EEG-Based Online Regulation of Difficulty in Simulated Flying

Adaptively increasing the difficulty level in learning was shown to be beneficial than increasing the level after some fixed time intervals. To efficiently adapt the level, we aimed at decoding the subjective difficulty level based on Electroencephalography (EEG) signals. We designed a visuomotor learning task that one needed to pilot a simulated drone through a series of waypoints of different sizes, to investigate the effectiveness of the EEG decoder. The EEG decoder was compared with another condition that the subjects decided when to increase the difficulty level. We examined the decoding performance together with behavioral outcomes. The online accuracies were higher than the chance level for 16 out of 26 cases, and the behavioral results, such as task scores, skill curves, and learning patterns, of EEG condition were similar to the condition based on manual regulation of difficulty.