Journal
COGNITIVE NEURODYNAMICS
Volume 4, Issue 3, Pages 207-216Publisher
SPRINGER
DOI: 10.1007/s11571-010-9114-0
Keywords
Electroencephalogram (EEG); Motor imagery; Brain computer interface (BCI); Channel; Frequency band; Semi-supervised learning
Categories
Funding
- National Natural Science Foundation of China [60825306, 60802068]
- Natural Science Foundation of Guangdong Province, China [9251064101000012]
- SCUT [2009ZZ0055]
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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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