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Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning

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Publicado en:Mathematics vol. 13, no. 2 (2025), p. 254
Autor principal: Cruz-Vazquez, Jonathan Axel
Otros Autores: Montiel-Pérez, Jesús Yaljá, Romero-Herrera, Rodolfo, Rubio-Espino, Elsa
Publicado:
MDPI AG
Materias:
Physiology
Emotional factors
Accuracy
Happiness
Datasets
Deep learning
Classification
Affective computing
Fourier transforms
Emotion recognition
Artificial neural networks
Signal processing
Neural networks
Electroencephalography
Emotions
Machine learning
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2227-7390
DOI:10.3390/math13020254
Fuente:Engineering Database