Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning

Jonathan Axel Cruz-Vazquez (), Jesús Yaljá Montiel-Pérez (), Rodolfo Romero-Herrera and Elsa Rubio-Espino
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Jonathan Axel Cruz-Vazquez: Instituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México 07738, Mexico
Jesús Yaljá Montiel-Pérez: Instituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México 07738, Mexico
Rodolfo Romero-Herrera: Instituto Politécnico Nacional, Escuela Superior de Cómputo, Ciudad de México 07738, Mexico
Elsa Rubio-Espino: Instituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México 07738, Mexico

Mathematics, 2025, vol. 13, issue 2, 1-40

Abstract: Affective computing aims to develop systems capable of effectively interacting with people through emotion recognition. Neuroscience and psychology have established models that classify universal human emotions, providing a foundational framework for developing emotion recognition systems. Brain activity related to emotional states can be captured through electroencephalography (EEG), enabling the creation of models that classify emotions even in uncontrolled environments. In this study, we propose an emotion recognition model based on EEG signals using deep learning techniques on a proprietary database. To improve the separability of emotions, we explored various data transformation techniques, including Fourier Neural Networks and quantum rotations. The convolutional neural network model, combined with quantum rotations, achieved a 95% accuracy in emotion classification, particularly in distinguishing sad emotions. The integration of these transformations can further enhance overall emotion recognition performance.

Keywords: EEG signals; emotion recognition; deep learning; signal processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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