Pradeep Shenoy et al 2006 J. Neural Eng. 3 R13 doi:10.1088/1741-2560/3/1/R02
Pradeep Shenoy1,2, Matthias Krauledat2,3, Benjamin Blankertz2, Rajesh P N Rao1 and Klaus-Robert Müller2,3
Show affiliationsNon-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain–computer interfaces (BCIs): (a) in the differences between the 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 evidence 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 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.
87.85.Ng Biological signal processing
Issue 1 (March 2006)
Received 19 October 2005, accepted for publication 27 January 2006
Published 1 March 2006
Pradeep Shenoy et al 2006 J. Neural Eng. 3 R13
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