Towards Adaptive Classification for BCI
Pradeep Shenoy, Matthias Krauledat, Benjamin Blankertz, Rajesh P. N. Rao and Klaus-Robert Müller
Journal of Neural Engineering
Non-stationarities are ubiquitous in EEG signals. They are especially
apparent in the use of EEG-based Brain-Computer Interfaces (BCI): (a)
in the differences between initial calibration measurement and the
online operation of a BCI, or (b) caused by changes in the subject's
brain processes during an experiment (e.g. due to fatigue, change of
task involvement etc.).
In this paper we quantify for the first time such systematic evidences
of statistical differences in data recorded during offline and online
sessions. Furthermore we propose novel techniques of investigating and
visualizing data distributions which are particularly useful for the
analysis of (non)stationarities.
Our study shows that the brain signals used for control % what data?
can change substantially from the offline calibration sessions to
online control, and also within a single session.
In addition to this general characterization of the signals, we
propose several adaptive classification schemes and study their
performance on data recorded during online experiments.
An encouraging result of our study is that surprisingly simple
adaptive methods in combination with an offline feature selection
scheme, can significantly increase BCI performance.