title: Classification of four-class motor imagery employing single-channel electroencephalography.
abstract: With advances in brain-computer interface (BCI) research, a portable few- or single-channel BCI system has become necessary. Most recent BCI studies have demonstrated that the common spatial pattern (CSP) algorithm is a powerful tool in extracting features for multiple-class motor imagery. However, since the CSP algorithm requires multi-channel information, it is not suitable for a few- or single-channel system. In this study, we applied a short-time Fourier transform to decompose a single-channel electroencephalography signal into the time-frequency domain and construct multi-channel information. Using the reconstructed data, the CSP was combined with a support vector machine to obtain high classification accuracies from channels of both the sensorimotor and forehead areas. These results suggest that motor imagery can be detected with a single channel not only from the traditional sensorimotor area but also from the forehead area.
authors.affiliation: Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu, China.
authors.forename: Sheng
authors.initials: S
authors.lastname: Ge
authors.affiliation: School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu, China.
authors.forename: Ruimin
authors.initials: R
authors.lastname: Wang
authors.affiliation: Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu, China.
authors.forename: Dongchuan
authors.initials: D
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D005583:Fourier Analysis; D006225:Hand / Q000502:physiology; D006801:Humans; D008962:Models, Theoretical; D009044:Motor Cortex / Q000502:physiology*; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine