An approach on recognizing Electroencephalography (EEG) emotion using empirical wavelet transform (EWT) and autoregressive (AR) model is given in this paper.
An approach on recognizing Electroencephalography (EEG) emotion using empirical wavelet transform (EWT) and autoregressive (AR) model is given in this paper.
In this paper, EEG signals-based automated cross-subject emotion recognition framework is proposed using the Fourier-Bessel series expansion-based empirical ...
In this study, we have developed an effective approach to mitigate the motion artifacts in EEG signals by using empirical wavelet transform (EWT) technique.
Jul 20, 2023 · Its classical techniques include wavelet transform (WT) (Gupta et al., 2018), Hilbert–Huang transform (HHT) (Chang et al., 2022), and Wigner– ...
Oct 1, 2021 · In this paper, we describe the common steps of an emotion recognition algorithm based on EEG from data acquisition, preprocessing, feature extraction, feature ...
This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD).
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This study designs a wavelet-based synchro-squeezing transform (WSST) driven optimized ensemble deep learning-based automatic multi-class emotion ...
This work proposes the classification of emotions in electroencephalographical signals, transforming these discrete signals into a time-scale representation by ...
Sep 11, 2023 · In this paper, based on prior knowledge that emotion varies slowly across time, we propose a temporal-difference minimizing neural network (TDMNN) for EEG ...