Next Article in Journal
Design and Analysis of Electromagnetic-Piezoelectric Hybrid Driven Three-Degree-of-Freedom Motor
Next Article in Special Issue
Improving the Accuracy of Low-Cost Sensor Measurements for Freezer Automation
Previous Article in Journal
Analysis of Machine Learning-Based Assessment for Elbow Spasticity Using Inertial Sensors
Previous Article in Special Issue
Towards IoT-Aided Human–Robot Interaction Using NEP and ROS: A Platform-Independent, Accessible and Distributed Approach
Article

Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback

1
Neurocognition and Functional Neurorehabilitation Group, Neuropsychology Lab, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany
2
Institute for Assistive Technologies, Jade University of Applied Science, 26389 Oldenburg, Germany
3
Neuropsychology Lab, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany
4
Cluster of Excellence Hearing4All, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany
5
Research Center Neurosensory Science, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany
6
Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1620; https://doi.org/10.3390/s20061620
Received: 7 February 2020 / Revised: 3 March 2020 / Accepted: 10 March 2020 / Published: 14 March 2020
(This article belongs to the Special Issue Human-Robot Interaction Applications in Internet of Things (IoT) Era)
Optimizing neurofeedback (NF) and brain–computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user’s control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8–30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants’ MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factors. View Full-Text
Keywords: BCI; mobile EEG; neurofeedback; robot; motor imagery; ERD/S; individual differences BCI; mobile EEG; neurofeedback; robot; motor imagery; ERD/S; individual differences
Show Figures

Figure 1

MDPI and ACS Style

Daeglau, M.; Wallhoff, F.; Debener, S.; Condro, I.S.; Kranczioch, C.; Zich, C. Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback. Sensors 2020, 20, 1620. https://doi.org/10.3390/s20061620

AMA Style

Daeglau M, Wallhoff F, Debener S, Condro IS, Kranczioch C, Zich C. Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback. Sensors. 2020; 20(6):1620. https://doi.org/10.3390/s20061620

Chicago/Turabian Style

Daeglau, Mareike, Frank Wallhoff, Stefan Debener, Ignatius S. Condro, Cornelia Kranczioch, and Catharina Zich. 2020. "Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback" Sensors 20, no. 6: 1620. https://doi.org/10.3390/s20061620

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop