An Empirical Study on Sensor-aware Design of Convolutional Neural Networks for P300 Speller in Brain Computer Interface

Abstract

A Brain Computer Interface (BCI) character 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 achieved state-of-the-art results on the BCI character spelling accuracy. Unfortunately, to the best of our knowledge, it has not been studied whether the CNN should be designed differently to increase the spelling accuracy when the number of sensors used to acquire EEG signals is different. This paper performs an empirical study to investigate this issue. First, we show a motivational example which motivates us for this investigation. Then, we propose a method to design CNNs according to the number of sensors used in the BCI character speller. This method automatically configures a parametric CNN we have devised according to the given number of sensors. Experimental results on six datasets show that we need to design different CNNs when different number of sensors are used for the acquisition of EEG signals. Experimental results also show that our designed sensor-aware CNNs outperform other CNNs in terms of spelling accuracy in most cases. Our CNNs can increase the spelling accuracy achieved by other CNNs with up to 34%.