Abstract
Neurofeedback training teaches individuals to modulate brain activity by providing real-time feedback and can be used for brain–computer interface control. The present study aimed to optimize training by maximizing engagement through goal-oriented task design. Participants were shown either a visual display or a robot, where each was manipulated using motor imagery (MI)-related electroencephalography signals. Those with the robot were instructed to quickly navigate grid spaces, as the potential for goal-oriented design to strengthen learning was central to our investigation. Both groups were hypothesized to show increased magnitude of these signals across 10 sessions, with the greatest gains being seen in those navigating the robot due to increased engagement. Participants demonstrated the predicted increase in magnitude, with no differentiation between hemispheres. Participants navigating the robot showed stronger left-hand MI increases than those with the computer display. This is likely due to success being reliant on maintaining strong MI-related signals. While older participants showed stronger signals in early sessions, this trend later reversed, suggesting greater natural proficiency but reduced flexibility. These results demonstrate capacity for modulating neurofeedback using MI over a series of training sessions, using tasks of varied design. Importantly, the more goal-oriented robot control task resulted in greater improvements.



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Acknowledgements
We are grateful to the following people for their assistance in this study: Jack P. Solomon, Christopher Friesen, Gloria Kamba, Jeremy Huard, and Derek Rodgers. This work was supported by the Brain Repair Centre.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
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McWhinney, S.R., Tremblay, A., Boe, S.G. et al. The impact of goal-oriented task design on neurofeedback learning for brain–computer interface control. Med Biol Eng Comput 56, 201–210 (2018). https://doi.org/10.1007/s11517-017-1683-1
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DOI: https://doi.org/10.1007/s11517-017-1683-1