Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals
Nowadays, the recognition of emotions in people with sensory disabilities still represents a challenge due to the difficulty of generalizing and modeling the set of brain signals. In recent years, the technology that has been used to study a person’s behavior and emotions based on brain signals is t...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-05-01
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| Series: | Frontiers in Neuroinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2021.642766/full |
| _version_ | 1819298608560734208 |
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| author | Jesús Leonardo López-Hernández Israel González-Carrasco José Luis López-Cuadrado Belén Ruiz-Mezcua |
| author_facet | Jesús Leonardo López-Hernández Israel González-Carrasco José Luis López-Cuadrado Belén Ruiz-Mezcua |
| author_sort | Jesús Leonardo López-Hernández |
| collection | DOAJ |
| description | Nowadays, the recognition of emotions in people with sensory disabilities still represents a challenge due to the difficulty of generalizing and modeling the set of brain signals. In recent years, the technology that has been used to study a person’s behavior and emotions based on brain signals is the brain–computer interface (BCI). Although previous works have already proposed the classification of emotions in people with sensory disabilities using machine learning techniques, a model of recognition of emotions in people with visual disabilities has not yet been evaluated. Consequently, in this work, the authors present a twofold framework focused on people with visual disabilities. Firstly, auditory stimuli have been used, and a component of acquisition and extraction of brain signals has been defined. Secondly, analysis techniques for the modeling of emotions have been developed, and machine learning models for the classification of emotions have been defined. Based on the results, the algorithm with the best performance in the validation is random forest (RF), with an accuracy of 85 and 88% in the classification for negative and positive emotions, respectively. According to the results, the framework is able to classify positive and negative emotions, but the experimentation performed also shows that the framework performance depends on the number of features in the dataset and the quality of the Electroencephalogram (EEG) signals is a determining factor. |
| first_indexed | 2024-12-24T05:32:36Z |
| format | Article |
| id | doaj.art-3baf01539b124acc819d9b89a520a19d |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-24T05:32:36Z |
| publishDate | 2021-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-3baf01539b124acc819d9b89a520a19d2022-12-21T17:13:07ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962021-05-011510.3389/fninf.2021.642766642766Framework for the Classification of Emotions in People With Visual Disabilities Through Brain SignalsJesús Leonardo López-HernándezIsrael González-CarrascoJosé Luis López-CuadradoBelén Ruiz-MezcuaNowadays, the recognition of emotions in people with sensory disabilities still represents a challenge due to the difficulty of generalizing and modeling the set of brain signals. In recent years, the technology that has been used to study a person’s behavior and emotions based on brain signals is the brain–computer interface (BCI). Although previous works have already proposed the classification of emotions in people with sensory disabilities using machine learning techniques, a model of recognition of emotions in people with visual disabilities has not yet been evaluated. Consequently, in this work, the authors present a twofold framework focused on people with visual disabilities. Firstly, auditory stimuli have been used, and a component of acquisition and extraction of brain signals has been defined. Secondly, analysis techniques for the modeling of emotions have been developed, and machine learning models for the classification of emotions have been defined. Based on the results, the algorithm with the best performance in the validation is random forest (RF), with an accuracy of 85 and 88% in the classification for negative and positive emotions, respectively. According to the results, the framework is able to classify positive and negative emotions, but the experimentation performed also shows that the framework performance depends on the number of features in the dataset and the quality of the Electroencephalogram (EEG) signals is a determining factor.https://www.frontiersin.org/articles/10.3389/fninf.2021.642766/fullemotion classification algorithmbrain–computer interfacemachine learningvisual disabilitiesaffective computing |
| spellingShingle | Jesús Leonardo López-Hernández Israel González-Carrasco José Luis López-Cuadrado Belén Ruiz-Mezcua Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals Frontiers in Neuroinformatics emotion classification algorithm brain–computer interface machine learning visual disabilities affective computing |
| title | Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals |
| title_full | Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals |
| title_fullStr | Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals |
| title_full_unstemmed | Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals |
| title_short | Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals |
| title_sort | framework for the classification of emotions in people with visual disabilities through brain signals |
| topic | emotion classification algorithm brain–computer interface machine learning visual disabilities affective computing |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2021.642766/full |
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