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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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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