Abstract
Brain-computer interfacing (BCI) is a bridging technology between a human brain and a device that enables signals from the brain to direct some external activity, such as control of a cursor or a prosthetic limb. In practice, brain signals are captured by the popular EEG technique and then the scalp voltage level is transferred into corresponding cursor movements. In multi-target based BCI, the set of targets are assigned to the different clusters initially and then the cursor is mapped to the nearest cluster using clustering technique. Finally, the cursor hits all the targets sequentially inside its own cluster. In this work, the famous CLIQUE clustering technique is chosen to assign the cursor into a proper cluster and if the cursor movement will be optimum in time, then the disabled persons can communicate efficiently. CLIQUE clustering is an integration of density based and grid based Clustering methods which is used to measure the cursor movement as bit transfer rate in a cell within the grid. This technique will lead us to improve the performance of the BCI system in terms of multi-targets search.
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References
Xia, B., Yang, J., Cheng, C., Xie, H.: A motor imagery based brain—computer interface speller. In: 2013 Advances in Computational Intelligence, pp. 413–421. Springer, Berlin (2013)
Donchin, E., Spencer, K.M., Wijesinghe, R.: The mental prosthesis: assessing the speed of a p300-based brain—computer interface. IEEE Trans. Rehabil. Eng. 8, 174–179 (2000)
Huang, D., Qian, K., Fei, D.-Y., Jia, W., Chen, X., Bai, O.: Electroencephalography (EEG)-based brain-computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control. IEEE Trans Neural Syst. Rehab. Eng. 20, 379–388 (2012)
Li, J., Liang, J., Zhao, Q., Li, J., Hong, K., Zhang, L.: Design of assistive wheelchair system directly steered by human thoughts. Int. J. Neural Syst. 23, 1350013 (2013)
Li, J., Ji, H., Cao, L., Zang, D., Gu, R., Xia, B., Wu, Q.: Evaluation and application of a hybrid brain computer interface for real wheelchair parallel control with multi-degree of freedom. Int. J. Neural Syst. 24, 1450014 (2014)
Chakladar, D.D., and Chakraborty, S.: Study and analysis of a fast moving cursor control in a multithreaded way in brain computer interface. In: International Conference on Computational Intelligence, Communications, and Business Analytics, pp. 44–56. Springer, Singapore, March 2017
Fabiani, G.E., McFarland, D.J., Wolpaw, J.R., Pfurtscheller, G.: Conversion of EEG activity into cursor movement by a brain-computer interface (BCI). IEEE Trans. Neural Syst. Rehabil. Eng. 12(3), 331–338 (2004)
Xia, B., Maysam, O., Veser, S., Cao, L., Li, J., Jia, J., Xie, H., Birbaumer, N.: A combination strategy based brain—computer interface for two-dimensional movement control. J. Neural Eng. 12(4), 046021 (2015)
Ortiz-Rosario, A., Adeli, H.: Brain-computer interface technologies: from signal to action. Rev. Neurosci. 24(5), 537–552 (2013)
Lotte, F., Congedo, M., Lecuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain—computer interfaces. J. Neural Eng. 4(2), R1 (2007)
Steinbach, M., Karypis, G., Kumar, V.: A comparison of document clustering techniques. KDD Workshop Text Min. 400(1), 525–526 (2000)
Tajunisha, S., Saravanan, V.: Performance analysis of k-means with different initialization methods for high dimensional datasets. Int. J. Artif. Intell. Appl. (IJAIA) 1(4), 44–52 (2010)
Schikuta, E.: Grid-clustering: an efficient hierarchical clustering method for very large data sets. In: Proceedings of the 13th International Conference on Pattern Recognition, 1996, vol. 2, pp. 101–105. IEEE (1996)
Andrade, G., Ramos, G., Madeira, D., Sachetto, R., Ferreira, R., Rocha, L.: G-DBSCAN: a GPU accelerated algorithm for density-based clustering. Procedia Comput. Sci. 18, 369–378 (2013)
Aho, A.V., Hopcroft, J.E., Ullman, J.D.: The Design and Analysis of Computer Algorithms. Addison-Wesley, Reading, MA (1974)
Cao, F., Martin E., Weining Q., Aoying Z.: Density-based clustering over an evolving data stream with noise. In: Proceedings of the 2006 SIAM International Conference on Data Mining, pp. 328–339. Society for Industrial and Applied Mathematics (2006)
Wolpaw, J.R., McFarland, D.J., Vaughan, T.M.: Brain-computer interface research at the wadsworth center. IEEE Trans. Rehabil. Eng. 8(2), 222–226 (2012)
Wolpaw, J.R., McFarland, D.J., Neat, G.W., Forneris, C.A.: An EEG-based brain-computer interface for cursor control. Electroencephalogr. Clin. Neurophysiol. 78, 252–259 (1991)
Yadav, J., Kumar, D.: Sub space Clustering using CLIQUE: an exploratory study. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 3 (2014)
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Saurav, S., Chakladar, D.D., Shaw, P., Chakraborty, S., Kairi, A. (2019). Multi-target-Based Cursor Movement in Brain-Computer Interface Using CLIQUE Clustering. In: Chakraborty, M., Chakrabarti, S., Balas, V., Mandal, J. (eds) Proceedings of International Ethical Hacking Conference 2018. Advances in Intelligent Systems and Computing, vol 811. Springer, Singapore. https://doi.org/10.1007/978-981-13-1544-2_34
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DOI: https://doi.org/10.1007/978-981-13-1544-2_34
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