Abstract
In recent years motor imagery-based brain–computer interface (MI-BCI) is widely used in the rehabilitation of stroke patients and received certain therapeutic effect. The existing imagery mode of brain-computer interface focuses more on the aspect of pure motor imagery and less on the experimental ways of combining other motion and imagination. In this paper, aiming at studying the role of attention in the context of classifying covert and overt motor activities, we design different experiments to explore it in different modes. In our experiments, covert activities are only motor imagery. Overt motor activities are divided into two types—attention to the screen and attention to intended hand. The classification accuracy of six subjects in three modes are compared and analyzed. The average accuracy of overt motor activities with attention to intended hand is the highest, which are respectively 3% and 5% higher than those of covert activities and overt motor activities with attention to screen. At the same time, overt motor activities with attention to intended hand induce more active brain areas according to the spatial pattern of the corresponding EEG data.
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Yang, B., Wang, J., Guan, C., Hu, C., Wang, J. (2018). A Study of the Role of Attention in Classifying Covert and Overt Motor Activities. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_15
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