Machine Learning and EEG for Emotional State Estimation
Defining “emotion” and its accurate measuring is a notorious problem in the psychology domain. It is usually addressed with subjective self-assessment forms filled manually by participants. Machine learning methods and EEG correlates of emotions enable to construction of automatic systems for object...
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| Autores principales: | , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2021
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/75970 |
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| Sumario: | Defining “emotion” and its accurate measuring is a notorious problem in the psychology domain. It is usually addressed with subjective self-assessment forms filled manually by participants. Machine learning methods and EEG correlates of emotions enable to construction of automatic systems for objective emotion recognition. Such systems could help to assess emotional states and could be used to improve emotional perception. In this chapter, we present a computer system that can automatically recognize an emotional state of a human, based on EEG signals induced by a standardized affective picture database. Based on the EEG signal, trained deep neural networks are then used together with mappings between emotion models to predict the emotions perceived by the participant. This, in turn, can be used for example in validation of affective picture databases standardization. |
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| ISBN: | 978-1-83968-635-1 |
| DOI: | 10.5772/intechopen.97133 |