A Review on Nonlinear Methods Using Electroencephalographic Recordings for Emotion Recognition
Electroencephalographic (EEG) recordings are re ceiving growing attention in the field of emotion recognition, stimulus. Traditionally, EEG signals have been studied from a linear viewpoint by means of statistical and frequency fea tures. Nevertheless, given that the brain follows a completely nonli...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Castilla-La Mancha |
| Repositorio: | RUIdeRA. Repositorio Institucional de la UCLM |
| OAI Identifier: | oai:ruidera.uclm.es:10578/35314 |
| Acceso en línea: | https://doi.org/10.1109/TAFFC.2018.2890636 https://hdl.handle.net/10578/35314 |
| Access Level: | acceso abierto |
| Palabra clave: | Electroencephalogram Emotion recognition Nonlinear metrics Survey |
| Sumario: | Electroencephalographic (EEG) recordings are re ceiving growing attention in the field of emotion recognition, stimulus. Traditionally, EEG signals have been studied from a linear viewpoint by means of statistical and frequency fea tures. Nevertheless, given that the brain follows a completely nonlinear and nonstationary behavior, linear metrics present certain important limitations. In this sense, the use of nonlinear methods has recently revealed new information that may help to understand how the brain works under a series of emotional states. Hence, this paper summarizes the most recent works that have applied nonlinear methods in EEG signal analysis for emotion recognition. This paper also identifies some nonlinear indices that have not been employed yet in this research area. |
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