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Channel selection using glow swarm optimization and its application in line of sight secure communication

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Abstract

The brain computer interfaces (BCI), that are also known as brain machine interfaces or sometimes neural interface systems have a pathway of direct communication between a device and the brain. In BCI, certain devices acquire electrical signals from the brain like Electroencephalography (EEG) rhythm and further translate them in accordance to the user given commands. The acquired signals are translated by the BCI to meaningful commands. Common spatial pattern filters’ discrimination and channels’ number are incorporated for the search of the optimal channel group which is a non-deterministic polynomial (NP) problem. In this work, glow swarm optimization algorithm is used for solving the NP problem of selection of the electrodes that are optimal and that can improve the classification. Experiments were conducted for evaluating the proposed method. And the results of the experiments prove the proposed approach’s performance. The channel optimized-Naïve Bayes (NB) achieves the best classification accurateness compared to k nearest neighbor (kNN), NB and channel optimized-kNN.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, St. Joseph’s College of Engineering and Technology, Thanjavur, India

    A. Franklin Alex Joseph

  2. Department of Electrical and Electronics Engineering, Government College of Engineering, Salem, India

    C. Govindaraju

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  1. A. Franklin Alex Joseph
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  2. C. Govindaraju
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Correspondence to A. Franklin Alex Joseph.

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Franklin Alex Joseph, A., Govindaraju, C. Channel selection using glow swarm optimization and its application in line of sight secure communication. Cluster Comput 22 (Suppl 5), 10801–10808 (2019). https://doi.org/10.1007/s10586-017-1177-9

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  • DOI: https://doi.org/10.1007/s10586-017-1177-9

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