A Comprehensive Evaluation of Features and Simple Machine Learning Algorithms for Electroencephalographic-Based Emotion Recognition
The study of electroencephalographic (EEG) signals has gained popularity in recent years because they are unlikely to intentionally fake brain activity. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. A...
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MDPI AG
2024-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/6/2228 |
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| author | Mayra Álvarez-Jiménez Tania Calle-Jimenez Myriam Hernández-Álvarez |
| author_facet | Mayra Álvarez-Jiménez Tania Calle-Jimenez Myriam Hernández-Álvarez |
| author_sort | Mayra Álvarez-Jiménez |
| collection | DOAJ |
| description | The study of electroencephalographic (EEG) signals has gained popularity in recent years because they are unlikely to intentionally fake brain activity. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. Analyzing these signals involves three main processes: feature extraction, feature selection, and classification. The present study extensively evaluates feature sets across domains and their impact on emotion recognition. Feature selection improves results across the different domains. Additionally, hybrid models combining features from various domains offer a superior performance when applying the public DEAP dataset for emotion classification using EEG signals. Time, frequency, time–frequency, and spatial domain attributes and their combinations were analyzed. The effectiveness of the input vectors for the classifiers was validated using SVM, KNN, and ANN, which are simple classification algorithms selected for their widespread use and better performance in the state of the art. The use of simple machine learning algorithms makes the findings particularly valuable for real-time emotion recognition applications where the computational resources and processing time are often limited. After the analysis stage, feature vector combinations were proposed to identify emotions in four quadrants of the valence–arousal representation space using the DEAP dataset. This research achieved a classification accuracy of 96% using hybrid features in the four domains and the ANN classifier. A lower computational cost was obtained in the frequency domain. |
| first_indexed | 2024-04-24T18:35:23Z |
| format | Article |
| id | doaj.art-362f1729afb348d3b8bc89b40abeeb3f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:35:23Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-362f1729afb348d3b8bc89b40abeeb3f2024-03-27T13:19:02ZengMDPI AGApplied Sciences2076-34172024-03-01146222810.3390/app14062228A Comprehensive Evaluation of Features and Simple Machine Learning Algorithms for Electroencephalographic-Based Emotion RecognitionMayra Álvarez-Jiménez0Tania Calle-Jimenez1Myriam Hernández-Álvarez2Departamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Ladrón de Guevara E11-25 y Andalucía, Edificio de Sistemas, Quito 170525, EcuadorDepartamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Ladrón de Guevara E11-25 y Andalucía, Edificio de Sistemas, Quito 170525, EcuadorDepartamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Ladrón de Guevara E11-25 y Andalucía, Edificio de Sistemas, Quito 170525, EcuadorThe study of electroencephalographic (EEG) signals has gained popularity in recent years because they are unlikely to intentionally fake brain activity. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. Analyzing these signals involves three main processes: feature extraction, feature selection, and classification. The present study extensively evaluates feature sets across domains and their impact on emotion recognition. Feature selection improves results across the different domains. Additionally, hybrid models combining features from various domains offer a superior performance when applying the public DEAP dataset for emotion classification using EEG signals. Time, frequency, time–frequency, and spatial domain attributes and their combinations were analyzed. The effectiveness of the input vectors for the classifiers was validated using SVM, KNN, and ANN, which are simple classification algorithms selected for their widespread use and better performance in the state of the art. The use of simple machine learning algorithms makes the findings particularly valuable for real-time emotion recognition applications where the computational resources and processing time are often limited. After the analysis stage, feature vector combinations were proposed to identify emotions in four quadrants of the valence–arousal representation space using the DEAP dataset. This research achieved a classification accuracy of 96% using hybrid features in the four domains and the ANN classifier. A lower computational cost was obtained in the frequency domain.https://www.mdpi.com/2076-3417/14/6/2228DEAP datasetfeature extractionfeature selectionfrequency domaintime domaintime–frequency domain |
| spellingShingle | Mayra Álvarez-Jiménez Tania Calle-Jimenez Myriam Hernández-Álvarez A Comprehensive Evaluation of Features and Simple Machine Learning Algorithms for Electroencephalographic-Based Emotion Recognition Applied Sciences DEAP dataset feature extraction feature selection frequency domain time domain time–frequency domain |
| title | A Comprehensive Evaluation of Features and Simple Machine Learning Algorithms for Electroencephalographic-Based Emotion Recognition |
| title_full | A Comprehensive Evaluation of Features and Simple Machine Learning Algorithms for Electroencephalographic-Based Emotion Recognition |
| title_fullStr | A Comprehensive Evaluation of Features and Simple Machine Learning Algorithms for Electroencephalographic-Based Emotion Recognition |
| title_full_unstemmed | A Comprehensive Evaluation of Features and Simple Machine Learning Algorithms for Electroencephalographic-Based Emotion Recognition |
| title_short | A Comprehensive Evaluation of Features and Simple Machine Learning Algorithms for Electroencephalographic-Based Emotion Recognition |
| title_sort | comprehensive evaluation of features and simple machine learning algorithms for electroencephalographic based emotion recognition |
| topic | DEAP dataset feature extraction feature selection frequency domain time domain time–frequency domain |
| url | https://www.mdpi.com/2076-3417/14/6/2228 |
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