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

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 measu...

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Published in:Sensors (Basel, Switzerland) Switzerland), 2020-03, Vol.20 (6), p.1620
Main Authors: Daeglau, Mareike, Wallhoff, Frank, Debener, Stefan, Condro, Ignatius Sapto, Kranczioch, Cornelia, Zich, Catharina
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cited_by cdi_FETCH-LOGICAL-c469t-705d94113140b1e3d4ed8c1645f0804489687d52c71ba85c8772113bdc5acf923
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container_title Sensors (Basel, Switzerland)
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creator Daeglau, Mareike
Wallhoff, Frank
Debener, Stefan
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Kranczioch, Cornelia
Zich, Catharina
description 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.
doi_str_mv 10.3390/s20061620
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subjects Adult
bci
Biofeedback
Brain-Computer Interfaces
Classification
Collaboration
Competition
Electroencephalography
Electroencephalography - methods
erd/s
Experiments
Feasibility studies
Female
Human-computer interface
Humanoid
Humans
Imagery
Imagery, Psychotherapy - methods
individual differences
Male
mobile eeg
Motivation
motor imagery
neurofeedback
Neurofeedback - methods
Paradigms
Preferences
robot
Robotics - trends
Robots
Sensorimotor Cortex - physiology
Signal processing
Signal Processing, Computer-Assisted
Young Adult
title Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback
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