EEG Processing in Internet of Medical Things Using Non-Harmonic Analysis: Application and Evolution for SSVEP Responses
In recent years, the Internet of Things has been applied in many fields with rapid development, such as software, sensors, and medical and healthcare. In the case of medical and healthcare, extensive research has focused on the development of brain-computer interface systems, particularly those util...
Gespeichert in:
| Veröffentlicht in: | IEEE access 2019, Vol.7, p.11318-11327 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 11327 |
|---|---|
| container_issue | |
| container_start_page | 11318 |
| container_title | IEEE access |
| container_volume | 7 |
| creator | Jia, Dongbao Dai, Hongwei Takashima, Yuta Nishio, Takuro Hirobayashi, Kanna Hasegawa, Masaya Hirobayashi, Shigeki Misawa, Tadanobu |
| description | In recent years, the Internet of Things has been applied in many fields with rapid development, such as software, sensors, and medical and healthcare. In the case of medical and healthcare, extensive research has focused on the development of brain-computer interface systems, particularly those utilizing steady-state visual-evoked potentials (SSVEPs). However, the conventional short-time Fourier transform (STFT) analysis is associated with the low-frequency resolution because of the length of the analysis window, resulting in sidelobe artifacts. In this paper, we utilized the non-harmonic analysis (NHA), which does not depend on the length of the analysis window, to analyze the continuous changes in and determine the classification accuracy of SSVEPs. Moreover, our experiments utilized the gray-scale images, allowing for the presentation of the stimulus as a sinusoidal pattern and reducing the effect of frequency distortion associated with the refresh rate of the liquid-crystal display. Our findings indicated that NHA resulted in exponential improvements in time-frequency resolution when compared with the STFT analysis. As the accuracy of NHA was high, our results suggest that this method is effective for examining SSVEPs and changes in brain waves during experiments conducted using liquid-crystal displays. |
| doi_str_mv | 10.1109/ACCESS.2019.2892188 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2455612154</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8610084</ieee_id><doaj_id>oai_doaj_org_article_1a36578c3b4342cd8e10b3e8e6e9676b</doaj_id><sourcerecordid>2455612154</sourcerecordid><originalsourceid>FETCH-LOGICAL-c474t-d5dcc81bcd6c49252737d31fb99a13abdf2424dd6697a7e5b4c9ff3ed68897463</originalsourceid><addsrcrecordid>eNpNkU9P3DAQxSPUSkWUT8DFEuds47-xe1utUlgJWtSFXi3HnlCvgp3a2VZ8e8wGofoynpn3nmX9quoCNyuMG_Vlvdl0u92KNFitiFQES3lSnRIsVE05FR_-u3-qznPeN-XIMuLtafWv667QXYoWcvbhEfmAtmGGFGBGcUC34Lw1I7r_XZYZPRw132Oor016isFbtA5mfM4-f0XraRqLePYxIBMc6v7G8XDshpjQbveru0M_IU8xZMifq4-DGTOcv9Wz6uFbd7-5rm9-XG0365vaspbNtePOWol764RlinDS0tZRPPRKGUxN7wbCCHNOCNWaFnjPrBoGCk5IqVom6Fm1XXJdNHs9Jf9k0rOOxuvjIKZHbdLs7QgaGyp4Ky3tGWXEOgm46SlIEKBEK_qSdblkTSn-OUCe9T4eUvl_1oRxLjDBnBUVXVQ2xZwTDO-v4ka_AtMLMP0KTL8BK66LxeUB4N0hBS6oGH0B16KRfQ</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2455612154</pqid></control><display><type>article</type><title>EEG Processing in Internet of Medical Things Using Non-Harmonic Analysis: Application and Evolution for SSVEP Responses</title><source>IEEE Open Access Journals</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Jia, Dongbao ; Dai, Hongwei ; Takashima, Yuta ; Nishio, Takuro ; Hirobayashi, Kanna ; Hasegawa, Masaya ; Hirobayashi, Shigeki ; Misawa, Tadanobu</creator><creatorcontrib>Jia, Dongbao ; Dai, Hongwei ; Takashima, Yuta ; Nishio, Takuro ; Hirobayashi, Kanna ; Hasegawa, Masaya ; Hirobayashi, Shigeki ; Misawa, Tadanobu</creatorcontrib><description>In recent years, the Internet of Things has been applied in many fields with rapid development, such as software, sensors, and medical and healthcare. In the case of medical and healthcare, extensive research has focused on the development of brain-computer interface systems, particularly those utilizing steady-state visual-evoked potentials (SSVEPs). However, the conventional short-time Fourier transform (STFT) analysis is associated with the low-frequency resolution because of the length of the analysis window, resulting in sidelobe artifacts. In this paper, we utilized the non-harmonic analysis (NHA), which does not depend on the length of the analysis window, to analyze the continuous changes in and determine the classification accuracy of SSVEPs. Moreover, our experiments utilized the gray-scale images, allowing for the presentation of the stimulus as a sinusoidal pattern and reducing the effect of frequency distortion associated with the refresh rate of the liquid-crystal display. Our findings indicated that NHA resulted in exponential improvements in time-frequency resolution when compared with the STFT analysis. As the accuracy of NHA was high, our results suggest that this method is effective for examining SSVEPs and changes in brain waves during experiments conducted using liquid-crystal displays.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2019.2892188</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Biomedical imaging ; Brain computer interface ; Chirp ; chirp stimulus ; Distortion ; Fourier analysis ; Fourier transforms ; grayscale images ; Harmonic analysis ; Health care ; Human-computer interface ; Image classification ; Internet of medical things ; Liquid crystal displays ; Medical research ; Microsoft Windows ; non-harmonic analysis ; Sidelobes ; steady-state visual evoked potential ; Time-frequency analysis ; Visualization</subject><ispartof>IEEE access, 2019, Vol.7, p.11318-11327</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c474t-d5dcc81bcd6c49252737d31fb99a13abdf2424dd6697a7e5b4c9ff3ed68897463</citedby><orcidid>0000-0001-7007-4134</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8610084$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Jia, Dongbao</creatorcontrib><creatorcontrib>Dai, Hongwei</creatorcontrib><creatorcontrib>Takashima, Yuta</creatorcontrib><creatorcontrib>Nishio, Takuro</creatorcontrib><creatorcontrib>Hirobayashi, Kanna</creatorcontrib><creatorcontrib>Hasegawa, Masaya</creatorcontrib><creatorcontrib>Hirobayashi, Shigeki</creatorcontrib><creatorcontrib>Misawa, Tadanobu</creatorcontrib><title>EEG Processing in Internet of Medical Things Using Non-Harmonic Analysis: Application and Evolution for SSVEP Responses</title><title>IEEE access</title><addtitle>Access</addtitle><description>In recent years, the Internet of Things has been applied in many fields with rapid development, such as software, sensors, and medical and healthcare. In the case of medical and healthcare, extensive research has focused on the development of brain-computer interface systems, particularly those utilizing steady-state visual-evoked potentials (SSVEPs). However, the conventional short-time Fourier transform (STFT) analysis is associated with the low-frequency resolution because of the length of the analysis window, resulting in sidelobe artifacts. In this paper, we utilized the non-harmonic analysis (NHA), which does not depend on the length of the analysis window, to analyze the continuous changes in and determine the classification accuracy of SSVEPs. Moreover, our experiments utilized the gray-scale images, allowing for the presentation of the stimulus as a sinusoidal pattern and reducing the effect of frequency distortion associated with the refresh rate of the liquid-crystal display. Our findings indicated that NHA resulted in exponential improvements in time-frequency resolution when compared with the STFT analysis. As the accuracy of NHA was high, our results suggest that this method is effective for examining SSVEPs and changes in brain waves during experiments conducted using liquid-crystal displays.</description><subject>Biomedical imaging</subject><subject>Brain computer interface</subject><subject>Chirp</subject><subject>chirp stimulus</subject><subject>Distortion</subject><subject>Fourier analysis</subject><subject>Fourier transforms</subject><subject>grayscale images</subject><subject>Harmonic analysis</subject><subject>Health care</subject><subject>Human-computer interface</subject><subject>Image classification</subject><subject>Internet of medical things</subject><subject>Liquid crystal displays</subject><subject>Medical research</subject><subject>Microsoft Windows</subject><subject>non-harmonic analysis</subject><subject>Sidelobes</subject><subject>steady-state visual evoked potential</subject><subject>Time-frequency analysis</subject><subject>Visualization</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkU9P3DAQxSPUSkWUT8DFEuds47-xe1utUlgJWtSFXi3HnlCvgp3a2VZ8e8wGofoynpn3nmX9quoCNyuMG_Vlvdl0u92KNFitiFQES3lSnRIsVE05FR_-u3-qznPeN-XIMuLtafWv667QXYoWcvbhEfmAtmGGFGBGcUC34Lw1I7r_XZYZPRw132Oor016isFbtA5mfM4-f0XraRqLePYxIBMc6v7G8XDshpjQbveru0M_IU8xZMifq4-DGTOcv9Wz6uFbd7-5rm9-XG0365vaspbNtePOWol764RlinDS0tZRPPRKGUxN7wbCCHNOCNWaFnjPrBoGCk5IqVom6Fm1XXJdNHs9Jf9k0rOOxuvjIKZHbdLs7QgaGyp4Ky3tGWXEOgm46SlIEKBEK_qSdblkTSn-OUCe9T4eUvl_1oRxLjDBnBUVXVQ2xZwTDO-v4ka_AtMLMP0KTL8BK66LxeUB4N0hBS6oGH0B16KRfQ</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Jia, Dongbao</creator><creator>Dai, Hongwei</creator><creator>Takashima, Yuta</creator><creator>Nishio, Takuro</creator><creator>Hirobayashi, Kanna</creator><creator>Hasegawa, Masaya</creator><creator>Hirobayashi, Shigeki</creator><creator>Misawa, Tadanobu</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-7007-4134</orcidid></search><sort><creationdate>2019</creationdate><title>EEG Processing in Internet of Medical Things Using Non-Harmonic Analysis: Application and Evolution for SSVEP Responses</title><author>Jia, Dongbao ; Dai, Hongwei ; Takashima, Yuta ; Nishio, Takuro ; Hirobayashi, Kanna ; Hasegawa, Masaya ; Hirobayashi, Shigeki ; Misawa, Tadanobu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c474t-d5dcc81bcd6c49252737d31fb99a13abdf2424dd6697a7e5b4c9ff3ed68897463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Biomedical imaging</topic><topic>Brain computer interface</topic><topic>Chirp</topic><topic>chirp stimulus</topic><topic>Distortion</topic><topic>Fourier analysis</topic><topic>Fourier transforms</topic><topic>grayscale images</topic><topic>Harmonic analysis</topic><topic>Health care</topic><topic>Human-computer interface</topic><topic>Image classification</topic><topic>Internet of medical things</topic><topic>Liquid crystal displays</topic><topic>Medical research</topic><topic>Microsoft Windows</topic><topic>non-harmonic analysis</topic><topic>Sidelobes</topic><topic>steady-state visual evoked potential</topic><topic>Time-frequency analysis</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jia, Dongbao</creatorcontrib><creatorcontrib>Dai, Hongwei</creatorcontrib><creatorcontrib>Takashima, Yuta</creatorcontrib><creatorcontrib>Nishio, Takuro</creatorcontrib><creatorcontrib>Hirobayashi, Kanna</creatorcontrib><creatorcontrib>Hasegawa, Masaya</creatorcontrib><creatorcontrib>Hirobayashi, Shigeki</creatorcontrib><creatorcontrib>Misawa, Tadanobu</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jia, Dongbao</au><au>Dai, Hongwei</au><au>Takashima, Yuta</au><au>Nishio, Takuro</au><au>Hirobayashi, Kanna</au><au>Hasegawa, Masaya</au><au>Hirobayashi, Shigeki</au><au>Misawa, Tadanobu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>EEG Processing in Internet of Medical Things Using Non-Harmonic Analysis: Application and Evolution for SSVEP Responses</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2019</date><risdate>2019</risdate><volume>7</volume><spage>11318</spage><epage>11327</epage><pages>11318-11327</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>In recent years, the Internet of Things has been applied in many fields with rapid development, such as software, sensors, and medical and healthcare. In the case of medical and healthcare, extensive research has focused on the development of brain-computer interface systems, particularly those utilizing steady-state visual-evoked potentials (SSVEPs). However, the conventional short-time Fourier transform (STFT) analysis is associated with the low-frequency resolution because of the length of the analysis window, resulting in sidelobe artifacts. In this paper, we utilized the non-harmonic analysis (NHA), which does not depend on the length of the analysis window, to analyze the continuous changes in and determine the classification accuracy of SSVEPs. Moreover, our experiments utilized the gray-scale images, allowing for the presentation of the stimulus as a sinusoidal pattern and reducing the effect of frequency distortion associated with the refresh rate of the liquid-crystal display. Our findings indicated that NHA resulted in exponential improvements in time-frequency resolution when compared with the STFT analysis. As the accuracy of NHA was high, our results suggest that this method is effective for examining SSVEPs and changes in brain waves during experiments conducted using liquid-crystal displays.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2019.2892188</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-7007-4134</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2169-3536 |
| ispartof | IEEE access, 2019, Vol.7, p.11318-11327 |
| issn | 2169-3536 2169-3536 |
| language | eng |
| recordid | cdi_proquest_journals_2455612154 |
| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Biomedical imaging Brain computer interface Chirp chirp stimulus Distortion Fourier analysis Fourier transforms grayscale images Harmonic analysis Health care Human-computer interface Image classification Internet of medical things Liquid crystal displays Medical research Microsoft Windows non-harmonic analysis Sidelobes steady-state visual evoked potential Time-frequency analysis Visualization |
| title | EEG Processing in Internet of Medical Things Using Non-Harmonic Analysis: Application and Evolution for SSVEP Responses |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T23%3A37%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=EEG%20Processing%20in%20Internet%20of%20Medical%20Things%20Using%20Non-Harmonic%20Analysis:%20Application%20and%20Evolution%20for%20SSVEP%20Responses&rft.jtitle=IEEE%20access&rft.au=Jia,%20Dongbao&rft.date=2019&rft.volume=7&rft.spage=11318&rft.epage=11327&rft.pages=11318-11327&rft.issn=2169-3536&rft.eissn=2169-3536&rft.coden=IAECCG&rft_id=info:doi/10.1109/ACCESS.2019.2892188&rft_dat=%3Cproquest_cross%3E2455612154%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2455612154&rft_id=info:pmid/&rft_ieee_id=8610084&rft_doaj_id=oai_doaj_org_article_1a36578c3b4342cd8e10b3e8e6e9676b&rfr_iscdi=true |