Video‐triggered EEG‐emotion public databases and current methods: A survey

Hu, Wanrou; Huang, Gan; Li, Linling; Zhang, Li; Zhang, Zhiguo; Liang, Zhen

doi:10.26599/bsa.2020.9050026

Cited by 41 publications

(23 citation statements)

References 122 publications

(167 reference statements)

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“…Each university will establish various business departments according to different functional divisions. Each department's business systems are relatively independent and connected, such as grades and course selection data from the Registrar's Office, consumption, and access control data from the Logistics Department [18]. erefore, before mining multidimensional student data, it is necessary to physically integrate the data from different data sources with different storage media and different data formats, a process called data integration.…”

Section: Svm Multidimensional State Data Reduction Evaluationmentioning

confidence: 99%

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

Peiqing

2022

Journal of Mathematics

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In response to the shortcomings of the traditional methods for evaluating the mental health status of college students in terms of computational complexity and low accuracy, a method for evaluating the mental health status of college students based on data reduction and support vector machines was proposed. A model experiment containing internal and external personality tendency classification, anxiety, and depression dichotomy was designed using logistic regression analysis, information entropy, and SVM algorithm to construct the feature dimensions of the network behavior data, combined with the labeled data of mental state to derive the sample data set for model experiments. In the experimental process, to reflect the difference in the effect of different models, various types of mathematical models were constructed for horizontal comparison; at the same time, to reflect the influence of the parameters of the same type of model, different combinations of parameters were constructed using a grid search algorithm to vertically compare the difference in the effect. The average accuracy of the dichotomous model for anxiety and depression in the sample of 1433 students was 0.80 or higher. The experiments show that the method of predicting students’ psychological status through their online behavioral data is feasible, and the mathematical classification model can be used to grasp students’ psychological status in real time and to warn students with abnormal psychological status, thus helping school counselors to intervene and prevent them promptly.

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Section: Svm Multidimensional State Data Reduction Evaluationmentioning

confidence: 99%

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

Peiqing

2022

Journal of Mathematics

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“…Video induction (Soleymani et al, 2014;Huo et al, 2016;Zheng and Lu, 2016;Hu et al, 2020) uses different types of videos for emotion induction. Compared with picture induction, video induction requires a shorter time to obtain different emotions and has more obvious effects.…”

Section: Emotion Inductionmentioning

confidence: 99%

Review on Emotion Recognition Based on Electroencephalography

Liu

Zhang

et al. 2021

Front. Comput. Neurosci.

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Emotions are closely related to human behavior, family, and society. Changes in emotions can cause differences in electroencephalography (EEG) signals, which show different emotional states and are not easy to disguise. EEG-based emotion recognition has been widely used in human-computer interaction, medical diagnosis, military, and other fields. In this paper, we describe the common steps of an emotion recognition algorithm based on EEG from data acquisition, preprocessing, feature extraction, feature selection to classifier. Then, we review the existing EEG-based emotional recognition methods, as well as assess their classification effect. This paper will help researchers quickly understand the basic theory of emotion recognition and provide references for the future development of EEG. Moreover, emotion is an important representation of safety psychology.

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“…A FFECTIVE computing is a growing interdisciplinary research field attracting researchers' attention from different areas including computer science, neuroscience, psychology, and signal processing [1]. Recently, EEG-based emotion recognition studies have become an increasingly important topic for affective computing and human sentiment analysis [2], [3]. A proper design of EEG-based emotion recognition models is helpful for facilitating the data processing, benefiting to discriminant feature characterization, and lightening the model performance.…”

Section: Introductionmentioning

confidence: 99%