Subject

Subjects : Electrodermal Activity

  • Articles (19)
  • Patents (0)
  • R&D Reports (0)
논문 검색결과
Type Year Title Cited Download
Journal 2025 Classifying social and physical pain from multimodal physiological signals using machine learning   Jang Eun Hye  Scientific Reports, v.15, pp.1-15 0 원문
Journal 2024 Comparing Multimodal Physiological Responses to Social and Physical Pain in Healthy Participants   Jang Eun Hye  FRONTIERS IN PUBLIC HEALTH, v.12, pp.1-13 3 원문
Journal 2023 Automated detection of panic disorder based on multimodal physiological signals using machine learning   Jang Eun Hye  ETRI Journal, v.45, no.1, pp.105-118 19 원문
Journal 2022 Real‐world multimodal lifelog dataset for human behavior study   Chung Seung Eun  ETRI Journal, v.44, no.3, pp.426-437 27 원문
Journal 2018 Automatic Detection of Major Depressive Disorder using Electrodermal Activity   Kim Ahyoung  Scientific Reports, v.8, pp.1-9 60 원문
Conference 2017 Identification of Electrodermal Activity(EDA) in Major Depressive Disorder   Kim Ahyoung  International Conferences on Emotion and Sensibility (ICES) 2017, pp.1-1
Conference 2017 Electrodermal Activity and Major Depressive Disorders : Physiological Changes and Their Identifying   Kim Ahyoung  International Conference of the Association of Psychology and Psychiatry for Adults and Children (APPAC) 2017, pp.1-2
Journal 2015 Analysis of Physiological Signals for Recognition of Boredom, Pain, and Surprise Emotions   Jang Eun Hye  Journal of Physiological Anthropology, v.34, pp.1-12 143 원문
Conference 2014 A Study on Analysis of Bio-Signals for Basic Emotions Classification: Recognition Using Machine Learning Algorithms   Jang Eun Hye  International Conference on Information Science and Applications (ICISA) 2014, pp.1-4 7 원문
Conference 2014 Emotion Classification Based on Bio-Signals Using Machine Learning Algorithms   Jang Eun Hye  International Conference on Advanced Cognitive Technologies and Applications (COGNITIVE) 2014, pp.104-109
Conference 2014 Emotion classification based on bio-signals emotion recognition using machine learning algorithms   Jang Eun Hye  International Conference on Information Science, Electronics and Electrical Engineering (ISEEE) 2014, pp.1373-1376 40 원문
Conference 2014 A Study on Autonomic Nervous System Responses and Feauture Selection for Emotion Recognition - Emotion Recognition using Machine Learning Algorithms   Park Byoung-Jun  International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS) 2014, pp.116-121 2 원문
Conference 2014 Correlation between Psychological and Physiological Responses during Fear - Relationship to Perceived Intensity of Fear   Jang Eun Hye  International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS) 2014, pp.104-109 3 원문
Conference 2014 Relationship between Affective Dimensions and Physiological Responses Induced by Emotional Stimuli - Base on Affective Dimensions: Arousal, Valence, Intensity and Approach   Jang Eun Hye  International Conference on Physiological Computing Systems (PhyCS) 2014, pp.254-259 2 원문
Journal 2013 Design of Prototype-Based Emotion Recognizer Using Physiological Signals   Park Byoung-Jun  ETRI Journal, v.35, no.5, pp.869-879 18 원문
Conference 2013 Classification of Three Negative Emotions Based on Physiological Signals   Jang Eun Hye  International Conference on Intelligent Systems and Applications (INTELLI) 2013, pp.75-78
Conference 2012 Emotion classification based on physiological signals induced by negative emotions: Discriminantion of negative emotions by machine learning algorithm   Jang Eun Hye  International Conference on Networking, Sensing and Control (ICNSC) 2012, pp.283-288 22 원문
Conference 2012 Seven emotion recognition by means of particle swarm optimization on physiological signals: Seven emotion recognition   Park Byoung-Jun  International Conference on Networking, Sensing and Control (ICNSC) 2012, pp.277-282 9 원문
Conference 2012 Identification of Optimal Emotion Classifier with Feature Selections Using Physiological Signals   Park Byoung-Jun  International Conference on Advances in Computer-Human Interactions (ACHI) 2012, pp.224-229
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