82rVIE4C1wEj8XHND7LYUgkovAx4w_JqG7q-IjQlhZH8jzzqFgZSohNYuptoSwHC5ol5OVag-a8I8YwAChQzWaj5oSMUbebDshl3Qqay2-mPxomChghALksILmY9GVQTsYSBIUIZV4k5DWujMETB-r3tv9xAMaJ3FXmACCP0LDSKiF7g55wUvyweb22pj-pS_N3XyXkxaYZv8Tsszo2V20fjcSNQiRkt9sKmymhVCkNU9WDTTKY87ClPvve8nhnKRcF6Pnx_8f1nNyYHs9nZvZ-8fEJuYn17jFXLS_2yGh1eRWfQlS0cs_6rf8Hw5kKeA |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagSIgeUHkHWjAIiZO1SWzHzqnqtrst0BYOFPVm-RXoJSndrbjyH_iH_JLOJNmwERJX25Hs8Xge9uT7CHmrtbBZoQKD6LdiwgXBXK5ylgorI7dOe4eJ4slpcXQmPpzL877-adGXVa5sYmuoQ-PxjnwCbrAQioMpnVR9WcTng_nu5Q-GDFL40trTadwmdzDIRjYDPT8cki8OuViHLMQhzZ8sIK7hssTi5zV_1ML2_2uc17zTuHJyzRXNt8j9Poake92mPyC3Yv2QbK4hCz4inw7aygxq60C_QqjdMSfRpqKWHjc_2X6zWNKTxoFNoLPZIZuCMwt0inwRf379XlE90Pa-sLI-PiZn89mX_SPWcycwL7lasiAhD8uqUlZWh-hcnipnY-5EVUnrleZVqqA5C1wEjzRkGh_oMDgSUfiY8Sdko27q-IzQFiBH8jzzHLJBGUqIUmLqbaEsB1uaJeTNSoLmsoPIMJBaoJjNIOaETFG2wwBEtW4bmqtvpj8kJgqYIaR0WUCYMenLoJyMJTQUIpR5kZB3uDMGzx6I39v-FwKYJ6JYmT1IfoSGnVYJ2R6NhDPjx92rvTX9mV2YvxqWkNdDN36JdWh1bK7bMRohHIVIyNNOFYYlYatSGpaqR0oyWvO4p7743iJ6ZykXBcj5-f_n9YrcBZ03x-9PP74g95D4HovW8mKbbCyvruMOhEdL97LV-xvScg1H |
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=Design+and+Validation+of+a+Low-Cost+Mobile+EEG-Based+Brain%E2%80%93Computer+Interface&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Craik%2C+Alexander&rft.au=Gonz%C3%A1lez-Espa%C3%B1a%2C+Juan+Jos%C3%A9&rft.au=Alamir%2C+Ayman&rft.au=Edquilang%2C+David&rft.date=2023-06-26&rft.issn=1424-8220&rft.eissn=1424-8220&rft.volume=23&rft.issue=13&rft.spage=5930&rft_id=info:doi/10.3390%2Fs23135930&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_s23135930 |
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 |