Determining AR order for BCI based on motor imagery

In this paper, autoregressive (AR) model coefficients and support vector machine (SVM) are used to classify the motor imagery EEG available from the well-known BCI competition database. In order to determine AR order, we use paired t-test to assess the impact of AR order on the classification precision of motor imagery EEG. The results show that there is a significant difference in the classification performance when the different AR orders are used to model motor imagery EEG. In this investigation, 12-order prevails. We try using the method of continuous re-training the SVM classifier to improve the classification precision of motor imagery EEG, and the experimental results show that the method is feasible and effective.