Variability and Nonstationarity in Brain Computer Interfaces
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2009-10-15
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en
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Abstract
A well-known problem in Brain Computer Interfacing (BCI) research is the large degree of
variability in the acquired data. Every user has different brain signals, and also the performance
of a user varies widely between sessions, within a session, between online and offline
settings, and even from epoch to epoch. This variation can be caused by many factors such as
psychological factors (e.g., fatigue, mood), physiological factors, change of task involvement
(offline versus online task), learning, or measurement artifacts and noise. For maintaining
good BCI performance, a BCI must be able to adapt to these changes.
In our research we attempt to characterize these variability types in a BCI context. Therefore,
we have conducted an EEG imagined movement experiment with 8 participants taking
part in two sessions. Participants received two types of feedback, namely continuous
during movement imagination, or once immediately after movement imagination. The performance
of the BCI and some simple bias and gain adaptation methods for improving the
feedback were analyzed.
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Faculteit der Sociale Wetenschappen