Correcting robot mistakes in real time using EEG signals
Name
Correcting_Robot_Mistakes_in_Real_Time_Using_EEG_Signals.pdf
Description
Accepted version
Size
5.41 MB
Format
Adobe PDF
Checksum (MD5)
9cb331df7d5d4fbeb853236177e7b002
Author(s)
Salazar-Gomez, Andres F.
DelPreto, Joseph
Gil, Stephanie
Guenther, Frank H.
Rus, Daniela
Date Issued
May 2017
Publisher
IEEE
Citation
Salazar-Gomez, Andres F., DelPreto, Joseph, Gil, Stephanie, Guenther, Frank H. and Rus, Daniela. 2017. "Correcting robot mistakes in real time using EEG signals."
Version
Author's final manuscript
Abstract
© 2017 IEEE. 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2017.7989777
