  The electroencephalography (EEG) signal is a non-stationary, stochastic, and
highly non-linear bioelectric signal for which achieving high classification
accuracy is challenging, especially when the number of subjects is limited. As
frequently used solution, classifiers based on multilayer neural networks has
to be implemented without large training data sets and careful tuning. This
paper proposes a manifold learning framework to classify two types of EEG data
from motor imagery (MI) tasks by discovering lower dimensional geometric
structures. For feature extraction, it is implemented by Common Spatial Pattern
(CSP) from the preprocessed EEG signals. In the neighborhoods of the features
for classification, the local approximation to the support of the data is
obtained, and then the features are assigned to the classes with the closest
support. A spherical approximation (SPA) classifier is created using spherelets
for local approximation, and the extracted features are classified with this
manifold-based method. The SPA classifier achieves high accuracy in the 2008
BCI competition data, and the analysis shows that this method can significantly
improve the decoding accuracy of MI tasks and exhibit strong robustness for
small sample datasets. It would be simple and efficient to tune the
two-parameters classifier for the online brain-computer interface(BCI)system.

Signal Processing;Machine Learning