Journal ArticleDOI
Emotion classification with multichannel physiological signals using hybrid feature and adaptive decision fusion
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TLDR
In this article, a multimodal emotion classification framework that uses multichannel physiological signals and introduces two key techniques, hybrid feature extraction and adaptive decision fusion, was proposed for emotion classification.About:
This article is published in Biomedical Signal Processing and Control.The article was published on 2022-01-01. It has received 10 citations till now. The article focuses on the topics: Computer science & Emotion classification.read more
Citations
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Journal ArticleDOI
Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals
Wenqian Lin,Chao-Jun Li +1 more
TL;DR: A review of the research work and application of emotion recognition and judgment based on the four physiological signals mentioned above is offered in this paper , which covers the technologies adopted, the objects of application and the effects achieved.
Journal ArticleDOI
Decision-level information fusion powered human pose estimation
Yiqing Zhang,Weiting Chen +1 more
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Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals
Kevin G. Montero Quispe,Daniel M. S. Utyiama,Eulanda Miranda dos Santos,Horacio Oliveira,Eduardo J. P. Souto +4 more
TL;DR: In this paper , self-supervised learning (SSL) is used to learn representations directly from unlabeled signals and subsequently use them to classify affective states, which can improve data efficiency, are widely transferable, are competitive when compared to their fully supervised counterparts and do not require the data to be labeled for learning.
Journal ArticleDOI
Model of Emotion Judgment Based on Features of Multiple Physiological Signals
TL;DR: The model of emotion judgment based on features of multiple physiological signals was investi-gated by playing a computer game while their physiological signals were acquired and each volunteer rated their own emotion when experiencing the six events.
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EEG Data Augmentation for Emotion Recognition with a Task-Driven GAN
Qing Liu,Jianjun Hao,Yijun Guo +2 more
TL;DR: In this paper , a task-driven method based on CWGAN was proposed to generate high-quality artificial data, where the generated data were represented as multi-channel EEG data differential entropy feature maps, and a task network was introduced to guide the generator during the adversarial training.
References
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Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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Development and validation of brief measures of positive and negative affect: The PANAS scales.
TL;DR: Two 10-item mood scales that comprise the Positive and Negative Affect Schedule (PANAS) are developed and are shown to be highly internally consistent, largely uncorrelated, and stable at appropriate levels over a 2-month time period.
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DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Sander Koelstra,Christian Mühl,Mohammad Soleymani,Jong-Seok Lee,Ashkan Yazdani,Touradj Ebrahimi,Thierry Pun,Anton Nijholt,Ioannis Patras +8 more
TL;DR: A multimodal data set for the analysis of human affective states was presented and a novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool.
Journal Article
Supervised Machine Learning: A Review of Classification Techniques
TL;DR: The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features, and the resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown.
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Autonomic nervous system activity distinguishes among emotions
TL;DR: Emotion-specific activity in the autonomic nervous system was generated by constructing facial prototypes of emotion muscle by muscle and by reliving past emotional experiences, and distinguished not only between positive and negative emotions, but also among negative emotions.