Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
405600 | Neural Networks | 2009 | 8 Pages |
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 ℓ1ℓ1 regularization such that the final classifier generalizes reliably to data of subjects not included in the ensemble. Our offline results indicate that BCI-naïve users could start real-time BCI use without any prior calibration at only very limited loss of performance.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Siamac Fazli, Florin Popescu, Márton Danóczy, Benjamin Blankertz, Klaus-Robert Müller, Cristian Grozea,