Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning
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 act...
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MDPI AG
2025-01-01
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| Series: | Mathematics |
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| author | Jonathan Axel Cruz-Vazquez Jesús Yaljá Montiel-Pérez Rodolfo Romero-Herrera Elsa Rubio-Espino |
| author_facet | Jonathan Axel Cruz-Vazquez Jesús Yaljá Montiel-Pérez Rodolfo Romero-Herrera Elsa Rubio-Espino |
| author_sort | Jonathan Axel Cruz-Vazquez |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a6d0613dfcb74b7b9b44410bc0b4b404 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-a6d0613dfcb74b7b9b44410bc0b4b4042025-01-24T13:39:54ZengMDPI AGMathematics2227-73902025-01-0113225410.3390/math13020254Emotion Recognition from EEG Signals Using Advanced Transformations and Deep LearningJonathan Axel Cruz-Vazquez0Jesús Yaljá Montiel-Pérez1Rodolfo Romero-Herrera2Elsa Rubio-Espino3Instituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México 07738, MexicoInstituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México 07738, MexicoInstituto Politécnico Nacional, Escuela Superior de Cómputo, Ciudad de México 07738, MexicoInstituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México 07738, MexicoAffective 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.https://www.mdpi.com/2227-7390/13/2/254EEG signalsemotion recognitiondeep learningsignal processing |
| spellingShingle | Jonathan Axel Cruz-Vazquez Jesús Yaljá Montiel-Pérez Rodolfo Romero-Herrera Elsa Rubio-Espino Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning Mathematics EEG signals emotion recognition deep learning signal processing |
| title | Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning |
| title_full | Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning |
| title_fullStr | Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning |
| title_full_unstemmed | Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning |
| title_short | Emotion Recognition from EEG Signals Using Advanced Transformations and Deep Learning |
| title_sort | emotion recognition from eeg signals using advanced transformations and deep learning |
| topic | EEG signals emotion recognition deep learning signal processing |
| url | https://www.mdpi.com/2227-7390/13/2/254 |
| work_keys_str_mv | AT jonathanaxelcruzvazquez emotionrecognitionfromeegsignalsusingadvancedtransformationsanddeeplearning AT jesusyaljamontielperez emotionrecognitionfromeegsignalsusingadvancedtransformationsanddeeplearning AT rodolforomeroherrera emotionrecognitionfromeegsignalsusingadvancedtransformationsanddeeplearning AT elsarubioespino emotionrecognitionfromeegsignalsusingadvancedtransformationsanddeeplearning |