2009 Special Issue: Subject-independent mental state classification in single trials

Authors:
Siamac Fazli;Florin Popescu;Márton Danóczy;Benjamin Blankertz;Klaus-Robert Müller;Cristian Grozea
Affiliations:
Fraunhofer First, Kekuléstr. 7, 12489 Berlin, Germany;Fraunhofer First, Kekuléstr. 7, 12489 Berlin, Germany;Fraunhofer First, Kekuléstr. 7, 12489 Berlin, Germany;Technical University, Franklinstr.28/29, 10587 Berlin, Germany;Technical University, Franklinstr.28/29, 10587 Berlin, Germany;Fraunhofer First, Kekuléstr. 7, 12489 Berlin, Germany
Venue:
Neural Networks
Year:
2009

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Abstract

Current state-of-the-art in Brain Computer Interfacing (BCI) involves tuning classifiers to subject-specific training data acquired from calibration sessions prior to functional BCI use. Using a large database of EEG recordings from 45 subjects, who took part in movement imagination task experiments, we construct an ensemble of classifiers derived from subject-specific temporal and spatial filters. The ensemble is then sparsified using quadratic regression with @?"1 regularization such that the final classifier generalizes reliably to data of subjects not included in the ensemble. Our offline results indicate that BCI-naive users could start real-time BCI use without any prior calibration at only very limited loss of performance.