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- W2907371384 endingPage "820" @default.
- W2907371384 startingPage "801" @default.
- W2907371384 abstract "Electroencephalographic (EEG) recordings are receiving growing attention in the field of emotion recognition, since they monitor the brain’s first response to an external stimulus. Traditionally, EEG signals have been studied from a linear viewpoint by means of statistical and frequency features. 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." @default.
- W2907371384 created "2019-01-11" @default.
- W2907371384 creator A5009736033 @default.
- W2907371384 creator A5048340039 @default.
- W2907371384 creator A5053660048 @default.
- W2907371384 creator A5069243017 @default.
- W2907371384 date "2021-07-01" @default.
- W2907371384 modified "2023-10-17" @default.
- W2907371384 title "A Review on Nonlinear Methods Using Electroencephalographic Recordings for Emotion Recognition" @default.
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