1 research outputs found
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 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 factor