title: Towards adaptive classification for BCI.
abstract: Non-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.
authors.affiliation: Computer Science Department, University of Washington, Box 352350, Seattle, WA 98195, USA.
authors.forename: Pradeep
authors.initials: P
authors.lastname: Shenoy
authors.forename: Matthias
authors.initials: M
authors.lastname: Krauledat
authors.forename: Benjamin
authors.initials: B
authors.lastname: Blankertz
authors.forename: Rajesh P N
authors.initials: RP
authors.lastname: Rao
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D004562:Electrocardiography / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012189:Retrospective Studies; D014584:User-Computer Interface