Abstract
Brain-computer interfaces (BCIs) based on electroencephalograms (EEG) are a noninvasive and cheap alternative to get a communication channel between brain and computers. Some of the main issues with EEG signals are its high dimensionality, high inter-user variance, and non-stationarity. In this work we present different approaches to deal with the high dimensionality of the data, finding relevant descriptors in EEG signals for motor intention recognition: first, a classical dimensionality reduction method using Diffusion Distance, second a technique based on spectral analysis of EEG channels associated with the frontal and prefrontal cortex, and third a projection over average signals. Performance analysis for different sets of features is done, showing that some of them are more robust to user variability.
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Iturralde, P.A., Patrone, M., Lecumberry, F., Fernández, A. (2012). Motor Intention Recognition in EEG: In Pursuit of a Relevant Feature Set. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2012. Lecture Notes in Computer Science, vol 7441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33275-3_68
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DOI: https://doi.org/10.1007/978-3-642-33275-3_68
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