ArticleFrontiers in human neuroscience2016
An Intelligent Man-Machine Interface-Multi-Robot Control Adapted for Task Engagement Based on Single-Trial Detectability of P300.
Article in Frontiers in human neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy.Brain sciences · 2025Review
- An Adaptive Human-Robotic Interaction Architecture for Augmenting Surgery Performance Using Real-Time Workload Sensing-Demonstration of a Semi-autonomous Suction Tool.Human factors · 2024Article
- EEG and EMG dataset for the detection of errors introduced by an active orthosis device.Frontiers in human neuroscience · 2024Article
- Multi-Tasking and Choice of Training Data Influencing Parietal ERP Expression and Single-Trial Detection-Relevance for Neuroscience and Clinical Applications.Frontiers in neuroscience · 2018Article
- Intrinsic interactive reinforcement learning - Using error-related potentials for real world human-robot interaction.Scientific reports · 2017Article
- A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction.Sensors (Basel, Switzerland) · 2017Article
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6 authors.
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Abstract
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.
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