Riemannian geometry metric-based visual feedback for BCI user training: towards exploratory learning of motor imagery

Objective Despite their many emerging applications, practical use of brain–computer interfaces (BCIs) is often impeded by BCI-inefficiency, that is, the failure of the technology to decode neural modulations with sufficient accuracy. Recent evidence suggests that ineffective user training, namely feeding back to the user performance metrics that do not relate to the future performance of the BCI, may be obstructing users from learning how to produce machine-discernible sensorimotor rhythm modulations. Here, we use models of human skill acquisition to design a user-training interface to address these challenges.