Effect of Dilated Convolution on Performance and Parameters of Devanagari Script-based P300 Speller

P300 speller is a well-known Brain-Computer Interface (BCI) application that allows users to spell words using cognitive ability and establishes a pathway between the human mind and a computer. P300 detection is the most crucial stage in the design of the P300 character speller. However, present Convolutional Neural Network (CNN) architectures hinder the use of CNNs in portable BCIs as they restrict future accuracy improvements of P300 detection and require significant complexity to attain competitive accuracy. Furthermore, the multi-trial approach adopted in most of the recent works is a major bottleneck in the real-time implementation of such a speller. To deal with both issues, the authors propose a single trial P300 detection using compact CNN architecture with dilated convolution (D-EEGNet). The proposed model with 1066 parameters achieves a classification accuracy of 80.86 % for a Devanagari Script-based P300 speller. Apart from lessening the trainable parameters, D-EEGNet also reduces computational complexity. Moreover, the proposed model demonstrates the ability to deal with high variance often encountered in single-trial detection.