Emotion discrimination using source connectivity analysis based on dynamic ROI identification

Kouti, Mayadeh; Ansari-Asl, Karim; Namjoo, Ehsan

doi:10.1016/j.bspc.2021.103332

Cited by 6 publications

(4 citation statements)

References 79 publications

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“…Lee et al classified the emotional states of 40 subjects who watched the video by assessing the functional brain connectivity of EEG signals using three connectivity metrics: correlation, coherence, and phase synchronization 25 . Kouti et al proposed a source connectivity‐based approach for emotion recognition and considered three connection measures for emotion classification, including imaginary part of coherency (iCoh), PLV, and phase lag index (PLI) 26 …”

Section: Related Workmentioning

confidence: 99%

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Dynamic differential entropy and brain connectivity features based EEG emotion recognition

Zheng

et al. 2022

Int J of Intelligent Sys

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Emotion recognition has become a research focus in the brain–computer interface and cognitive neuroscience. Electroencephalogram (EEG) is employed for its advantages as accurate, objective, and noninvasive nature. However, many existing research only focus on extracting the time and frequency domain features of the EEG signals while failing to utilize the dynamic temporal changes and the positional relationships between different electrode channels. To fill this gap, we develop the dynamic differential entropy and brain connectivity features based EEG emotion recognition using linear graph convolutional network named DDELGCN. First, the dynamic differential entropy feature which represents the frequency domain feature as well as time domain feature is extracted based on the traditional differential entropy feature. Second, brain connectivity matrices are constructed by calculating the Pearson correlation coefficient, phase‐locked value and transfer entropy, and then are used to denote the connectivity features of all electrode combinations. Finally, a linear graph convolutional network is customized and applied to aggregate the features from total electrode combinations and then classifies the emotional states, which consists of five layers, namely, an input layer, two linear graph convolutional layers, a fully connected layer, and a softmax layer. Extensive experiments show that the accuracies in the valence and arousal dimensions reach 90.88% and 91.13%, and the precision reaches 96.66% and 97.02% on the DEAP dataset, respectively. On the SEED dataset, the accuracy and precision reach 91.56% and 97.38%, respectively.

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Section: Related Workmentioning

confidence: 99%

“…25 Kouti et al proposed a source connectivity-based approach for emotion recognition and considered three connection measures for emotion classification, including imaginary part of coherency (iCoh), PLV, and phase lag index (PLI). 26…”

Section: Eeg Featuresmentioning

confidence: 99%

Dynamic differential entropy and brain connectivity features based EEG emotion recognition

Zheng

et al. 2022

Int J of Intelligent Sys

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“…These limitations have been noted in previous research. To address the limitations of sensor signals in functional connectivity studies, two types of EEG source reconstruction approaches have been applied to infer the directional connections between brain sources [27][28][29]. The first method uses a biophysical generative model to infer functional connectivity between sources directly from the sensor data.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Functional Brain Network of Deception in Source-Level EEG via Partial Mutual Information

Kang

Gao

2023

Electronics

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In this study, partial mutual information at the source level was used to construct brain functional networks in order to examine differences in brain functions between lying and honest responses. The study used independent component analysis and clustering methods to computationally generate source signals from EEG signals recorded from subjects who were lying and those who were being honest. Partial mutual information was calculated between regions of interest (ROIs), and used to construct a functional brain network with ROIs as nodes and partial mutual information values as connections between them. The partial mutual information connections that showed significant differences between the two groups of people were selected as the feature set and classified using a functional connectivity network (FCN) classifier, resulting in an accuracy of 88.5%. Analysis of the brain networks of the lying and honest groups showed that, in the lying state, there was increased informational exchange between the frontal lobe and temporal lobe, and the language motor center of the frontal lobe exchanged more information with other brain regions, suggesting increased working and episodic memory load and the mobilization of more cognitive resources.

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“…, fog computing (Thoumi & Haraty, 2022), the Internet of Things (Chamra & Harmanani, 2020), smart homes (Madhu et al, 2022;Guebli & Belkhir, 2021), intelligent communication (Samir et al, 2020) and other fields, the application scope of human emotion recognition is becoming increasingly widespread. Therefore, conveniently, effectively, and accurately recognizing human emotions is significantly important for promoting the development of new eras such as artificial intelligence, Web 3.0, and the metaverse (Kouti et al, 2022).…”

mentioning

confidence: 99%

Channel Semantic Enhancement-Based Emotional Recognition Method Using SCLE-2D-CNN

Fu,

Yang,

Pan

2024

International Journal on Semantic Web and Information Systems

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The existing EEG emotion classification methods have some problems, such as insufficient emotion representation and lack of targeted channel enhancement module due to feature redundancy. To this end, a novel EEG emotion recognition method (SCLE-2D-CNN) combining scaled convolutional layer (SCLs), enhanced channel module and two-dimensional convolutional neural network (2D-CNN) is proposed. Firstly, the time-frequency features of multi-channel EEG emotional signals were extracted by stacking scl layer by layer. Secondly, channel enhancement module is used to reassign different importance to all EEG physical channels. Finally, 2D-CNN was used to obtain deep local spatiotemporal features and complete emotion classification. The experimental results show that the accuracy of SEED data set and F1 are 98.09% and 97.00%, respectively, and the binary classification accuracy of DEAP data set is 98.06% and 96.83%, respectively, which are superior to other comparison methods. The proposed method has a certain application prospect in the recognition of human mental state.

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Emotion discrimination using source connectivity analysis based on dynamic ROI identification

Cited by 6 publications

References 79 publications

Dynamic differential entropy and brain connectivity features based EEG emotion recognition

Dynamic differential entropy and brain connectivity features based EEG emotion recognition

Exploring the Functional Brain Network of Deception in Source-Level EEG via Partial Mutual Information

Channel Semantic Enhancement-Based Emotional Recognition Method Using SCLE-2D-CNN

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