caa a22 4500 445294876 CHVBK 20180317142538.0 cr unu---uuuuu 170323e20100901xx s 000 0 eng 10.1007/s11571-010-9114-0 doi (NATIONALLICENCE)springer-10.1007/s11571-010-9114-0 A semi-supervised support vector machine approach for parameter setting in motor imagery-based brain computer interfaces [Elektronische Daten] [Jinyi Long, Yuanqing Li, Zhuliang Yu] 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. Springer Science+Business Media B.V., 2010 Electroencephalogram (EEG) nationallicence Motor imagery nationallicence Brain computer interface (BCI) nationallicence Channel nationallicence Frequency band nationallicence Semi-supervised learning nationallicence Long Jinyi The College of Automation Science and Engineering, South China University of Technology, 510640, Guangzhou, China aut Li Yuanqing The College of Automation Science and Engineering, South China University of Technology, 510640, Guangzhou, China aut Yu Zhuliang The College of Automation Science and Engineering, South China University of Technology, 510640, Guangzhou, China aut Cognitive Neurodynamics Springer Netherlands 4/3(2010-09-01), 207-216 1871-4080 4:3<207 2010 4 11571 https://doi.org/10.1007/s11571-010-9114-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11571-010-9114-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Long Jinyi The College of Automation Science and Engineering, South China University of Technology, 510640, Guangzhou, China aut NATIONALLICENCE 700 1- Li Yuanqing The College of Automation Science and Engineering, South China University of Technology, 510640, Guangzhou, China aut NATIONALLICENCE 700 1- Yu Zhuliang The College of Automation Science and Engineering, South China University of Technology, 510640, Guangzhou, China aut NATIONALLICENCE 773 0- Cognitive Neurodynamics Springer Netherlands 4/3(2010-09-01), 207-216 1871-4080 4:3<207 2010 4 11571 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer