Towards Enhancing Motor Imagery Based Brain-Computer Interface Performance by Integrating Speed of Imagined Movement
Left and right motor imagery tasks have commonly been utilized to construct a two-class Brain-computer Interface system, whilst the speed property of imagined movement has received less attention. In this study, we are trying to integrate the types and speed property of both imagined movement and real movement to further improve the performance of the two-class BCI system. Thus, real movement session and imagined movement session were carried out on the separated days. In real movement session, it has shown that 8 healthy volunteers have achieved an average accuracy of 67.62% with the same actual left and right hand clenching speed, and 78.62% with diverse speeds, which was a significant improvement (p=0.0176). Besides, only three subjects could pass the 70% accuracy threshold with same actual clenching speed, while six of them achieved to pass it with diverse speeds. In imagined movement session, all the subjects with diverse imagined clenching speed achieved a better control compared with same imagined speed. The proposed idea of integration of speed information has shown a promising benefit in two-class BCI construction in this preliminary study.
KeywordsBrain-computer interface motor imagery clenching speed ERD/ERS
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- 10.Yuan, H., Perdoni, C., He, B.: Decoding speed of imagined hand movement from eeg. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 142–145. IEEE (2010)Google Scholar
- 12.Allison, B.Z., Brunner, C., Kaiser, V., Müller-Putz, G.R., Neuper, C., Pfurtscheller, G.: Toward a hybrid brain–computer interface based on imagined movement and visual attention. Journal of Neural Engineering 7(2), 026007 (2010)Google Scholar
- 13.Yao, L., Meng, J., Zhang, D., Sheng, X., Zhu, X.: Selective sensation based brain-computer interface via mechanical vibrotactile stimulation. PLoS ONE 8(6), e64784 (2013)Google Scholar
- 14.Yao, L., Meng, J., Zhang, D., Sheng, X., Zhu, X.: Combining motor imagery with selective sensation towards a hybrid-modality bci. IEEE Transactions on Biomedical Engineering PP(99), 1 (2013)Google Scholar
- 18.Fukunaga, K.: Introduction to statistical pattern recognition, 2nd edn., pp. 1–2. Academic Press (1990)Google Scholar