title: On the applicability of brain reading for predictive human-machine interfaces in robotics.
abstract: The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.
authors.affiliation: Robotics Lab, University of Bremen, Bremen, Germany ; Robotics Innovation Center (RIC), German Research Center for Artificial Intelligence (DFKI), Bremen, Germany.
authors.forename: Elsa Andrea
authors.initials: EA
authors.lastname: Kirchner
authors.affiliation: Robotics Lab, University of Bremen, Bremen, Germany ; Robotics Innovation Center (RIC), German Research Center for Artificial Intelligence (DFKI), Bremen, Germany.
authors.forename: Su Kyoung
authors.initials: SK
authors.lastname: Kim
authors.affiliation: Robotics Lab, University of Bremen, Bremen, Germany.
authors.forename: Sirko
authors.initials: S
authors.lastname: Straube
authors.affiliation: Robotics Innovation Center (RIC), German Research Center for Artificial Intelligence (DFKI), Bremen, Germany.
authors.forename: Anett
authors.initials: A
authors.lastname: Seeland
authors.affiliation: Robotics Innovation Center (RIC), German Research Center for Artificial Intelligence (DFKI), Bremen, Germany.
authors.forename: Hendrik
authors.initials: H
authors.lastname: Wöhrle
authors.affiliation: Robotics Lab, University of Bremen, Bremen, Germany.
authors.forename: Mario Michael
authors.initials: MM
authors.lastname: Krell
authors.affiliation: Robotics Lab, University of Bremen, Bremen, Germany.
authors.forename: Marc
authors.initials: M
authors.lastname: Tabie
authors.affiliation: Brain Research Institute IV, University of Bremen, Bremen, Germany ; The Henry Wellcome Laboratories for Vision Sciences, City University, London, United Kingdom.
authors.forename: Manfred
authors.initials: M
authors.lastname: Fahle
mesh_terms: D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D004569:Electroencephalography; D006801:Humans; D012371:Robotics; D014584:User-Computer Interface