title: Spatial filter adaptation based on the divergence framework for motor imagery EEG classification.
abstract: To address the nonstationarity issue in EEG-based brain computer interface (BCI), the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel adaptation approach based on the divergence framework. Cross-session changes can be taken into consideration by searching the discriminative subspaces for test data on the manifold of orthogonal matrices in a semi-supervised manner. Subsequently, the feature space becomes more consistent across sessions and classifiers performance can be enhanced. Experimental results show that the proposed adaptation method yields improvements in classification performance.
authors.lastname: Xinyang Li
authors.lastname: Cuntai Guan
authors.lastname: Kai Keng Ang
authors.lastname: Haihong Zhang
authors.lastname: Sim Heng Ong
mesh_terms: D000222:Adaptation, Physiological; D000465:Algorithms; D062207:Brain-Computer Interfaces; D002138:Calibration; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D019018:Imagery, Psychotherapy; D015233:Models, Statistical; D009048:Motor Skills / Q000502:physiology*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted