Hybrid convolutional-multilayer perceptron artificial neural network for person recognition by high gamma EEG features

Abstract

We propose to use a hybrid Convolutional-Multilayer Perceptron Neural Network (CNN-MLP) architecture to learn high gamma EEG features for person recognition. An original EEG data set was collected featuring recordings during various physical and mental activities for this study. Experiments with high and low gamma and beta scale Convolutional filters were conducted. Particular usefulness of the EEG bands was observed, and high gamma scale CNN features were recognized as efficient for the person recognition task.