Brain signal-based safety measure activation for robotic systems
In this paper, we present our approach for using EEG signals to activate safety measures of a robot when an error or unexpected event is perceived by the human operator. In particular, we consider brain-based error perception while the operator passively observes the robot performing an action. Our...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Penaloza, Christian I [verfasserIn] |
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| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: © 2015 Taylor & Francis and The Robotics Society of Japan 2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Advanced robotics - Utrecht : VNU Sciences Pr., 1986, 29(2015), 19, Seite 1234 |
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| Übergeordnetes Werk: |
volume:29 ; year:2015 ; number:19 ; pages:1234 |
| Links: |
|---|
| DOI / URN: |
10.1080/01691864.2015.1057615 |
|---|
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| 520 | |a In this paper, we present our approach for using EEG signals to activate safety measures of a robot when an error or unexpected event is perceived by the human operator. In particular, we consider brain-based error perception while the operator passively observes the robot performing an action. Our approach consists of monitoring EEG signals and detecting a brain potential called error related negativity (ERN) that spontaneously occurs when the operator perceives an error made by the robot or when an unexpected event occurs. We detect ERN by pre-training two linear classifiers using data collected from a preliminary experiment based on a visual reaction task. We derive the probability of failure in demand (PFD), commonly used to assess functional safety for a two-channel verification system based on the combination of linear classifiers. Functional safety analysis was then performed on a BMI-based robotic framework in which a signal was sent to the robot to active its safety measures in when an ERN was detected. Using brain-based signals, we demonstrate that it is possible to send an emergency stop action during mobile navigation task when unexpected events occur with an accuracy of 75%. | ||
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10.1080/01691864.2015.1057615 doi PQ20160617 (DE-627)OLC1957218428 (DE-599)GBVOLC1957218428 (PRQ)i1169-9f2d1c7e32a68c8fb19ed5fb853f07f537157d5d7e01a07c357faac7e9d961c40 (KEY)0142017820150000029001901234brainsignalbasedsafetymeasureactivationforrobotics DE-627 ger DE-627 rakwb eng 004 620 DNB 50.25 bkl Penaloza, Christian I verfasserin aut Brain signal-based safety measure activation for robotic systems 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we present our approach for using EEG signals to activate safety measures of a robot when an error or unexpected event is perceived by the human operator. In particular, we consider brain-based error perception while the operator passively observes the robot performing an action. Our approach consists of monitoring EEG signals and detecting a brain potential called error related negativity (ERN) that spontaneously occurs when the operator perceives an error made by the robot or when an unexpected event occurs. We detect ERN by pre-training two linear classifiers using data collected from a preliminary experiment based on a visual reaction task. We derive the probability of failure in demand (PFD), commonly used to assess functional safety for a two-channel verification system based on the combination of linear classifiers. Functional safety analysis was then performed on a BMI-based robotic framework in which a signal was sent to the robot to active its safety measures in when an ERN was detected. Using brain-based signals, we demonstrate that it is possible to send an emergency stop action during mobile navigation task when unexpected events occur with an accuracy of 75%. Nutzungsrecht: © 2015 Taylor & Francis and The Robotics Society of Japan 2015 robot tele-operation functional safety event related negativity brain machine interface Mae, Yasushi oth Kojima, Masaru oth Arai, Tatsuo oth Enthalten in Advanced robotics Utrecht : VNU Sciences Pr., 1986 29(2015), 19, Seite 1234 (DE-627)12921616X (DE-600)55912-X (DE-576)029137179 0169-1864 nnns volume:29 year:2015 number:19 pages:1234 http://dx.doi.org/10.1080/01691864.2015.1057615 Volltext http://www.tandfonline.com/doi/abs/10.1080/01691864.2015.1057615 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2020 GBV_ILN_2244 50.25 AVZ AR 29 2015 19 1234 |
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10.1080/01691864.2015.1057615 doi PQ20160617 (DE-627)OLC1957218428 (DE-599)GBVOLC1957218428 (PRQ)i1169-9f2d1c7e32a68c8fb19ed5fb853f07f537157d5d7e01a07c357faac7e9d961c40 (KEY)0142017820150000029001901234brainsignalbasedsafetymeasureactivationforrobotics DE-627 ger DE-627 rakwb eng 004 620 DNB 50.25 bkl Penaloza, Christian I verfasserin aut Brain signal-based safety measure activation for robotic systems 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper, we present our approach for using EEG signals to activate safety measures of a robot when an error or unexpected event is perceived by the human operator. In particular, we consider brain-based error perception while the operator passively observes the robot performing an action. Our approach consists of monitoring EEG signals and detecting a brain potential called error related negativity (ERN) that spontaneously occurs when the operator perceives an error made by the robot or when an unexpected event occurs. We detect ERN by pre-training two linear classifiers using data collected from a preliminary experiment based on a visual reaction task. We derive the probability of failure in demand (PFD), commonly used to assess functional safety for a two-channel verification system based on the combination of linear classifiers. Functional safety analysis was then performed on a BMI-based robotic framework in which a signal was sent to the robot to active its safety measures in when an ERN was detected. Using brain-based signals, we demonstrate that it is possible to send an emergency stop action during mobile navigation task when unexpected events occur with an accuracy of 75%. Nutzungsrecht: © 2015 Taylor & Francis and The Robotics Society of Japan 2015 robot tele-operation functional safety event related negativity brain machine interface Mae, Yasushi oth Kojima, Masaru oth Arai, Tatsuo oth Enthalten in Advanced robotics Utrecht : VNU Sciences Pr., 1986 29(2015), 19, Seite 1234 (DE-627)12921616X (DE-600)55912-X (DE-576)029137179 0169-1864 nnns volume:29 year:2015 number:19 pages:1234 http://dx.doi.org/10.1080/01691864.2015.1057615 Volltext http://www.tandfonline.com/doi/abs/10.1080/01691864.2015.1057615 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2020 GBV_ILN_2244 50.25 AVZ AR 29 2015 19 1234 |
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Brain signal-based safety measure activation for robotic systems |
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Brain signal-based safety measure activation for robotic systems |
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Penaloza, Christian I |
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brain signal-based safety measure activation for robotic systems |
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Brain signal-based safety measure activation for robotic systems |
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In this paper, we present our approach for using EEG signals to activate safety measures of a robot when an error or unexpected event is perceived by the human operator. In particular, we consider brain-based error perception while the operator passively observes the robot performing an action. Our approach consists of monitoring EEG signals and detecting a brain potential called error related negativity (ERN) that spontaneously occurs when the operator perceives an error made by the robot or when an unexpected event occurs. We detect ERN by pre-training two linear classifiers using data collected from a preliminary experiment based on a visual reaction task. We derive the probability of failure in demand (PFD), commonly used to assess functional safety for a two-channel verification system based on the combination of linear classifiers. Functional safety analysis was then performed on a BMI-based robotic framework in which a signal was sent to the robot to active its safety measures in when an ERN was detected. Using brain-based signals, we demonstrate that it is possible to send an emergency stop action during mobile navigation task when unexpected events occur with an accuracy of 75%. |
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In this paper, we present our approach for using EEG signals to activate safety measures of a robot when an error or unexpected event is perceived by the human operator. In particular, we consider brain-based error perception while the operator passively observes the robot performing an action. Our approach consists of monitoring EEG signals and detecting a brain potential called error related negativity (ERN) that spontaneously occurs when the operator perceives an error made by the robot or when an unexpected event occurs. We detect ERN by pre-training two linear classifiers using data collected from a preliminary experiment based on a visual reaction task. We derive the probability of failure in demand (PFD), commonly used to assess functional safety for a two-channel verification system based on the combination of linear classifiers. Functional safety analysis was then performed on a BMI-based robotic framework in which a signal was sent to the robot to active its safety measures in when an ERN was detected. Using brain-based signals, we demonstrate that it is possible to send an emergency stop action during mobile navigation task when unexpected events occur with an accuracy of 75%. |
| abstract_unstemmed |
In this paper, we present our approach for using EEG signals to activate safety measures of a robot when an error or unexpected event is perceived by the human operator. In particular, we consider brain-based error perception while the operator passively observes the robot performing an action. Our approach consists of monitoring EEG signals and detecting a brain potential called error related negativity (ERN) that spontaneously occurs when the operator perceives an error made by the robot or when an unexpected event occurs. We detect ERN by pre-training two linear classifiers using data collected from a preliminary experiment based on a visual reaction task. We derive the probability of failure in demand (PFD), commonly used to assess functional safety for a two-channel verification system based on the combination of linear classifiers. Functional safety analysis was then performed on a BMI-based robotic framework in which a signal was sent to the robot to active its safety measures in when an ERN was detected. Using brain-based signals, we demonstrate that it is possible to send an emergency stop action during mobile navigation task when unexpected events occur with an accuracy of 75%. |
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Brain signal-based safety measure activation for robotic systems |
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http://dx.doi.org/10.1080/01691864.2015.1057615 http://www.tandfonline.com/doi/abs/10.1080/01691864.2015.1057615 |
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Mae, Yasushi Kojima, Masaru Arai, Tatsuo |
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