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2021 | OriginalPaper | Buchkapitel

Feature Comparison of Emotion Estimation by EEG and Heart Rate Variability Indices and Accuracy Evaluation by Machine Learning

verfasst von : Kei Suzuki, Ryota Matsubara, Tipporn Laohakangvalvit, Midori Sugaya

Erschienen in: Advances in Neuroergonomics and Cognitive Engineering

Verlag: Springer International Publishing

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Abstract

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%.

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Titel: Feature Comparison of Emotion Estimation by EEG and Heart Rate Variability Indices and Accuracy Evaluation by Machine Learning
verfasst von: Kei Suzuki
Ryota Matsubara
Tipporn Laohakangvalvit
Midori Sugaya
Verlag: Springer International Publishing
Buch: Advances in Neuroergonomics and Cognitive Engineering
Print ISBN: 978-3-030-80284-4

Electronic ISBN: 978-3-030-80285-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-80285-1_27

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