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Le Song, Julien Epps, "Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features", Computational Intelligence and Neuroscience, vol. 2007, Article ID 057180, 11 pages, 2007. https://doi.org/10.1155/2007/57180
Classifying EEG for Brain-Computer Interface: Learning Optimal Filters for Dynamical System Features
Classification of multichannel EEG recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). In this paper, we consider EEG signals as the outputs of a networked dynamical system (the cortex), and exploit synchronization features from the dynamical system for classification. Herein, we also propose a new framework for learning optimal filters automatically from the data, by employing a Fisher ratio criterion. Experimental evaluations comparing the proposed dynamical system features with the CSP and the AR features reveal their competitive performance during classification. Results also show the benefits of employing the spatial and the temporal filters optimized using the proposed learning approach.
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Copyright © 2007 Le Song and Julien Epps. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.