Abstract
Parameter setting plays an important role for improving the performance of a brain computer interface (BCI). Currently, parameters (e.g. channels and frequency band) are often manually selected. It is time-consuming and not easy to obtain an optimal combination of parameters for a BCI. In this paper, motor imagery-based BCIs are considered, in which channels and frequency band are key parameters. First, a semi-supervised support vector machine algorithm is proposed for automatically selecting a set of channels with given frequency band. Next, this algorithm is extended for joint channel-frequency selection. In this approach, both training data with labels and test data without labels are used for training a classifier. Hence it can be used in small training data case. Finally, our algorithms are applied to a BCI competition data set. Our data analysis results show that these algorithms are effective for selection of frequency band and channels when the training data set is small.
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The authors would like to thank the Berlin BCI group for providing the data set IVa in BCI competition III.
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This work was supported by National Natural Science Foundation of China under Grants 60825306 and 60802068; Natural Science Foundation of Guangdong Province, China under grant 9251064101000012 and supported by the Fundamental Research Funds for the Central Universities, SCUT under grant 2009ZZ0055.
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Long, J., Li, Y. & Yu, Z. A semi-supervised support vector machine approach for parameter setting in motor imagery-based brain computer interfaces. Cogn Neurodyn 4, 207–216 (2010). https://doi.org/10.1007/s11571-010-9114-0
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DOI: https://doi.org/10.1007/s11571-010-9114-0