Ensemble of Convolutional Neural Networks for P300 Speller in Brain Computer Interface

Abstract

A Brain Computer Interface (BCI) speller allows human-beings to directly spell characters using eye-gazes, thereby building communication between the human brain and a computer. Convolutional Neural Networks (CNNs) have shown better ability than traditional machine learning methods to increase the character spelling accuracy for the BCI speller. Unfortunately, current CNNs can not learn well the features related to the target signal of the BCI speller. This issue limits these CNNs from further character spelling accuracy improvements. To address this issue, we propose a network, which combines our proposed two CNNs, with an existing CNN. These three CNNs of our network extract different features related to the target BCI signal. Our network uses the ensemble of the features extracted by these CNNs for BCI character spelling. Experimental results on three benchmark datasets show that our network outperforms other methods in most cases, with a significant spelling accuracy improvement up to 38.72%. In addition, the communication speed of the P300 speller based on our network is up to 2.56 times faster than the communication speed of the P300 speller based on other methods.