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SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

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Hybrid Brain-Computer-Interfacing for Human-Compliant Robots: Inferring Continuous Subjective Ratings With Deep

Lukas D J Fiederer^1,2, Martin Völker^1,2,3, Robin T Schirrmeister^1,2

¹Neuromedical AI Lab, Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.

Frontiers in Neurorobotics

|October 26, 2019

Summary

This summary is machine-generated.

This study introduces a novel rating system for evaluating robot behavior during human-robot interaction using a hybrid brain-computer interface (BCI). Continuous user feedback allows for adaptive robot responses, enhancing assistive robotic systems.

Keywords:

BCI CNN autonomous robots deep learning random forests regression robot behavior support vector machines

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Area of Science:

Robotics
Human-Robot Interaction
Neuroscience
Machine Learning

Background:

Developing human-compliant assistive robots requires appropriate robot behavior during interaction.
Evaluating the quality of robotic behavior in real-time is crucial for adaptive systems.

Purpose of the Study:

To develop a continuous rating system for evaluating robot behavior in a hybrid brain-computer interfacing (BCI) task.
To use collected data to adapt robot behavior based on subjective user perception.

Main Methods:

A thumb-based wireless controller rating system compatible with dry electroencephalography (EEG) recordings was developed.
Continuous ratings were collected alongside EEG, respiration, electrocardiogram (ECG), and robotic joint angles.
Various regression techniques, including deep convolutional neural networks (CNNs), were employed to predict subjective ratings.

Main Results:

Feasible to obtain continuous rating data offering insights into subjective user perception during human-robot interaction.
Robot hand position was a better predictor of subjective ratings than EEG, ECG, or respiration.
Models learned a combination of general and user-specific features, with pre-trained regressors showing accuracy in experienced users.

Conclusions:

The developed rating system provides a viable method for assessing subjective user experience in human-robot interaction.
Robot behavior adaptation based on continuous feedback is achievable and can be enhanced by user experience levels.
Further studies with more participants are needed to validate the methodology for practical application.