Emotional Analysis and Recognition Based on EEG Brain Network

Deng, Hongxia; Qian, Guanyu; Zhang, Yuefang; Hu, Chunxiang; Liu, Ying; Li, Haifang

doi:10.1109/cbd51900.2020.00019

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…For example, Jalili et al constructed EEG BN using partial correlation (PC) and then investigated its small-world, vulnerability, modularity, assortativity and synchronizability to distinguish schizophrenia patients from healthy controls (HC) [11]. Deng et al constructed BN based on the phase locking value (PLV) of each pair of EEG signals, and then extracted their corresponding topological attributes for emotion recognition [12]. Nicolaou et al calculated the Granger causality (GC) of each pair of EEG signals to construct BN in order to automatically classify the states of general anesthesia patients between awake and anesthesia [13].…”

Section: B Background Description B1 Multidimensional Eegmentioning

confidence: 99%

Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals

Han

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Objective: Multivariate signal (MS) analysis, especially the assessment of its information transmission (for example, from the perspective of network science), is the key to our understanding of various phenomena in biology, physics and economics. Although there is a large amount of literature demonstrating that MS can be decomposed into space-timefrequency domain information, there seems to be no research confirming that multivariate information transmission (MIT) in these three domains can be quantified. Therefore, in this study, we attempted to combine dynamic mode decomposition (DMD) and parallel communication model (PCM) together to realize it. Methods: We first regarded MS as a large-scale system and then used DMD to decompose it into specific subsystems with their own intrinsic oscillatory frequencies. At the same time, the transition probability matrix (TPM) of information transmission within and between MS at two consecutive moments in each subsystem can also be calculated. Then, communication parameters (CPs) derived from each TPM were calculated in order to quantify the MIT in the space-time-frequency domain. In this study, multidimensional electroencephalogram (EEG) signals were used to illustrate our method. Results: Compared with traditional EEG brain networks, this method shows greater potential in EEG analysis to distinguish between patients and healthy controls. Conclusion:This study demonstrates the feasibility of measuring MIT in the space-time-frequency domain simultaneously. Significance: This study shows that MIT analysis in the spacetime-frequency domain is not only completely different from the MS decomposition in these three domains, but also can reveal many new phenomena behind MS that have not yet been discovered.

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Section: B Background Description B1 Multidimensional Eegmentioning

confidence: 99%

Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals

Han

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Therefore, it is necessary for teachers to understand and adjust students' learning emotions in a timely manner to achieve optimal learning outcomes [6][7][8][9][10][11]. The smart classroom is an important product of new education technology, which uses the latest computer, communication, and Internet technologies to achieve deep perception, intelligent analysis, and accurate delivery of the education process [12][13][14][15][16][17]. Among them, emotion recognition technology is one of the important applications of artificial intelligence and machine learning in the field of education [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition in Learning Scenes Supported by Smart Classroom and Its Application

Zhu,

Zheng,

et al. 2023

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Emotion recognition technology is one of the important applications of artificial intelligence and machine learning in the field of education. By recognizing the emotions of students in learning scenes, teachers can better understand the learning status of students and provide them with personalized learning resources and help. Current emotion recognition methods are mainly based on static facial emotions, neglecting the temporal features of facial emotions, which may lead to inaccurate recognition results. In order to overcome these challenges, this study conducts research on emotion recognition and its application in learning scenes supported by smart classrooms. The Transformer encoder is used to extract the temporal features of student facial emotions based on learning scenes, i.e., the selfattention module of the encoder is used to extract the temporal features of facial emotions in learning scenes. Residual attention networks, Transformers, and non-local neural networks are used to extract facial emotion features from different perspectives and levels. The combination of Vision-Transformer (ViT) and NetVLAD enables the model to learn the features of data from multiple perspectives, thereby improving the generalization ability of the model. The experimental results verify the effectiveness of the constructed model.

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Constructing Bodily Emotion Maps Based on High-Density Body Surface Potentials for Psychophysiological Computing

Hu,

Bian,

Huang

et al. 2024

IEEE J. Biomed. Health Inform.

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Emotional Analysis and Recognition Based on EEG Brain Network

Cited by 3 publications

References 20 publications

Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals

Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals

Emotion Recognition in Learning Scenes Supported by Smart Classroom and Its Application

Constructing Bodily Emotion Maps Based on High-Density Body Surface Potentials for Psychophysiological Computing

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