Convolving Emotions: A Compact CNN for EEG-Based Emotion Recognition

• Main Authors: Cardoso-Moreno, Marco A., Macias, Cesar, Alcantara, Tania, Soto, Miguel, Calvo, Hiram, Yanez-Marquez, Cornelio
• Format: Conference Proceeding
• Language: English
• Subjects: Brain modeling; Classification; CNN; Convolutional Neural Network; Convolutional neural networks; Deep Learning; EEG; Electroencephalography; Emotion recognition; Proposals; Radio frequency; Skin
• ISSN: 2472-8322
• DOI: 10.1109/SSCI52147.2023.10372040

Emotion Recognition is a research area that has had a surge in interest, since areas such as mental health, psychological diagnosis, e-Learning and assistance for people who are not capable of communicating their feelings, depend on certain level, on the capacities of computer systems to reliably identify emotions. There are several approaches to this task, for instance, analyzing facial expressions, speech, and physiological signals (electrocardiogram, galvanic skin response, electroencephalogram, among others). Electroencephalogram is one of the preferred methods due, in part, to is great temporal resolution. Therefore, in this paper we used the EEG Brainwave Dataset as benchmark to test our model, which is a four layer, one dimensional convolutional neural network. After the preprocessing pipeline, consisting on considering certain features of the dataset as signals and processing them accordingly, by creating several channels by two decomposition methods, our model achieved accuracy values of 98.36% and 95.31 %, which is competitive with what is found on the state of the art, while being a significantly less complex model.