Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M., et al.: Brain-computer interfaces for communication and control. Clinical neurophysiology 113(6), 767–791 (2002)
Millan, J.R., Renkens, F., Mourino, J., Gerstner, W.: Noninvasive brain-actuated control of a mobile robot by human eeg. IEEE Transactions on Biomedical Engineering 51(6), 1026–1033 (2004)
Pfurtscheller, G., Müller, G.R., Pfurtscheller, J., Gerner, H.J., Rupp, R.: Thought – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neuroscience Letters 351(1), 33–36 (2003)
Pfurtscheller, G., Neuper, C., Flotzinger, D., Pregenzer, M.: Eeg-based discrimination between imagination of right and left hand movement. Electroencephalography and Clinical Neurophysiology 103(6), 642–651 (1997)
Pfurtscheller, G., Neuper, C., Brunner, C., Lopes da Silva, F.: et al. Beta rebound after different types of motor imagery in man. Neuroscience Letters 378(3), 156 (2005)
Pfurtscheller, G., Lopes da Silva, F.H.: Event-related eeg/meg synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110, 1842–1857 (1999)
Jerbi, K., Vidal, J.R., Mattout, J., Lecaignard, F., Ossandon, T., Hamame, C.M., Dalal, S.S., Bouet, R., Lachaux, J.-P.: Inferring hand movement kinematics frommeg, eeg and intracranial eeg: From brainmachine interfaces to motor rehabilitation. IRBM 32, 8–18 (2011)
Robinson, N., Vinod, A.P., Kai, K.A., Keng, P.T., Guan, C.T.: Eeg-based classification of fast and slow hand movements using wavelet-csp algorithm. IEEE Transactions on Biomedical Engineering 60(8), 2123–2132 (2013)
Yuan, H., Perdoni, C., He, B.: Relationship between speed and eeg activity during imagined and executed hand movements. J. Neural. Eng. 7, 26001 (2010)
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)
Guger, C., Daban, S., Sellers, E., Holzner, C., Krausz, G., Carabalona, R., Gramatica, F., Edlinger, G.: How many people are able to control a p300-based brain? computer interface (bci)? Neuroscience Letters 462(1), 94–98 (2009)
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)
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)
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)
Schupp, H.T., Lutzenberger, W., Birbaumer, N., Miltner, W., Braun, C.: Neurophysiological differences between perception and imagery. Cogn. Brain Res. 2, 77–86 (1994)
Pfurtscheller, G., Neuper, C.C.: Motor imagery activates primary sensorimotor area in humans. Neurosci. Lett. 236, 65–68 (1997)
McFarland, D., Miner, L., Vaughan, T., Wolpaw, J.: Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topography 12(3), 177–186 (2000)
Fukunaga, K.: Introduction to statistical pattern recognition, 2nd edn., pp. 1–2. Academic Press (1990)
Ramoser, H., Muller-Gerking, J., Pfurtscheller, G.: Optimal spatial filtering of single trial eeg during imagined hand movement. IEEE Transactions on Rehabilitation Engineering 8(4), 441–446 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Xie, T., Yao, L., Sheng, X., Zhang, D., Zhu, X. (2014). Towards Enhancing Motor Imagery Based Brain-Computer Interface Performance by Integrating Speed of Imagined Movement. In: Zhang, X., Liu, H., Chen, Z., Wang, N. (eds) Intelligent Robotics and Applications. ICIRA 2014. Lecture Notes in Computer Science(), vol 8917. Springer, Cham. https://doi.org/10.1007/978-3-319-13966-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-13966-1_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13965-4
Online ISBN: 978-3-319-13966-1
eBook Packages: Computer ScienceComputer Science (R0)