Hyperparameter optimization of convolutional neural network using particle swarm optimization for emotion recognition

Abstract

Emotion identification has been widely researched based on facial expressions, voice, and body movements. Several studies on emotion recognition have also been performed using electroencephalography (EEG) signals and the results also show that the technique has a high level of accuracy. EEG signals that detected by standart method using exclusive representations of time and frequency domains presented unefficient results. Some researchers using the convolutional neural network (CNN) method performed EEG signal for emotional recognition and obtained the best results in almost all benchmarks. Although CNN has shown fairly high accuracy, there is still a lot of room for improvement. CNN is sensitive to its hyperparameter value because it has considerable effect on the behavior and efficiency of the CNN architecture. So that the use of optimization algorithms is expected to provide an alternative selection of appropriate hyper parameter values on CNN. Particle swarm optimization (PSO) algorithm is a metaheuristic-based optimization algorithm with many advantages. This PSO algorithm was chosen to optimize the hyperparameter values on CNN. Based on the evaluation results in each model, hybrid CNN-PSO showed better results and achieved the best value in 80:20 split data which is 99.30% accuracy.