EEG-based emotion recognition using MobileNet Recurrent Neural Network with time-frequency features

Abstract

Despite the developments in deep learning, extracting different features from brain signals remains a crucial challenge in EEG-based emotion recognition. This study introduces a novel methodology to overcome the challenge of capturing temporal and channel-related features. The basic idea of the proposed method is to use scalograms to capture subtle emotional patterns in EEG signals, followed by MobileNet Recurrent Neural Network (MRNN). Unlike conventional methods, MRNN utilizes recurrent connections to learn sequential patterns and recognize complex temporal dependencies in the data, enhancing EEG-based emotion recognition. The model is trained and tested on two publicly available datasets, DEAP and DREAMER, and achieved better results at 85.95 % for 15 classes and 96.29 % for 9 classes accuracy on average. Our approach extends beyond binary or limited multi-class emotion classification and significantly improves accuracy compared to the state-of-the-art methods. This research can have potential applications in developing emotion recognition systems for real-world scenarios, such as mental health monitoring or human-computer interaction.