Towards Neuroadaptive Augmented Reality Piano Tutorials

EEG-based neuro-adaptive systems are becoming increasingly popular in the field of learning and education. Their ability to detect changes in brain activity in real-time makes them unique and enables monitoring mental workload (MWL) and fatigue. We present a neuro-adaptive system that combines an Augmented Reality (AR) piano tutorial with online EEG measurements of its user, delivered by a passive Brain Computer Interface (BCI) system. The MWL was measured by means of EEG and a Filter Bank Common Spatial Patter algorithm (FBCSP) was trained to differentiate between low and high levels of MWL. Low levels were connected to the 0-back task and high levels to the 2-back task. The n-back task was a calibration task to train a binary Machine Learning (ML) classifier to differentiate between low and high MWL. This trained ML algorithm was then used as the central element of the passive BCI, which constantly classified small windows of the EEG during the piano tutorial and adapted the difficulty of the tutorial. 22 Participants were randomly separated into two groups: adaptive and non-adaptive piano tutorial. The results of the non-adaptive group showed significantly higher levels of classified MWL throughout the piano tutorial.