Abstract
Brain-Computer Interface (BCI) provides a direct communication from a brain to a computer through the analysis of brain evoked responses. The detection of motor imagery (MI) is one of the main paradigm used in BCI. We investigate the classification of MI using Riemannian geometry, a density based approach for discriminating brain evoked responses in multiple frequency bands. We compare classifiers based on the minimum distance to the mean (MDM) and k-nearest neighbors (KNN) approaches, with decisions weighted in relation to the kappa value of each frequency band. For the multi-class classification, the best performance was achieved with the weighted KNN with an average kappa value of 51.9%.
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This study was supported by the NIH-R15 NS118581 project.
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Tiwale, G., Cecotti, H. (2022). Motor Imagery Classification Using Riemannian Geometry in Multiple Frequency Bands with a Weighted Nearest Neighbors Approach. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham. https://doi.org/10.1007/978-3-031-07750-0_15
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