The objective of the present study was to clarify the variation in and properties of mental images and policies used to regulate specific image selection when learning to control a brain-computer interface. Healthy volunteers performed a reaching task with a virtually generated monkey tail-like object on a computer monitor by regulating event-related desynchronization (ERD) on the buttock area of the sensorimotor cortex as recorded by electroencephalogram (EEG). Participants were instructed to find a free image by which the tail was well controlled. Seven participants frequently returned to specific images that were mostly unrelated to a tail, and returned to these images on the last day of training. The ERD levels were greater during use of those selected images versus when selected images were not employed. Our results suggest that individuals adopted a mental strategy where they imagine what would reduce the prediction error between the predicted and actual BCI actions.
Keywords: Neurofeedback; de novo learning; electroencephalogram; event-related desynchronization.