EEG Based Biometric Framework for Automatic Identity Verification
Abstract The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component releva...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Palaniappan, Ramaswamy [verfasserIn] Mandic, Danilo P. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2007 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of VLSI signal processing systems for signal, image and video technology - Springer Netherlands, 1989, 49(2007), 2 vom: 28. Juni, Seite 243-250 |
|---|---|
| Übergeordnetes Werk: |
volume:49 ; year:2007 ; number:2 ; day:28 ; month:06 ; pages:243-250 |
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| DOI / URN: |
10.1007/s11265-007-0078-1 |
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SPR018319408 |
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| 520 | |a Abstract The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56 ± 1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud. | ||
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10.1007/s11265-007-0078-1 doi (DE-627)SPR018319408 (SPR)s11265-007-0078-1-e DE-627 ger DE-627 rakwb eng Palaniappan, Ramaswamy verfasserin aut EEG Based Biometric Framework for Automatic Identity Verification 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56 ± 1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud. biometric (dpeaa)DE-He213 Davies–Bouldin index (dpeaa)DE-He213 electroencephalogram (dpeaa)DE-He213 identity identification (dpeaa)DE-He213 neural network (dpeaa)DE-He213 Mandic, Danilo P. verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 49(2007), 2 vom: 28. Juni, Seite 243-250 (DE-627)SPR018308090 nnns volume:49 year:2007 number:2 day:28 month:06 pages:243-250 https://dx.doi.org/10.1007/s11265-007-0078-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 49 2007 2 28 06 243-250 |
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10.1007/s11265-007-0078-1 doi (DE-627)SPR018319408 (SPR)s11265-007-0078-1-e DE-627 ger DE-627 rakwb eng Palaniappan, Ramaswamy verfasserin aut EEG Based Biometric Framework for Automatic Identity Verification 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56 ± 1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud. biometric (dpeaa)DE-He213 Davies–Bouldin index (dpeaa)DE-He213 electroencephalogram (dpeaa)DE-He213 identity identification (dpeaa)DE-He213 neural network (dpeaa)DE-He213 Mandic, Danilo P. verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 49(2007), 2 vom: 28. Juni, Seite 243-250 (DE-627)SPR018308090 nnns volume:49 year:2007 number:2 day:28 month:06 pages:243-250 https://dx.doi.org/10.1007/s11265-007-0078-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 49 2007 2 28 06 243-250 |
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10.1007/s11265-007-0078-1 doi (DE-627)SPR018319408 (SPR)s11265-007-0078-1-e DE-627 ger DE-627 rakwb eng Palaniappan, Ramaswamy verfasserin aut EEG Based Biometric Framework for Automatic Identity Verification 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56 ± 1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud. biometric (dpeaa)DE-He213 Davies–Bouldin index (dpeaa)DE-He213 electroencephalogram (dpeaa)DE-He213 identity identification (dpeaa)DE-He213 neural network (dpeaa)DE-He213 Mandic, Danilo P. verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 49(2007), 2 vom: 28. Juni, Seite 243-250 (DE-627)SPR018308090 nnns volume:49 year:2007 number:2 day:28 month:06 pages:243-250 https://dx.doi.org/10.1007/s11265-007-0078-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 49 2007 2 28 06 243-250 |
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10.1007/s11265-007-0078-1 doi (DE-627)SPR018319408 (SPR)s11265-007-0078-1-e DE-627 ger DE-627 rakwb eng Palaniappan, Ramaswamy verfasserin aut EEG Based Biometric Framework for Automatic Identity Verification 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56 ± 1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud. biometric (dpeaa)DE-He213 Davies–Bouldin index (dpeaa)DE-He213 electroencephalogram (dpeaa)DE-He213 identity identification (dpeaa)DE-He213 neural network (dpeaa)DE-He213 Mandic, Danilo P. verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 49(2007), 2 vom: 28. Juni, Seite 243-250 (DE-627)SPR018308090 nnns volume:49 year:2007 number:2 day:28 month:06 pages:243-250 https://dx.doi.org/10.1007/s11265-007-0078-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 49 2007 2 28 06 243-250 |
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Abstract The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56 ± 1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud. |
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Abstract The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56 ± 1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud. |
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Abstract The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56 ± 1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud. |
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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">SPR018319408</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124222359.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2007 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11265-007-0078-1</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR018319408</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11265-007-0078-1-e</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="100" ind1="1" ind2=" "><subfield code="a">Palaniappan, Ramaswamy</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">EEG Based Biometric Framework for Automatic Identity Verification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2007</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies–Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56 ± 1.87%. 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Juni, Seite 243-250</subfield><subfield code="w">(DE-627)SPR018308090</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:49</subfield><subfield code="g">year:2007</subfield><subfield code="g">number:2</subfield><subfield code="g">day:28</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:243-250</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s11265-007-0078-1</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">49</subfield><subfield code="j">2007</subfield><subfield code="e">2</subfield><subfield code="b">28</subfield><subfield code="c">06</subfield><subfield code="h">243-250</subfield></datafield></record></collection>
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