Abstract
Brain Computer Interface (BCI) is a type of human-computer relationship research that directly translates electrical activity of brain into commands that can rule equipment and create novel communication channel for muscular disabled patients. In this study, in order to overcome shortcoming of Singular Value Decomposition in Extreme Learning Machine, iteratively optimized neuron numbered QR Decomposition technique with different approaches are proposed. QR Decomposition Extreme Learning Machine technique based P300 event-related potential BCI application that achieves almost % 100 classification accuracy with milliseconds is presented. QR decomposition based ELM and novel feature extraction method named Multi Order Difference Plot (MoDP) techniques are milestones of proposed BCI system.
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Kutlu, Y., Yayik, A., Yildirim, E., Yildirim, S. (2015). Orthogonal Extreme Learning Machine Based P300 Visual Event-Related BCI. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_33
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DOI: https://doi.org/10.1007/978-3-319-26535-3_33
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