Improving the P300-based brain-computer interface with transfer learning

Abstract

P300-based brain-computer interface (BCI) is one of the most common BCIs. Due to the characteristics of P300 responses vary from person to person, it leads to the necessity of collecting much labeled data from each user and the problem of time-consuming in many applications. In this work, a transfer learning method which dynamically adjusts the weights of instances is applied to improve the P300-based BCI. Offline experiments on BCI competition III and P300 speller with ALS patients dataset prove the robustness of different subjects and the validity when the data of different individuals are sufficient. Online experiments on our P300-based robot control system demonstrated that the classification performance could be enhanced by 13.02% at most compared to the traditional classifiers.