P300 event-related potential detection using one-dimensional convolutional capsule networks
• A new method to P300 ERP detection called 1D-CapsNet. • The improvement of the convolution dimension allows better detection of P300 ERP. • Two classifiers based on 1D-CapsNet are proposed to better transform BCI. • The performance of 1D-CapsNet is better than other state-of-the-art algorithms....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Liu, Xiang [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Do denture processing techniques affect the mechanical properties of denture teeth? - Clements, Jody L. ELSEVIER, 2017, an international journal, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:174 ; year:2021 ; day:15 ; month:07 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.eswa.2021.114701 |
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| 520 | |a • A new method to P300 ERP detection called 1D-CapsNet. • The improvement of the convolution dimension allows better detection of P300 ERP. • Two classifiers based on 1D-CapsNet are proposed to better transform BCI. • The performance of 1D-CapsNet is better than other state-of-the-art algorithms. | ||
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• A new method to P300 ERP detection called 1D-CapsNet. • The improvement of the convolution dimension allows better detection of P300 ERP. • Two classifiers based on 1D-CapsNet are proposed to better transform BCI. • The performance of 1D-CapsNet is better than other state-of-the-art algorithms. |
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• A new method to P300 ERP detection called 1D-CapsNet. • The improvement of the convolution dimension allows better detection of P300 ERP. • Two classifiers based on 1D-CapsNet are proposed to better transform BCI. • The performance of 1D-CapsNet is better than other state-of-the-art algorithms. |
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• A new method to P300 ERP detection called 1D-CapsNet. • The improvement of the convolution dimension allows better detection of P300 ERP. • Two classifiers based on 1D-CapsNet are proposed to better transform BCI. • The performance of 1D-CapsNet is better than other state-of-the-art algorithms. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV053981995</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624203545.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210910s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.eswa.2021.114701</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001576.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV053981995</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0957-4174(21)00142-1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.96</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Liu, Xiang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">P300 event-related potential detection using one-dimensional convolutional capsule networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• A new method to P300 ERP detection called 1D-CapsNet. • The improvement of the convolution dimension allows better detection of P300 ERP. • Two classifiers based on 1D-CapsNet are proposed to better transform BCI. • The performance of 1D-CapsNet is better than other state-of-the-art algorithms.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Brain-computer interface</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Ergonomics</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Capsule network</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">EEG classification</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Features extraction</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xie, Qingsheng</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lv, Jian</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Haisong</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Weixing</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Clements, Jody L. 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