Multi-domain fusion deep graph convolution neural network for EEG emotion recognition

Bi, Jinying; Wang, Fei; Yan, Xin; Ping, Jingyu; Wen, Yongzhao

doi:10.1007/s00521-022-07643-1

Cited by 14 publications

(3 citation statements)

References 44 publications

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“…The authors of the study [43] found that connectivity features extracted from signals from high-frequency bands are more informative than signals from low-frequency bands. The study [78] also used the DEAP dataset to classify emotion from EEG signals from five sub-frequency bands and the full-frequency band and showed that recognition accuracy in the Gamma band was higher than in the full-frequency band and other lower-frequency bands.…”

Section: B Er Performance In Different Bandsmentioning

confidence: 99%

A Comparative Study on Prominent Connectivity Features for Emotion Recognition From EEG

et al. 2023

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Classifying distinct human emotions, the fundamental purpose of brain-computer interface research, is essential for providing instant personalized services and assistance to individuals. With such emerging applications for individuals, several techniques have been proposed recently to explore interactions between brain regions, such as correlation, synchronization, and dependence. Notably, functional and effective connectivity methods are applied to assess the relationships between different brain areas. The primary objective of this study is to compare the frequently used functional and effective connectivity methods to recognize emotion using Electroencephalogram (EEG) signals. This paper uses a benchmark emotional EEG dataset consisting of 32 channels of EEG signals collected from 32 subjects while they were watching 40 inspirational music videos. Specifically, correlation, phase synchronization, and mutual information are used to measure functional brain connectivity, and transfer entropy is used to acquire effective brain connectivity. After extracting the features, they are represented in a two-dimensional connectivity feature map (CFM). The CFMs are then used to classify emotions by a convolutional neural network model. The results of classified emotions are analyzed regarding compatible EEG bands, accuracy, and time. Notably, the Gamma band is found as the most compatible band. The comparative study has demonstrated that though the connectivity method named Pearson correlation coefficient requires less time, the normalized mutual information is the most accurate method with advantageous detecting capability of nonlinear dependencies.

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Section: B Er Performance In Different Bandsmentioning

confidence: 99%

A Comparative Study on Prominent Connectivity Features for Emotion Recognition From EEG

et al. 2023

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“…In order to further understand the specific emotional states of the brain and consider the correlations and interactions between channels, the research method of constructing inter-channel adjacency matrices using spatial distances of EEG channels [ 28 ] and functional connectivity metrics has also been gradually adopted by most researchers. Functional connectivity metrics are usually selected such as the phase-locked value PLV [ 29 ], Pearson correlation coefficient PCC [ 30 ], etc. However, in most of the previous studies, the extraction of the adjacency matrix was based on a priori knowledge, which did not fully consider the correlation relationship between channels embedded in the EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Subject-Independent Emotion Recognition Based on EEG Frequency Band Features and Self-Adaptive Graph Construction

Zhang,

Hao,

Wen

et al. 2024

Brain Sciences

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Emotion is one of the most important higher cognitive functions of the human brain and plays an important role in transaction processing and decisions. In traditional emotion recognition studies, the frequency band features in EEG signals have been shown to have a high correlation with emotion production. However, traditional emotion recognition methods cannot satisfactorily solve the problem of individual differences in subjects and data heterogeneity in EEG, and subject-independent emotion recognition based on EEG signals has attracted extensive attention from researchers. In this paper, we propose a subject-independent emotion recognition model based on adaptive extraction of layer structure based on frequency bands (BFE-Net), which is adaptive in extracting EEG map features through the multi-graphic layer construction module to obtain a frequency band-based multi-graphic layer emotion representation. To evaluate the performance of the model in subject-independent emotion recognition studies, extensive experiments are conducted on two public datasets including SEED and SEED-IV. The experimental results show that in most experimental settings, our model has a more advanced performance than the existing studies of the same type. In addition, the visualization of brain connectivity patterns reveals that some of the findings are consistent with previous neuroscientific validations, further validating the model in subject-independent emotion recognition studies.

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“…Wu et al (2022) proposed a generalized zero-shot learning framework composed of two branches of a hierarchical prototype network and a semantic auto-encoder, which can recognize body gesture categories by semantic information, and predict emotions through gestures. As the internal signal source, physiological signals are more authentic, reliable and accessible when describing emotions (Bi et al 2022), therefore many scholars focus on emotion recognition based on physiological signals. Cho and Hwang (2020) proposed the spatio-temporal representations of electroencephalogram (EEG) signals for emotion identification based on 3D-CNN, and achieved a classification accuracy of 99.11% and 99.74% in the binary classification of valence and arousal, respectively.…”

Section: Introductionmentioning

confidence: 99%

Cross-subject emotion recognition using hierarchical feature optimization and support vector machine with multi-kernel collaboration

Pan,

Tang,

Wang

et al. 2023

Physiol. Meas.

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Objective. Due to individual differences, it is greatly challenging to realize the multiple types of emotion identification across subjects. Approach. In this research, a hierarchical feature optimization method is proposed in order to represent emotional states effectively based on peripheral physiological signals. Firstly, sparse learning combined with binary search is employed to achieve feature selection of single signals. Then an improved fast correlation-based filter is proposed to implement fusion optimization of multi-channel signal features. Aiming at overcoming the limitations of the support vector machine (SVM), which uses a single kernel function to make decisions, the multi-kernel function collaboration strategy is proposed to improve the classification performance of SVM. Main results. The effectiveness of the proposed method is verified on the DEAP dataset. Experimental results show that the proposed method presents a competitive performance for four cross-subject types of emotion identification with an accuracy of 84% (group 1) and 85.07% (group 2). Significance. The proposed model with hierarchical feature optimization and SVM with multi-kernel function collaboration demonstrates superior emotion recognition accuracy compared to state-of-the-art techniques. In addition, the analysis based on DEAP dataset composition characteristics presents a novel perspective to explore the emotion recognition issue more objectively and comprehensively.

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Multi-domain fusion deep graph convolution neural network for EEG emotion recognition

Cited by 14 publications

References 44 publications

A Comparative Study on Prominent Connectivity Features for Emotion Recognition From EEG

A Comparative Study on Prominent Connectivity Features for Emotion Recognition From EEG

Subject-Independent Emotion Recognition Based on EEG Frequency Band Features and Self-Adaptive Graph Construction

Cross-subject emotion recognition using hierarchical feature optimization and support vector machine with multi-kernel collaboration

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