The classification of emotion and empirical mode decomposition (EMD). discrete and emotion recognition based on empirical mode decomposition (EMD). Through EMD, EEG signals are automatically decomposed into intrinsic mode functions (IMF). The multidimensional information of the IMF is used as features, the first difference of time series, the first phase difference and the normalized energy. These three characteristics are effective for emotion recognition. The role of each IMF is investigated and we find that the high frequency component IMF1 has a significant effect on the detection of different emotional states. Moreover, the classification accuracy of the proposed method uses several classical techniques, including support vector machines, quadratic classifiers, k-nearest neighbor, and neural networks. The experiment results demonstrate that our method can improve emotion recognition performance. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essayIntroductionEmotion plays an important role in our daily lives and work. Real-time assessment and regulation of emotions will improve and make people's lives better. For example, in human-computer interaction communication, recognizing emotions will make the process more simple and natural. As another example, in treating patients, especially those with speech problems, the actual emotional state of patients will help doctors provide more appropriate medical care. In recent years, EEG emotion recognition has attracted widespread attention. It is also a very important factor in brain-computer interface (BCI) systems, which will effectively improve communication between humans and machines [1]. Various features and extraction methods have been proposed for emotion recognition from EEG signals, including in the time domain. techniques, frequency domain techniques, joint time-frequency analysis techniques and other strategies. The statistics of EEG series, i.e., first and second difference, mean value and power, are usually used in the time domain [2]. Nonlinear features, including fractal dimension (FD) [3, 4], sample entropy [5], and nonstationary index [6], are used for emotion recognition. Petrantonakis and Tiadis Hadjileon introduced higher order crossover (HOC) features to capture the oscillatory pattern of EEG [10]. Wang et al. frequency domain features extracted for classification [11]. The time-frequency analysis is based on the spectrum of EEG signals; then the energy, power, power spectral density (PSD) and differential entropy [12] of some sub-bands are usually used as features. Short-time Fourier transform (STFT) [13, 14], Hilbert-Huang transform (HHT) [15, 16] and discrete wavelet transform (DWT) [17–19] are the most commonly used techniques for the calculation of the spectrum. It has been commonly tested and verified that higher frequency subbands such as Beta (16–32 Hz) and Gamma (32–64 Hz) bands outperform lower subbands for emotion recognition [ 20 , 21 ]. Other characteristics extracted from the electrode combination are also used,such as electrode coherence and asymmetry in different brain regions [22–24] and graph theory characteristics [25]. Jenké et al. had carried out a research comparing the performance of different features mentioned above and obtained a guiding rule for feature extraction and selection [26]. Some other strategies such as using deep network to improve classification performance have also been studied. Yang et al. hierarchical network used with subnetwork nodes for emotion recognition [28].EMD is proposed by Huang et al. in 1998 [29]. Unlike DWT, which must predetermine the transformation basis function and decomposition level, EMD can automatically decompose signals into IMF. These IMFs represent different frequency components of the original signals, with band-limited characteristics. By applying the Hilbert transform to the IMF, we can obtain instantaneous phase information from the IMF. EMD is therefore suitable for the analysis of non-linear and non-stationary sequences, such as neuronal signals. EMD has been widely used for seizure prediction and detection, but for EMD-based emotion recognition, there is not much research. Higher order statistics of IMF [30], geometric properties of IMF decomposed into a complex plane [31], and variation and fluctuation of IMF [32] are used as features for prediction and seizure detection. For emotion recognition, Mert and Akan extracted entropy, power, power spectral density, correlation, and asymmetry from IMF as features, then used independent component analysis (ICA) to reduce the size of the set of characteristics (33). Classification accuracy is calculated by mixing all subjects. In this paper, we present an emotion recognition method based on EMD. We use the first difference of the IMF time series, the first phase difference of the IMF, and the normalized energy of the IMF as features. The motivation for using these three features is that they describe the characteristics of the IMF in the time, frequency, and energy domains, providing multidimensional information. The first time series difference represents the intensity of the signal change in the time domain. The first phase difference measures the intensity of the phase change and the normalized energy describes the weight of the current oscillation component. The three features constitute a feature vector, which is fed into the SVM classifier for emotional state detection. The proposed method is studied on a publicly available emotional database DEAP [20]. The effectiveness of the three features is studied. Both IMF reduction and channel reduction for feature extraction are discussed, which aim to improve classification accuracy with less computational complexity. Performance is compared to other techniques including fractal dimension (FD), sample entropy, differential entropy, and time-frequency DWT analysis. MethodologyTo achieve emotional state recognition, EEG signals are decomposed into IMF by EMD. Three features of IMFs, phase fluctuation, time series fluctuation, and normalized energy, are formed as a feature vector, which is fed into SVM for classification. The whole process of the algorithm is shown in Figure 1. Data and materialsDEAP is a publicly available dataset for emotion analysis, which has.

Essay / The classification of emotion and empirical mode decomposition (EMD). discrete and emotion recognition based on empirical mode decomposition (EMD). Through EMD, EEG signals are automatically decomposed into intrinsic mode functions (IMF). The multidimensional information of the IMF is used as features, the first difference of time series, the first phase difference and the normalized energy. These three characteristics are effective for emotion recognition. The role of each IMF is investigated and we find that the high frequency component IMF1 has a significant effect on the detection of different emotional states. Moreover, the classification accuracy of the proposed method uses several classical techniques, including support vector machines, quadratic classifiers, k-nearest neighbor, and neural networks. The experiment results demonstrate that our method can improve emotion recognition performance. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essayIntroductionEmotion plays an important role in our daily lives and work. Real-time assessment and regulation of emotions will improve and make people's lives better. For example, in human-computer interaction communication, recognizing emotions will make the process more simple and natural. As another example, in treating patients, especially those with speech problems, the actual emotional state of patients will help doctors provide more appropriate medical care. In recent years, EEG emotion recognition has attracted widespread attention. It is also a very important factor in brain-computer interface (BCI) systems, which will effectively improve communication between humans and machines [1]. Various features and extraction methods have been proposed for emotion recognition from EEG signals, including in the time domain. techniques, frequency domain techniques, joint time-frequency analysis techniques and other strategies. The statistics of EEG series, i.e., first and second difference, mean value and power, are usually used in the time domain [2]. Nonlinear features, including fractal dimension (FD) [3, 4], sample entropy [5], and nonstationary index [6], are used for emotion recognition. Petrantonakis and Tiadis Hadjileon introduced higher order crossover (HOC) features to capture the oscillatory pattern of EEG [10]. Wang et al. frequency domain features extracted for classification [11]. The time-frequency analysis is based on the spectrum of EEG signals; then the energy, power, power spectral density (PSD) and differential entropy [12] of some sub-bands are usually used as features. Short-time Fourier transform (STFT) [13, 14], Hilbert-Huang transform (HHT) [15, 16] and discrete wavelet transform (DWT) [17–19] are the most commonly used techniques for the calculation of the spectrum. It has been commonly tested and verified that higher frequency subbands such as Beta (16–32 Hz) and Gamma (32–64 Hz) bands outperform lower subbands for emotion recognition [ 20 , 21 ]. Other characteristics extracted from the electrode combination are also used,such as electrode coherence and asymmetry in different brain regions [22–24] and graph theory characteristics [25]. Jenké et al. had carried out a research comparing the performance of different features mentioned above and obtained a guiding rule for feature extraction and selection [26]. Some other strategies such as using deep network to improve classification performance have also been studied. Yang et al. hierarchical network used with subnetwork nodes for emotion recognition [28].EMD is proposed by Huang et al. in 1998 [29]. Unlike DWT, which must predetermine the transformation basis function and decomposition level, EMD can automatically decompose signals into IMF. These IMFs represent different frequency components of the original signals, with band-limited characteristics. By applying the Hilbert transform to the IMF, we can obtain instantaneous phase information from the IMF. EMD is therefore suitable for the analysis of non-linear and non-stationary sequences, such as neuronal signals. EMD has been widely used for seizure prediction and detection, but for EMD-based emotion recognition, there is not much research. Higher order statistics of IMF [30], geometric properties of IMF decomposed into a complex plane [31], and variation and fluctuation of IMF [32] are used as features for prediction and seizure detection. For emotion recognition, Mert and Akan extracted entropy, power, power spectral density, correlation, and asymmetry from IMF as features, then used independent component analysis (ICA) to reduce the size of the set of characteristics (33). Classification accuracy is calculated by mixing all subjects. In this paper, we present an emotion recognition method based on EMD. We use the first difference of the IMF time series, the first phase difference of the IMF, and the normalized energy of the IMF as features. The motivation for using these three features is that they describe the characteristics of the IMF in the time, frequency, and energy domains, providing multidimensional information. The first time series difference represents the intensity of the signal change in the time domain. The first phase difference measures the intensity of the phase change and the normalized energy describes the weight of the current oscillation component. The three features constitute a feature vector, which is fed into the SVM classifier for emotional state detection. The proposed method is studied on a publicly available emotional database DEAP [20]. The effectiveness of the three features is studied. Both IMF reduction and channel reduction for feature extraction are discussed, which aim to improve classification accuracy with less computational complexity. Performance is compared to other techniques including fractal dimension (FD), sample entropy, differential entropy, and time-frequency DWT analysis. MethodologyTo achieve emotional state recognition, EEG signals are decomposed into IMF by EMD. Three features of IMFs, phase fluctuation, time series fluctuation, and normalized energy, are formed as a feature vector, which is fed into SVM for classification. The whole process of the algorithm is shown in Figure 1. Data and materialsDEAP is a publicly available dataset for emotion analysis, which has.

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