Intelligent Systems with Applications (May 2023)
Practical hyperparameters tuning of convolutional neural networks for EEG emotional features classification
Abstract
EEG-based emotional states recognition is an active research direction, rushed up by recent emerging applications of intelligent systems and smart environments. However, accurate EEG-based emotion recognition is still challenging even with the use of the most efficient recent approaches like deep learning. Whereas many studies showed that convolutional neural networks can achieve good results in this field, they did not offer a general guide to subsequent researchers in designing dedicated networks. For practical deployment of deep neural networks in emotion-aware systems, an efficient design method, in terms of hyperparameters selection facilities, is needed. This paper investigates the simplification of the design process of a convolutional neural network applied to a binary and subject-dependent emotional valence classification. The proposed approach is based on two key solutions which are network architecture modularity and procedural tuning method. The proposed tuning approach estimates, in a given search space, the hyperparameter tunability defined as its main effect on the classification accuracy. The search space is depicted by selected hyperparameters levels and a fractional orthogonal array according to the Taguchi robust Design of Experiments method. In this investigation, we include twelve hyperparameters related to the neural network architecture and the training process. The significance of the estimated hyperparameter tunability is validated by statistical variance analysis. Experimental results allowed the determination of the best performance hyperparameters combination, confirmed the well-established effect of some training hyperparameters and revealed the importance of the pooling layer type in our case.
