Transferring BCI models from calibration to control: Observing shifts in
  EEG features
Ivo Pascal de Jong Lüke Luna van den Wittenboer Matias Valdenegro-Toro Andreea Ioana Sburlea
Public Motor Imagery-based brain-computer interface (BCI) datasets are being
used to develop increasingly good classifiers. However, they usually follow
discrete paradigms where participants perform Motor Imagery at regularly timed
intervals. It is often unclear what changes may happen in the EEG patterns when
users attempt to perform a control task with such a BCI. This may lead to
generalisation errors. We demonstrate a new paradigm containing a standard
calibration session and a novel BCI control session based on EMG. This allows
us to observe similarities in sensorimotor rhythms, and observe the additional
preparation effects introduced by the control paradigm. In the Movement Related
Cortical Potentials we found large differences between the calibration and
control sessions. We demonstrate a CSP-based Machine Learning model trained on
the calibration data that can make surprisingly good predictions on the
BCI-controlled driving data.