Adaptive Stacked Generalization for Multiclass Motor Imagery-Based Brain Computer Interfaces

作者：Nicolas Alonso Luis F^*; Corralejo Rebeca; Gomez Pilar Javier; Alvarez Daniel; Hornero Roberto

来源：IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015, 23(4): 702-712.

DOI：10.1109/TNSRE.2015.2398573

• Adaptive estimation
• brain computer interfaces
• classifier ensembles
• common spatial pattern
• electroencephalography
• linear discriminant analysis
• stacked generalization

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.

• 出版日期2015-7