Abstract
Motor Imagery handles the brain activity patterns of motor action without explicit movements. For extracting the discriminating features, Common Spatial Patterns are the most widely used algorithm that is very sensitive to artifacts and prone to overfitting. Here, we develop a metric to assess the relevance of Common Spatial Patterns using a mapping through Kernel Principal Component Analysis with the benefit of improved interpretation that allows evaluating the zones, which contribute the most to the motor imagery classification accuracy. Validation is carried out on a real-world database, appraising two labels of Motor Imagery activity. From the obtained results, we prove that the developed approach allows the performance enhancement, at the time, the relevant set decreases the number of channels to feed the classifier, and thus reducing the computational cost.
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Acknowledgments
This research was supported by Doctorados Nacionales, conv.727 funded by COLCIENCIAS, and the research project “Programa reconstrucción del tejido social en zonas de pos-conflicto en Colombia del proyecto Fortalecimiento docente desde la alfabetización mediática Informacional y la CTel, como estrategia didáctico-pedagógica y soporte para la recuperación de la confianza del tejido social afectado por el conflicto, Código SIGP 58950” funded by “Fondo Nacional de Financiamiento para la Ciencia, la Tecnología y la Innovación, Fondo Francisco José de Caldas con contrato No. 213 – 2018 con Código 58960”.
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Velasquez-Martinez, L.F., Luna-Naranjo, D., Cárdenas-Peña, D., Acosta-Medina, C., Castaño, G.A., Castellanos-Dominguez, G. (2019). Relevance of Common Spatial Patterns Ranked by Kernel PCA in Motor Imagery Classification. In: Liang, P., Goel, V., Shan, C. (eds) Brain Informatics. BI 2019. Lecture Notes in Computer Science(), vol 11976. Springer, Cham. https://doi.org/10.1007/978-3-030-37078-7_2
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DOI: https://doi.org/10.1007/978-3-030-37078-7_2
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