Abstract
Classification of EEG (electroencephalographic) signals recorded during right and left motor imagery tasks is a technique for designing BCI (Brain-computer interfaces). In this paper, the regression tree is used to separate the right/left patterns that are extracted by ERD time courses. The regression tree is a statistical method to identify complex patterns without rigorous theoretical and distributional assumptions. The simulation result shows that the proposed BCI can provide satisfactory offline classification error rate and mutual information.
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Wong, C., Wan, F. (2009). Classification of Imagery Movement Tasks for Brain-Computer Interfaces Using Regression Tree. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_48
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DOI: https://doi.org/10.1007/978-3-642-01216-7_48
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