MetaEmotionNet: Spatial–Spectral–Temporal-Based Attention 3-D Dense Network With Meta-Learning for EEG Emotion Recognition

Ning, Xiaojun; Wang, Jing; Lin, Youfang; Cai, Xiyang; Chen, Haobin; Gou, Haijun; Li, Xiaoli; Jia, Ziyu

doi:10.1109/tim.2023.3338676

Cited by 10 publications

(4 citation statements)

References 63 publications

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“…However, in the biomedical field, manually annotated labels are costly and noisy, and annotating large-scale data is time-consuming (Liu S. et al, 2023 ; Ning et al, 2023 ). With the development in medical research, it is becoming easier to acquire large volumes of physiological signal data.…”

Section: Self-supervised Learning-based Emotional Recognition Modelmentioning

confidence: 99%

Self supervised learning based emotion recognition using physiological signals

Zhang,

Cui

2024

Front. Hum. Neurosci.

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IntroductionThe significant role of emotional recognition in the field of human-machine interaction has garnered the attention of many researchers. Emotion recognition based on physiological signals can objectively reflect the most authentic emotional states of humans. However, existing labeled Electroencephalogram (EEG) datasets are often of small scale.MethodsIn practical scenarios, a large number of unlabeled EEG signals are easier to obtain. Therefore, this paper adopts self-supervised learning methods to study emotion recognition based on EEG. Specifically, experiments employ three pre-defined tasks to define pseudo-labels and extract features from the inherent structure of the data.Results and discussionExperimental results indicate that self-supervised learning methods have the capability to learn effective feature representations for downstream tasks without any manual labels.

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Section: Self-supervised Learning-based Emotional Recognition Modelmentioning

confidence: 99%

Self supervised learning based emotion recognition using physiological signals

Zhang,

Cui

2024

Front. Hum. Neurosci.

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“…In addition, children with mTBIs were four times more likely to have a new anxiety diagnosis compared to orthopedic controls [ 14 ]. Multiple studies explore the psychological effects of mTBI; however, these are not well described [ 16 , 17 , 18 ]. More recently, emotion recognition has become prevalent in affective computing [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…Multiple studies explore the psychological effects of mTBI; however, these are not well described [ 16 , 17 , 18 ]. More recently, emotion recognition has become prevalent in affective computing [ 17 ]. However, this is still in its early stages.…”

Section: Introductionmentioning

confidence: 99%

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Anxiety and Mood Disruption in Collegiate Athletes Acutely Following Mild Traumatic Brain Injury

Zhang,

Martyna,

Cornwell

et al. 2024

Diagnostics

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Objective: To report the symptom burden of anxiety and mood-related indicators following mTBI in collegiate student-athletes. Study Design: Retrospective cohort study of varsity collegiate athletes. Setting: University sports medicine at a tertiary care center. Patients: Division I college varsity athletes diagnosed with mTBI at a single institution between 2016 and 2019. Independent Variables: Pre- and post-injury. Main Outcome Measures: Comparisons between baseline testing and post-mTBI symptom scale assessments were made to determine changes in scores at the individual and group levels. The primary outcome was the prevalence of post-mTBI symptoms from within 72 h of injury through return to play. Associations with sport, sex, age, and return-to-play time were included. Results: Compared to baseline, mood and anxiety symptom scores were significantly higher acutely following mTBI (2.1 ± 3.3 vs. 14.3 ± 12.2; p < 0.001). A family history of migraine was significantly associated with higher mood and anxiety symptom scores (20.0 ± 14.9 with history vs. 13.3 ± 11.3 without history; p = 0.042). Mood and anxiety symptom scores were highly correlated with non-mood and anxiety symptom scores for all athletes, including the subgroup with prolonged symptoms (r = 0.769; p < 0.001). Conclusions: Symptoms of anxiety or mood disruption are common during the acute period post-injury in varsity college athletes. Risk factors for higher symptom reports immediately following mTBI and for prolonged symptoms (>10 days) included female sex, those with a family history of migraine, and those with an overall higher symptom burden post-injury.

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A new EEG-based schizophrenia diagnosis method through a fuzzy DL model

Yang

2024

Multiscale and Multidiscip. Model. Exp. and Des.

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MetaEmotionNet: Spatial–Spectral–Temporal-Based Attention 3-D Dense Network With Meta-Learning for EEG Emotion Recognition

Cited by 10 publications

References 63 publications

Self supervised learning based emotion recognition using physiological signals

Self supervised learning based emotion recognition using physiological signals

Anxiety and Mood Disruption in Collegiate Athletes Acutely Following Mild Traumatic Brain Injury

A new EEG-based schizophrenia diagnosis method through a fuzzy DL model

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