Long-range correlation analysis of high frequency prefrontal electroencephalogram oscillations for dynamic emotion recognition

Gao, Zhilin; Cui, Xingran; Wan, Wang; Zheng, Wenming; Gu, Zhongze

doi:10.1016/j.bspc.2021.103291

Cited by 11 publications

(21 citation statements)

References 55 publications

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“…In Zeng et al (2021) , the authors proposed an emotional wheel attention-based emotion distribution learning model (EWAEDL). In Gao et al (2022) , the authors proposed a novel refined-detrended fluctuation analysis method, that was, multi-order detrended fluctuation analysis (MODFA). In Han et al (2021) , the authors proposed a novel cross-modal emotion embedding framework, called EmoBed, which aimed to improve the performance of existing emotion recognition systems by using the knowledge from other auxiliary patterns.…”

Section: Related Workmentioning

confidence: 99%

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Metaverse-Powered Experiential Situational English-Teaching Design: An Emotion-Based Analysis Method

Guo

Gao

2022

Front. Psychol.

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Metaverse is to build a virtual world that is both mapped and independent of the real world in cyberspace by using the improvement in the maturity of various digital technologies, such as virtual reality (VR), augmented reality (AR), big data, and 5G, which is important for the future development of a wide variety of professions, including education. The metaverse represents the latest stage of the development of visual immersion technology. Its essence is an online digital space parallel to the real world, which is becoming a practical field for the innovation and development of human society. The most prominent advantage of the English-teaching metaverse is that it can provide an immersive and interactive teaching field for teachers and students, simultaneously meeting the teaching and learning needs of teachers and students in both the physical world and virtual world. This study constructs experiential situational English-teaching scenario and convolutional neural networks (CNNs)–recurrent neural networks (RNNs) fusion models are proposed to recognize students’ emotion electroencephalogram (EEG) in experiential English teaching during the feature space of time domain, frequency domain, and spatial domain. Analyzing EEG data collected by OpenBCI EEG Electrode Cap Kit from students, experiential English-teaching scenario is designed into three types: sequential guidance, comprehensive exploration, and crowd-creation construction. Experimental data analysis of the three kinds of learning activities shows that metaverse-powered experiential situational English teaching can promote the improvement of students’ sense of interactivity, immersion, and cognition, and the accuracy and analysis time of CNN–RNN fusion model is much higher than that of baselines. This study can provide a nice reference for the emotion recognition of students under COVID-19.

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Section: Related Workmentioning

confidence: 99%

“…The experiment was driven on the SEED-IV dataset, using different movie clips as a library of four emotions (peace, sadness, fear, and happiness) for a total of 15 participants. EWAEDL ( Zeng et al, 2021 ), MODFA ( Gao et al, 2022 ), EmoBed ( Han et al, 2021 ), and the proposed method were compared on the SEED-IV dataset.…”

Section: Experiments and Analysismentioning

confidence: 99%

Metaverse-Powered Experiential Situational English-Teaching Design: An Emotion-Based Analysis Method

Guo

Gao

2022

Front. Psychol.

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“…Although it is difficult to distinguish particular emotions, the difference between happy and negative emotions (such as fear, sadness, anger, and disgust) is distinct. Frequency-based binary emotion classification (positive and negative) can achieve 96.81% accuracy [ 89 ]. Therefore, based on brain activation profiles, emotional valence is positive during lower frequencies, and negative amid information-heavy higher frequencies [ 89 , 90 , 91 ].…”

Section: Thermodynamic Regulation Of the Neural Systemmentioning

confidence: 99%

“…Frequency-based binary emotion classification (positive and negative) can achieve 96.81% accuracy [ 89 ]. Therefore, based on brain activation profiles, emotional valence is positive during lower frequencies, and negative amid information-heavy higher frequencies [ 89 , 90 , 91 ].…”

Section: Thermodynamic Regulation Of the Neural Systemmentioning

confidence: 99%

“…The brain’s energy/information flow is also frequency-dependent [ 92 , 93 ]. Recent emotion studies underline the cumulative effects of negative emotion (i.e., fear, sadness, anger, and disgust) in the frontal lobe, which is entirely missing for neutral and happy sentiments [ 89 ] ( Figure 3 ). For example, remorse and guilt invariably follow mistakes.…”

Section: Thermodynamic Regulation Of the Neural Systemmentioning

confidence: 99%

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How the Brain Becomes the Mind: Can Thermodynamics Explain the Emergence and Nature of Emotions?

Deli

Peters

Kisvárday

2022

Entropy

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The neural systems’ electric activities are fundamental for the phenomenology of consciousness. Sensory perception triggers an information/energy exchange with the environment, but the brain’s recurrent activations maintain a resting state with constant parameters. Therefore, perception forms a closed thermodynamic cycle. In physics, the Carnot engine is an ideal thermodynamic cycle that converts heat from a hot reservoir into work, or inversely, requires work to transfer heat from a low- to a high-temperature reservoir (the reversed Carnot cycle). We analyze the high entropy brain by the endothermic reversed Carnot cycle. Its irreversible activations provide temporal directionality for future orientation. A flexible transfer between neural states inspires openness and creativity. In contrast, the low entropy resting state parallels reversible activations, which impose past focus via repetitive thinking, remorse, and regret. The exothermic Carnot cycle degrades mental energy. Therefore, the brain’s energy/information balance formulates motivation, sensed as position or negative emotions. Our work provides an analytical perspective of positive and negative emotions and spontaneous behavior from the free energy principle. Furthermore, electrical activities, thoughts, and beliefs lend themselves to a temporal organization, an orthogonal condition to physical systems. Here, we suggest that an experimental validation of the thermodynamic origin of emotions might inspire better treatment options for mental diseases.

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Novel multimodal emotion detection method using Electroencephalogram and Electrocardiogram signals

Saha,

Ansaruddin Kunju,

Majid

et al. 2024

Biomedical Signal Processing and Control

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Long-range correlation analysis of high frequency prefrontal electroencephalogram oscillations for dynamic emotion recognition

Cited by 11 publications

References 55 publications

Metaverse-Powered Experiential Situational English-Teaching Design: An Emotion-Based Analysis Method

Metaverse-Powered Experiential Situational English-Teaching Design: An Emotion-Based Analysis Method

How the Brain Becomes the Mind: Can Thermodynamics Explain the Emergence and Nature of Emotions?

Novel multimodal emotion detection method using Electroencephalogram and Electrocardiogram signals

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