title: Adaptive Stacked Generalization for Multiclass Motor Imagery-Based Brain Computer Interfaces.
abstract: Practical motor imagery-based brain computer interface (MI-BCI) applications are limited by the difficult to decode brain signals in a reliable way. In this paper, we propose a processing framework to address non-stationarity, as well as handle spectral, temporal, and spatial characteristics associated with execution of motor tasks. Stacked generalization is used to exploit the power of classifier ensembles for combining information coming from multiple sources and reducing the existing uncertainty in EEG signals. The outputs of several regularized linear discriminant analysis (RLDA) models are combined to account for temporal, spatial, and spectral information. The resultant algorithm is called stacked RLDA (SRLDA). Additionally, an adaptive processing stage is introduced before classification to reduce the harmful effect of intersession non-stationarity. The benefits of the proposed method are evaluated on the BCI Competition IV dataset 2a. We demonstrate its effectiveness in binary and multiclass settings with four different motor imagery tasks: left-hand, right-hand, both feet, and tongue movements. The results show that adaptive SRLDA outperforms the winner of the competition and other approaches tested on this multiclass dataset.
authors: Luis F
authors: LF
authors: Nicolas-Alonso
authors: Rebeca
authors: R
authors: Corralejo
authors: Javier
authors: J
authors: Gomez-Pilar
authors: Daniel
authors: D
authors: Álvarez
authors: Roberto
authors: R
authors: Hornero
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology; D062207:Brain-Computer Interfaces; D016002:Discriminant Analysis; D004569:Electroencephalography; D005528:Foot / Q000502:physiology; D007839:Functional Laterality / Q000502:physiology; D006225:Hand / Q000502:physiology; D006801:Humans; D007092:Imagination / Q000502:physiology*; D009068:Movement; D014059:Tongue / Q000502:physiology