PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Towards Adaptive Classification for BCI
Pradeep Shenoy, Matthias Krauledat, Benjamin Blankertz, Rajesh P. N. Rao and Klaus-Robert Müller
Journal of Neural Engineering Volume 3, R13-R23, 2006.

Abstract

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:1346
Deposited By:Benjamin Blankertz
Deposited On:28 November 2005