TY - GEN
T1 - Feature Comparison of Emotion Estimation by EEG and Heart Rate Variability Indices and Accuracy Evaluation by Machine Learning
AU - Suzuki, Kei
AU - Matsubara, Ryota
AU - Laohakangvalvit, Tipporn
AU - Sugaya, Midori
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - There has been a lot of attempts on estimating human emotions using physio-logical data, and it is expected to be applied to medical diagnosis. Recently, there is emotion estimation model using EEG and heart rate variability index-es as feature values, and applying deep learning to classify emotions with an accuracy of 61%. However, the accuracy may not be sufficient for applications such as medical diagnosis. In this study, we extracted and selected features of EEG and heart rate variability indexes in order to improve the accuracy. As a result, by using our proposed method to extract and select features, the accuracy of the model was increased to almost 100%.
AB - There has been a lot of attempts on estimating human emotions using physio-logical data, and it is expected to be applied to medical diagnosis. Recently, there is emotion estimation model using EEG and heart rate variability index-es as feature values, and applying deep learning to classify emotions with an accuracy of 61%. However, the accuracy may not be sufficient for applications such as medical diagnosis. In this study, we extracted and selected features of EEG and heart rate variability indexes in order to improve the accuracy. As a result, by using our proposed method to extract and select features, the accuracy of the model was increased to almost 100%.
KW - Emotion recognition
KW - Feature extraction
KW - Feature selection
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85112040470&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112040470&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-80285-1_27
DO - 10.1007/978-3-030-80285-1_27
M3 - Conference contribution
AN - SCOPUS:85112040470
SN - 9783030802844
T3 - Lecture Notes in Networks and Systems
SP - 222
EP - 230
BT - Advances in Neuroergonomics and Cognitive Engineering - Proceedings of the AHFE 2021 Virtual Conferences on Neuroergonomics and Cognitive Engineering, Industrial Cognitive Ergonomics and Engineering Psychology, and Cognitive Computing and Internet of Things, 2021
A2 - Ayaz, Hasan
A2 - Asgher, Umer
A2 - Paletta, Lucas
PB - Springer Science and Business Media Deutschland GmbH
T2 - AHFE Conferences on Neuroergonomics and Cognitive Engineering, Industrial Cognitive Ergonomics and Engineering Psychology, and Cognitive Computing and Internet of Things, 2021
Y2 - 25 July 2021 through 29 July 2021
ER -