TY - GEN
T1 - Correcting robot mistakes in real time using EEG signals
AU - Salazar-Gomez, Andres F.
AU - Delpreto, Joseph
AU - Gil, Stephanie
AU - Guenther, Frank H.
AU - Rus, Daniela
N1 - Funding Information:
This work was funded in part by the Boeing Company, the NSF Graduate Research Fellowship number 1122374, and CELEST, a NSF Science of Learning Center (NSF SMA-0835976), for which the authors express thanks. Gratitude is also expressed towards Bianca Homberg, whose time and effort while laying a foundation for the Baxter interface are much appreciated.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/21
Y1 - 2017/7/21
N2 - Communication with a robot using brain activity from a human collaborator could provide a direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide variety of intuitive interaction tasks. This paper explores the application of EEG-measured error-related potentials (ErrPs) to closed-loop robotic control. ErrP signals are particularly useful for robotics tasks because they are naturally occurring within the brain in response to an unexpected error. We decode ErrP signals from a human operator in real time to control a Rethink Robotics Baxter robot during a binary object selection task. We also show that utilizing a secondary interactive error-related potential signal generated during this closed-loop robot task can greatly improve classification performance, suggesting new ways in which robots can acquire human feedback. The design and implementation of the complete system is described, and results are presented for realtime closed-loop and open-loop experiments as well as offline analysis of both primary and secondary ErrP signals. These experiments are performed using general population subjects that have not been trained or screened. This work thereby demonstrates the potential for EEG-based feedback methods to facilitate seamless robotic control, and moves closer towards the goal of real-time intuitive interaction.
AB - Communication with a robot using brain activity from a human collaborator could provide a direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide variety of intuitive interaction tasks. This paper explores the application of EEG-measured error-related potentials (ErrPs) to closed-loop robotic control. ErrP signals are particularly useful for robotics tasks because they are naturally occurring within the brain in response to an unexpected error. We decode ErrP signals from a human operator in real time to control a Rethink Robotics Baxter robot during a binary object selection task. We also show that utilizing a secondary interactive error-related potential signal generated during this closed-loop robot task can greatly improve classification performance, suggesting new ways in which robots can acquire human feedback. The design and implementation of the complete system is described, and results are presented for realtime closed-loop and open-loop experiments as well as offline analysis of both primary and secondary ErrP signals. These experiments are performed using general population subjects that have not been trained or screened. This work thereby demonstrates the potential for EEG-based feedback methods to facilitate seamless robotic control, and moves closer towards the goal of real-time intuitive interaction.
UR - http://www.scopus.com/inward/record.url?scp=85028018231&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028018231&partnerID=8YFLogxK
U2 - 10.1109/ICRA.2017.7989777
DO - 10.1109/ICRA.2017.7989777
M3 - Conference contribution
AN - SCOPUS:85028018231
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 6570
EP - 6577
BT - ICRA 2017 - IEEE International Conference on Robotics and Automation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Y2 - 29 May 2017 through 3 June 2017
ER -