EEG emotion recognition using multichannel weighted multiscale permutation entropy

Wang, Zhongmin; Zhang, Jiawen; He, Yan; Zhang, Jie

doi:10.1007/s10489-021-03070-2

Cited by 19 publications

(6 citation statements)

References 52 publications

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“…where f is the actual frequency of the speech signal. Wang et al (2022), the multi-resolution idea of wavelet analysis is combined with different forms of TEO and MFCC, and five non-linear features are proposed for speech emotion recognition. Qadri et al (2022) proposed that Teager-energy based MFCC (TEMFCCs) was classified on Berlin database by Gaussian mixture model (GMM), and experimental results showed that TEMFCCs had better performance than MFCC.…”

Section: Features Of Dance Emotionmentioning

confidence: 99%

Dance emotion recognition based on linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory from robot environment

Shen

Xiao-xi²,

Jiang³

et al. 2022

Front. Neurorobot.

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Dance emotion recognition is an important research direction of automatic speech recognition, especially in the robot environment. It is an important research content of dance emotion recognition to extract the features that best represent speech emotion and to construct an acoustic model with strong robustness and generalization. The dance emotion data set is small in size and high in dimension. The traditional recurrent neural network (RNN) has the problem of long-range dependence disappearance, and due to the focus on local information of convolutional neural network (CNN), the mining of potential relationships between frames in the input sequence is insufficient. To solve the above problems, this paper proposes a novel linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory (LSTM) for dance emotion recognition. In this paper, the linear prediction coefficient (LPC) and Meier frequency cepstrum coefficient (MFCC) are combined to obtain a new feature, namely the linear prediction Meier frequency cepstrum coefficient (LPMFCC). Then, the combined feature obtained by combining LPMFCC with energy feature is used as the extracted dance feature. The extracted features are input into the bidirectional LSTM network for training. Finally, support vector machine (SVM) is used to classify the obtained features through the full connection layer. Finally, we conduct experiments on public data sets and obtain the better effectiveness compared with the state-of-art dance motion recognition methods.

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Section: Features Of Dance Emotionmentioning

confidence: 99%

Dance emotion recognition based on linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory from robot environment

Shen

Xiao-xi²,

Jiang³

et al. 2022

Front. Neurorobot.

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“…However, the physical sciences have indicated that numerous physiological components function at various time scales [30,31], suggesting the importance of multiscale information for EEG signals. To this end, there exist some other studies which focus on multiscale EEG features-based emotion recognition, where multiscale permutation entropy (MPE) and multiscale convolutional kernels are commonly used [32][33][34]. Overall, there is rare exploration on the views of both multiscale information and spatial relationships.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Interaction in Multiscale Multichannel EEG Signals for Emotion Recognition

Guo,

Zhang,

Fan

et al. 2024

Mathematics

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Electroencephalogram (EEG) is the most preferred and credible source for emotion recognition, where long-short range features and a multichannel relationship are crucial for performance because numerous physiological components function at various time scales and on different channels. We propose a cascade scale-aware adaptive graph convolutional network and cross-EEG transformer (SAG-CET) to explore the comprehensive interaction between multiscale and multichannel EEG signals with two novel ideas. First, to model the relationship of multichannel EEG signals and enhance signal representation ability, the multiscale EEG signals are fed into a scale-aware adaptive graph convolutional network (SAG) before the CET model. Second, the cross-EEG transformer (CET), is used to explicitly capture multiscale features as well as their correlations. The CET consists of two self-attention encoders for gathering features from long-short time series and a cross-attention module to integrate multiscale class tokens. Our experiments show that CET significantly outperforms a vanilla unitary transformer, and the SAG module brings visible gains. Our methods also outperform state-of-the-art methods in subject-dependent tasks with 98.89%/98.92% in accuracy for valence/arousal on DEAP and 99.08%/99.21% on DREAMER.

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“…This model considers the biological topology between different brain regions to capture local and global relationships among different EEG signal channels, and the experimental results demonstrate the importance of global connectivity when modeling differential asymmetry in electroencephalography. Wang et al proposed a feature fusion method that can effectively reflect emotional states [ 15 ]. This method is characterized by multichannel weighted multiscale permutation entropy, which considers the time–frequency and spatial information of EEG signals and eliminates the inherent volume effect of EEG.…”

Section: Introductionmentioning

confidence: 99%

EEG Emotion Recognition Network Based on Attention and Spatiotemporal Convolution

Zhu,

Liu,

Zhao

et al. 2024

Sensors

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Human emotions are complex psychological and physiological responses to external stimuli. Correctly identifying and providing feedback on emotions is an important goal in human–computer interaction research. Compared to facial expressions, speech, or other physiological signals, using electroencephalogram (EEG) signals for the task of emotion recognition has advantages in terms of authenticity, objectivity, and high reliability; thus, it is attracting increasing attention from researchers. However, the current methods have significant room for improvement in terms of the combination of information exchange between different brain regions and time–frequency feature extraction. Therefore, this paper proposes an EEG emotion recognition network, namely, self-organized graph pesudo-3D convolution (SOGPCN), based on attention and spatiotemporal convolution. Unlike previous methods that directly construct graph structures for brain channels, the proposed SOGPCN method considers that the spatial relationships between electrodes in each frequency band differ. First, a self-organizing map is constructed for each channel in each frequency band to obtain the 10 most relevant channels to the current channel, and graph convolution is employed to capture the spatial relationships between all channels in the self-organizing map constructed for each channel in each frequency band. Then, pseudo-three-dimensional convolution combined with partial dot product attention is implemented to extract the temporal features of the EEG sequence. Finally, LSTM is employed to learn the contextual information between adjacent time-series data. Subject-dependent and subject-independent experiments are conducted on the SEED dataset to evaluate the performance of the proposed SOGPCN method, which achieves recognition accuracies of 95.26% and 94.22%, respectively, indicating that the proposed method outperforms several baseline methods.

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EEG emotion recognition using multichannel weighted multiscale permutation entropy

Cited by 19 publications

References 52 publications

Dance emotion recognition based on linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory from robot environment

Dance emotion recognition based on linear predictive Meir frequency cepstrum coefficient and bidirectional long short-term memory from robot environment

A Comprehensive Interaction in Multiscale Multichannel EEG Signals for Emotion Recognition

EEG Emotion Recognition Network Based on Attention and Spatiotemporal Convolution

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