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Before the operation of a motor imagery based brain-computer interface (BCI)adopting machine learning techniques, a cumbersome training procedure isunavoidable. The development of a practical BCI posed the challenge ofclassifying single-trial EEG with a small training set. In this letter, weaddressed this problem by employing a series of signal processing and machinelearning approaches to alleviate overfitting and obtained test accuracy similarto training accuracy on the datasets from BCI Competition III and our ownexperiments.

Classifying Single-Trial EEG during Motor Imagery with a Small Training Set