title: Subject-independent mental state classification in single trials.
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 l(1) 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.
authors: Fraunhofer First, Kekuléstr. 7, 12489 Berlin, Germany. fazli@first.fraunhofer.de
authors: Siamac
authors: S
authors: Fazli
authors: Florin
authors: F
authors: Popescu
authors: Márton
authors: M
authors: Danóczy
authors: Benjamin
authors: B
authors: Blankertz
authors: Klaus-Robert
authors: KR
authors: Müller
authors: Cristian
authors: C
authors: Grozea
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D002138:Calibration; D019992:Databases as Topic; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008606:Mental Processes / Q000502:physiology*; D012044:Regression Analysis; D015203:Reproducibility of Results; D013997:Time Factors; D014584:User-Computer Interface