Classification and Recognition of P300 Event Related Potential Based on Convolutional Neural Network

Abstract

Electroencephalography plays an important role in brain science research and human disease diagnosis, especially the classification and recognition of P300 event related potential has been widely concerned. In view of the low accuracy of P300 potential recognition in brain computer interface system, a classification and recognition method of P300 event related potentials based on improved Convolution Neural Network (CNN) is proposed. First, we use EEGLAB toolkit for preprocessing operations such as filtering, power frequency removal, eye movement removal and normalization. The character multiclassification problem is then transformed into a dichotomous task of whether P300 signals existed, with classification labels manually made based on stimulus row and column numbers in the training data. Finally, contrast is made with the Support Vector Machine (SVM) algorithm. The results show that the CNN algorithm we employed can achieve 98.5% correct prediction on the testing data, with higher accuracy rate and reliability than the SVM algorithm. This is of great significance for the application of brain-computer interface.