Enhancement of Hybrid Deep Neural Network Using Activation Function for EEG based Emotion Recognition

Abstract Deep Neural Network (DNN) is an advancing technology that improves our life by allowing machines to perform complex tasks. Hybrid Deep Neural Network (HDNN) is widely used for emotion recognition using EEG signals due to its increase in performance than DNN. Among several factors that improve the performance of the network, activation is an essential parameter that improves the model accuracy by introducing non-linearity into DNN. Activation function enables non-linear learning and solve the complexity between the input and output data. The selection of activation function depends on the type of data that is used for computation. This paper investigates the model performance of different activation functions like ReLU, ELU and tanh on a hybrid CNN with Bi-LSTM model for emotion recognition. The model was tested on DEAP dataset which is an emotion dataset that uses physiological and EEG signals. The experimental results have shown that the model has improved accuracy when ELU function is used.