Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources

A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yu-Ting Liu [verfasserIn] Nikhil R. Pal [verfasserIn] Amar R. Marathe [verfasserIn] Yu-Kai Wang [verfasserIn] Chin-Teng Lin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Human-Machine Autonomous (HMA) System Brain-Computer Interface (BCI) Fuzzy Decision-Making Fuser (FDMF) Dempster-Shafer Theory Information Fusion

Übergeordnetes Werk:	In: Frontiers in Neuroscience - Frontiers Media S.A., 2008, 11(2017)
Übergeordnetes Werk:	volume:11 ; year:2017

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnins.2017.00332

Katalog-ID:	DOAJ036405965

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spelling	10.3389/fnins.2017.00332 doi (DE-627)DOAJ036405965 (DE-599)DOAJ39243e779498486881709b8e6c0254b3 DE-627 ger DE-627 rakwb eng RC321-571 Yu-Ting Liu verfasserin aut Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems. Human-Machine Autonomous (HMA) System Brain-Computer Interface (BCI) Fuzzy Decision-Making Fuser (FDMF) Dempster-Shafer Theory Information Fusion Neurosciences. Biological psychiatry. Neuropsychiatry Nikhil R. Pal verfasserin aut Amar R. Marathe verfasserin aut Yu-Kai Wang verfasserin aut Chin-Teng Lin verfasserin aut In Frontiers in Neuroscience Frontiers Media S.A., 2008 11(2017) (DE-627)55908109X (DE-600)2411902-7 1662453X nnns volume:11 year:2017 https://doi.org/10.3389/fnins.2017.00332 kostenfrei https://doaj.org/article/39243e779498486881709b8e6c0254b3 kostenfrei http://journal.frontiersin.org/article/10.3389/fnins.2017.00332/full kostenfrei https://doaj.org/toc/1662-453X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2017
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allfieldsSound	10.3389/fnins.2017.00332 doi (DE-627)DOAJ036405965 (DE-599)DOAJ39243e779498486881709b8e6c0254b3 DE-627 ger DE-627 rakwb eng RC321-571 Yu-Ting Liu verfasserin aut Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems. Human-Machine Autonomous (HMA) System Brain-Computer Interface (BCI) Fuzzy Decision-Making Fuser (FDMF) Dempster-Shafer Theory Information Fusion Neurosciences. Biological psychiatry. Neuropsychiatry Nikhil R. Pal verfasserin aut Amar R. Marathe verfasserin aut Yu-Kai Wang verfasserin aut Chin-Teng Lin verfasserin aut In Frontiers in Neuroscience Frontiers Media S.A., 2008 11(2017) (DE-627)55908109X (DE-600)2411902-7 1662453X nnns volume:11 year:2017 https://doi.org/10.3389/fnins.2017.00332 kostenfrei https://doaj.org/article/39243e779498486881709b8e6c0254b3 kostenfrei http://journal.frontiersin.org/article/10.3389/fnins.2017.00332/full kostenfrei https://doaj.org/toc/1662-453X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2017
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callnumber-first	R - Medicine
author	Yu-Ting Liu
spellingShingle	Yu-Ting Liu misc RC321-571 misc Human-Machine Autonomous (HMA) System misc Brain-Computer Interface (BCI) misc Fuzzy Decision-Making Fuser (FDMF) misc Dempster-Shafer Theory misc Information Fusion misc Neurosciences. Biological psychiatry. Neuropsychiatry Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources
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topic_title	RC321-571 Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources Human-Machine Autonomous (HMA) System Brain-Computer Interface (BCI) Fuzzy Decision-Making Fuser (FDMF) Dempster-Shafer Theory Information Fusion
topic	misc RC321-571 misc Human-Machine Autonomous (HMA) System misc Brain-Computer Interface (BCI) misc Fuzzy Decision-Making Fuser (FDMF) misc Dempster-Shafer Theory misc Information Fusion misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc Human-Machine Autonomous (HMA) System misc Brain-Computer Interface (BCI) misc Fuzzy Decision-Making Fuser (FDMF) misc Dempster-Shafer Theory misc Information Fusion misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_browse	misc RC321-571 misc Human-Machine Autonomous (HMA) System misc Brain-Computer Interface (BCI) misc Fuzzy Decision-Making Fuser (FDMF) misc Dempster-Shafer Theory misc Information Fusion misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources
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title_sort	fuzzy decision-making fuser (fdmf) for integrating human-machine autonomous (hma) systems with adaptive evidence sources
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title_auth	Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources
abstract	A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems.
abstractGer	A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems.
abstract_unstemmed	A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems.
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title_short	Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources
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In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Human-Machine Autonomous (HMA) System</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Brain-Computer Interface (BCI)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fuzzy Decision-Making Fuser (FDMF)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dempster-Shafer Theory</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Information Fusion</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neurosciences. Biological psychiatry. Neuropsychiatry</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Nikhil R. Pal</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Amar R. Marathe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Yu-Kai Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Chin-Teng Lin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Frontiers in Neuroscience</subfield><subfield code="d">Frontiers Media S.A., 2008</subfield><subfield code="g">11(2017)</subfield><subfield code="w">(DE-627)55908109X</subfield><subfield code="w">(DE-600)2411902-7</subfield><subfield code="x">1662453X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:11</subfield><subfield code="g">year:2017</subfield></datafield><datafield tag="856" ind1="4" 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Fuzzy Decision-Making Fuser (FDMF) for Integrating Human-Machine Autonomous (HMA) Systems with Adaptive Evidence Sources

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