Next Article in Journal
On the Frequency Carrier Offset and Symbol Timing Estimation for CCSDS 131.2-B-1 High Data-Rate Telemetry Receivers
Next Article in Special Issue
The Effects of Individual Differences, Non-Stationarity, and the Importance of Data Partitioning Decisions for Training and Testing of EEG Cross-Participant Models
Previous Article in Journal
Obituary for Prof. Dr. Alexander Gaskov
Previous Article in Special Issue
Wearable Technologies for Mental Workload, Stress, and Emotional State Assessment during Working-Like Tasks: A Comparison with Laboratory Technologies
Article

Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

Shibaura Institute of Technology, Tokyo 135-8548, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Alan Jović
Sensors 2021, 21(9), 2910; https://doi.org/10.3390/s21092910
Received: 31 March 2021 / Revised: 17 April 2021 / Accepted: 19 April 2021 / Published: 21 April 2021
(This article belongs to the Special Issue Intelligent Biosignal Analysis Methods)
In human emotion estimation using an electroencephalogram (EEG) and heart rate variability (HRV), there are two main issues as far as we know. The first is that measurement devices for physiological signals are expensive and not easy to wear. The second is that unnecessary physiological indexes have not been removed, which is likely to decrease the accuracy of machine learning models. In this study, we used single-channel EEG sensor and photoplethysmography (PPG) sensor, which are inexpensive and easy to wear. We collected data from 25 participants (18 males and 7 females) and used a deep learning algorithm to construct an emotion classification model based on Arousal–Valence space using several feature combinations obtained from physiological indexes selected based on our criteria including our proposed feature selection methods. We then performed accuracy verification, applying a stratified 10-fold cross-validation method to the constructed models. The results showed that model accuracies are as high as 90% to 99% by applying the features selection methods we proposed, which suggests that a small number of physiological indexes, even from inexpensive sensors, can be used to construct an accurate emotion classification model if an appropriate feature selection method is applied. Our research results contribute to the improvement of an emotion classification model with a higher accuracy, less cost, and that is less time consuming, which has the potential to be further applied to various areas of applications. View Full-Text
Keywords: emotion recognition; electroencephalogram (EEG); photoplethysmography (PPG); machine learning; feature extraction; feature selection emotion recognition; electroencephalogram (EEG); photoplethysmography (PPG); machine learning; feature extraction; feature selection
Show Figures

Figure 1

MDPI and ACS Style

Suzuki, K.; Laohakangvalvit, T.; Matsubara, R.; Sugaya, M. Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms. Sensors 2021, 21, 2910. https://doi.org/10.3390/s21092910

AMA Style

Suzuki K, Laohakangvalvit T, Matsubara R, Sugaya M. Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms. Sensors. 2021; 21(9):2910. https://doi.org/10.3390/s21092910

Chicago/Turabian Style

Suzuki, Kei, Tipporn Laohakangvalvit, Ryota Matsubara, and Midori Sugaya. 2021. "Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms" Sensors 21, no. 9: 2910. https://doi.org/10.3390/s21092910

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop