A Deep Convolutional Neural Network Model for Classification of Emotions from Electroencephalography Data

Employing electroencephalography (EEG) data for classifying emotion has attracted a significant interest among researchers. With the ever advancing machine learning and neural network computational power, several models have been proposed for classifying the emotion states. Training an efficient classifier requires selection of appropriate features demanding complex computations. The present article investigates whether dense convolutional neural networks can extract the features from just pre-processed raw EEG signals enabling a suitability for online classification. The article presents a fully connected multi-channel neural network model comprising of dense con-volutional layers coupled with sparse autoencoder and dense perceptron layers for classifying emotions from 62-channel EEG data. The proposed model uses the dense convolution layers to learn emotion features directly from each channel. The cascaded SAE layer coupled with dense perceptron network learns correlations between the channels for efficient classification. In addition, different methods exist to generate training and testing sets for validating classifier models. In this light, the article discusses three most recent methods to generate training and testing sets employed in emotion classification. The proposed model achieves a best testing accuracy of 97.42%.