P300 Speller is an important information transforming method in Brain-Computer Interface.However

because the signal-to-noise ratio is comparably low and the size of training sample set is too large

the recognition efficiency in P300 Speller is not ideal by using popular linear recognition method or non linear methods such as Support Vector Machine.This paper introduces a novel method based on Adaptive Boosting SVM using weighted resampling process.We use AdaBoost resampling method to construct a series of little size training sample sub sets on the large integer set and then train the SVMs on these sub sets

finally run the recognition by combining these SVM's output.Using 6 subjects' EEG features from P300 Speller character identifying experiment we find that this method improves character identifying accuracy significantly.We achieve a character identifying accuracy of 97.5% on combining 5 repetitive stimulus features.A further evaluation using international BCI Competition dataset has proved that this method achieves a 7.35% enhancement in character identifying accuracy and a 48.9% enhancement in information transforming velocity on combing 5 repetitive stimulus features.This study demonstrated that ABSVM has the ability of improve recognition accuracy and information transforming velocity in P300 Speller and it is worthy of a further study and development.