LAUSR

Existing studies have demonstrated that eye tracking can be a complementary approach to Electroencephalogram (EEG) based brain-computer interaction (BCI), especially in improving BCI performance in visual perception and cognition. In this paper, we proposed a method to fuse EEG and eye movement data extracted from motor imagery (MI) tasks. The results of the tests showed that on the feature layer, the average MI classification accuracy from the fusion of EEG and eye movement data was higher than that of pure EEG data or pure eye movement data, respectively. Besides, we also found that the average classification accuracy from the fusion on the decision layer was higher than that from the feature layer. Additionally, when EEG data were not available for the shifting of parts of electrodes, we combined EEG data collected from the rest of the electrodes (only 50% of the original) with the eye movement data, and the average MI classification accuracy was only 1.07% lower than that from all available electrodes. This result indicated that eye movement data was feasible to compensate for the loss of the EEG data in the MI scenario. Overall our approach was proved valuable and useful for augmenting MI based BCI applications.