TY  - JOUR
TT  - Emotion  Recognition via Galvanic Skin Response: Comparison of Machine Learning Algorithms and Feature Extraction Methods
AU  - Ayata, Deger
AU  - Yaslan, Yusuf
AU  - Kamaşak, Mustafa
PY  - 2017
DA  - March
JF  - IU-Journal of Electrical & Electronics Engineering
PB  - İstanbul University-Cerrahpasa
WT  - DergiPark
SN  - 1303-0914
SP  - 3147
EP  - 3156
VL  - 17
IS  - 1
KW  - Biomedical Signal Processing
KW  - Emotion Recognition
KW  - Pattern Recognition
KW  - Machine Learning
KW  - Physiological Signal
KW  - Galvanic Skin Response
KW  - Decision Tree
KW  - Random Forest
KW  - k-Nearest Neighbors
KW  - Support Vector Machine
N2  - Emotionsplay a significant and powerful role in everyday life of human beings.Developing algorithms for computers to recognize emotional expression is widelystudied area. In this study, emotion recognition from  Galvanic Skin Response signals was performedusing time domain, wavelet and empirical mode decomposition based features.Valence and arousal have been categorized and relationship between physiologicalsignals and arousal and valence has been studied using k-Nearest Neighbors,Decision Tree, Random Forest and Support Vector Machine algorithms. We haveachieved 81.81% and 89.29% accuracy rate for arousal and valence respectively. 
CR  - [1]	N. Sebe, I.Cohen, and T. S. Huang, “Multimodal Emotion Recognition”, WSPC, June 18, 2004
CR  - [2]	P. Ekman, P., R.W.Levenson, , W.V. Friesen. Autonomic nervous system activity distinguishing among emotions. Science 221, 1208– 1210., 1983
CR  - [3]	Shimmer, “Measuring Emotion: Reactions To Media”, Dublin, Ireland, 2015
UR  - https://dergipark.org.tr/en/pub/iujeee/issue//288871
L1  - https://dergipark.org.tr/en/download/article-file/272082
ER  -