An Intelligent Man-Machine Interface-Multi-Robot Control Adapted for Task Engagement Based on Single-Trial Detectability of P300

• Published in: Frontiers in human neuroscience 2016-06, Vol.10, p.291-291
• Main Authors: Kirchner, Elsa A, Kim, Su K, Tabie, Marc, Wöhrle, Hendrik, Maurus, Michael, Kirchner, Frank
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Auditory evoked potentials; Brain research; Cognitive ability; Computer science; Electroencephalography; Event-related potentials; Human performance; Informatics; Interfaces; International conferences; Internet; Language; Learning algorithms; Neuroscience; Physiology; Reaction time task; Reading; Robotics; Robots; Signal processing; Target recognition; Workloads
• ISSN: 1662-5161; 1662-5161
• DOI: 10.3389/fnhum.2016.00291

Advanced man-machine interfaces (MMIs) are being developed for teleoperating robots at remote and hardly accessible places. Such MMIs make use of a virtual environment and can therefore make the operator immerse him-/herself into the environment of the robot. In this paper, we present our developed MMI for multi-robot control. Our MMI can adapt to changes in task load and task engagement online. Applying our approach of embedded Brain Reading we improve user support and efficiency of interaction. The level of task engagement was inferred from the single-trial detectability of P300-related brain activity that was naturally evoked during interaction. With our approach no secondary task is needed to measure task load. It is based on research results on the single-stimulus paradigm, distribution of brain resources and its effect on the P300 event-related component. It further considers effects of the modulation caused by a delayed reaction time on the P300 component evoked by complex responses to task-relevant messages. We prove our concept using single-trial based machine learning analysis, analysis of averaged event-related potentials and behavioral analysis. As main results we show (1) a significant improvement of runtime needed to perform the interaction tasks compared to a setting in which all subjects could easily perform the tasks. We show that (2) the single-trial detectability of the event-related potential P300 can be used to measure the changes in task load and task engagement during complex interaction while also being sensitive to the level of experience of the operator and (3) can be used to adapt the MMI individually to the different needs of users without increasing total workload. Our online adaptation of the proposed MMI is based on a continuous supervision of the operator's cognitive resources by means of embedded Brain Reading. Operators with different qualifications or capabilities receive only as many tasks as they can perform to avoid mental overload as well as mental underload.