A calibration-free P300 BCI system using an on-line updating classifier based on reinforcement learning

Abstract

P300 brain-computer interface (BCI) is a very promising technology. However, the time-consuming calibration before every use reduces the convenience of P300 BCI. In recent years, researchers are increasingly concerning the studies on calibration-free P300 BCI. In this study, we designed an on-line updating classifier for recognizing P300 based on the fundament of reinforcement learning and developed a calibration-free P300 BCI system. The on-line updating classifier can be randomly initiated and quickly optimized by learning from the system feedback. The system uses the latest classifier to recognize P300, acquires the user's feedback, and calls an on-line updating algorithm to optimize the classifier. By this system, any user can start using P300 BCI without having to collect the training data. To verify this system, we recruited 7 volunteers as the subjects to participate in a brain-controlled Chinese Pinyin experiment based on this system. The mean accuracies of recognizing P300, Pinyin symbols and Chinese words in the on-line experiment were 84.69% and 80.55% respectively. The results show the effectiveness of our on-line updating classifier and the feasibility of the developed system.