Emotion Classification from Electroencephalographic Signals Using Machine Learning
Background: Emotions significantly influence decision-making, social interactions, and medical outcomes. Leveraging emotion recognition through Electroencephalography (EEG) signals offers potential advancements in personalized medicine, adaptive technologies, and mental health diagnostics. This stud...
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MDPI AG
2024-11-01
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| author | Jesus Arturo Mendivil Sauceda Bogart Yail Marquez José Jaime Esqueda Elizondo |
| author_facet | Jesus Arturo Mendivil Sauceda Bogart Yail Marquez José Jaime Esqueda Elizondo |
| author_sort | Jesus Arturo Mendivil Sauceda |
| collection | DOAJ |
| description | Background: Emotions significantly influence decision-making, social interactions, and medical outcomes. Leveraging emotion recognition through Electroencephalography (EEG) signals offers potential advancements in personalized medicine, adaptive technologies, and mental health diagnostics. This study aimed to evaluate the performance of three neural network architectures—ShallowFBCSPNet, Deep4Net, and EEGNetv4—for emotion classification using the SEED-V dataset. Methods: The SEED-V dataset comprises EEG recordings from 16 individuals exposed to 15 emotion-eliciting video clips per session, targeting happiness, sadness, disgust, neutrality, and fear. EEG data were preprocessed with a bandpass filter, segmented by emotional episodes, and split into training (80%) and testing (20%) sets. Three neural networks were trained and evaluated to classify emotions from the EEG signals. Results: ShallowFBCSPNet achieved the highest accuracy at 39.13%, followed by Deep4Net (38.26%) and EEGNetv4 (25.22%). However, significant misclassification issues were observed, such as EEGNetv4 predicting all instances as “Disgust” or “Neutral” depending on the configuration. Compared to state-of-the-art methods, such as ResNet18 combined with differential entropy, which achieved 95.61% accuracy on the same dataset, the tested models demonstrated substantial limitations. Conclusions: Our results highlight the challenges of generalizing across emotional states using raw EEG signals, emphasizing the need for advanced preprocessing and feature-extraction techniques. Despite these limitations, this study provides valuable insights into the potential and constraints of neural networks for EEG-based emotion recognition, paving the way for future advancements in the field. |
| format | Article |
| id | doaj-art-48d4aba33e224daaa3f772e33057d462 |
| institution | DOAJ |
| issn | 2076-3425 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-48d4aba33e224daaa3f772e33057d4622025-08-20T02:53:38ZengMDPI AGBrain Sciences2076-34252024-11-011412121110.3390/brainsci14121211Emotion Classification from Electroencephalographic Signals Using Machine LearningJesus Arturo Mendivil Sauceda0Bogart Yail Marquez1José Jaime Esqueda Elizondo2Tecnológico Nacional de México, Campus Tijuana. Calz del Tecnológico 12950, Tomas Aquino, Tijuana 22414, MexicoTecnológico Nacional de México, Campus Tijuana. Calz del Tecnológico 12950, Tomas Aquino, Tijuana 22414, MexicoFacultad de Ciencias Químicas e Ingeniería, Universidad Autónoma de Baja California, Calzada Universidad 14418, Parque Industrial Internacional, Tijuana 22390, MexicoBackground: Emotions significantly influence decision-making, social interactions, and medical outcomes. Leveraging emotion recognition through Electroencephalography (EEG) signals offers potential advancements in personalized medicine, adaptive technologies, and mental health diagnostics. This study aimed to evaluate the performance of three neural network architectures—ShallowFBCSPNet, Deep4Net, and EEGNetv4—for emotion classification using the SEED-V dataset. Methods: The SEED-V dataset comprises EEG recordings from 16 individuals exposed to 15 emotion-eliciting video clips per session, targeting happiness, sadness, disgust, neutrality, and fear. EEG data were preprocessed with a bandpass filter, segmented by emotional episodes, and split into training (80%) and testing (20%) sets. Three neural networks were trained and evaluated to classify emotions from the EEG signals. Results: ShallowFBCSPNet achieved the highest accuracy at 39.13%, followed by Deep4Net (38.26%) and EEGNetv4 (25.22%). However, significant misclassification issues were observed, such as EEGNetv4 predicting all instances as “Disgust” or “Neutral” depending on the configuration. Compared to state-of-the-art methods, such as ResNet18 combined with differential entropy, which achieved 95.61% accuracy on the same dataset, the tested models demonstrated substantial limitations. Conclusions: Our results highlight the challenges of generalizing across emotional states using raw EEG signals, emphasizing the need for advanced preprocessing and feature-extraction techniques. Despite these limitations, this study provides valuable insights into the potential and constraints of neural networks for EEG-based emotion recognition, paving the way for future advancements in the field.https://www.mdpi.com/2076-3425/14/12/1211machine learningartificial intelligenceEEGemotion recognitionneural networksdeep learning |
| spellingShingle | Jesus Arturo Mendivil Sauceda Bogart Yail Marquez José Jaime Esqueda Elizondo Emotion Classification from Electroencephalographic Signals Using Machine Learning Brain Sciences machine learning artificial intelligence EEG emotion recognition neural networks deep learning |
| title | Emotion Classification from Electroencephalographic Signals Using Machine Learning |
| title_full | Emotion Classification from Electroencephalographic Signals Using Machine Learning |
| title_fullStr | Emotion Classification from Electroencephalographic Signals Using Machine Learning |
| title_full_unstemmed | Emotion Classification from Electroencephalographic Signals Using Machine Learning |
| title_short | Emotion Classification from Electroencephalographic Signals Using Machine Learning |
| title_sort | emotion classification from electroencephalographic signals using machine learning |
| topic | machine learning artificial intelligence EEG emotion recognition neural networks deep learning |
| url | https://www.mdpi.com/2076-3425/14/12/1211 |
| work_keys_str_mv | AT jesusarturomendivilsauceda emotionclassificationfromelectroencephalographicsignalsusingmachinelearning AT bogartyailmarquez emotionclassificationfromelectroencephalographicsignalsusingmachinelearning AT josejaimeesquedaelizondo emotionclassificationfromelectroencephalographicsignalsusingmachinelearning |