P300 Recognition Based on Ensemble of SVMs : - BCI Controlled Robot Contest of 2019 World Robot Conference

Abstract

P300 is a typical event related potential (ERP), which has been applied in the brain-computer interface (BCI) and attracted the attention of researchers for nearly thirty years. Until now, the most challenge of P300-based BCI is to detect P300 in a minimum of repeats, which is a balance between recognition accuracy and a number of repeats. Previous studies showed that the shallow learning model such as support vector machine (SVM), relevance vector machine (RVM) and linear discriminant analysis (LDA) had been successfully used for P300 classification. In this study, we proposed a P300 recognition algorithm based on ensemble of SVMs. Firstly, we intercept 600 ms data after the visual stimulation. Secondly, the extracted segment electroencephalogram (EEG) was averaged by iteration to enhance the signal-to-noise ratio. Thirdly, these obtained signals were fed to the ensemble of SVMs for target character is recognition. Finally, an output regulation mechanism was proposed for adjusting the outputs of ensemble of SVMs. The experimental results show that the highest accuracy (80%) and the information transmission rate (17.21 bits/min) are observed in the repeat number of only five. As an application and verification, this algorithm won the third prize in the BCI Controlled Robot Contest of 2019 World Robot Conference.