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

Kei Suzuki¹, Tipporn Laohakangvalvit¹, Ryota Matsubara¹

¹Shibaura Institute of Technology, Tokyo 135-8548, Japan.

Sensors (Basel, Switzerland)

|April 30, 2021

Summary

This summary is machine-generated.

Accurate emotion classification is achievable using inexpensive sensors like electroencephalogram (EEG) and photoplethysmography (PPG). Proposed feature selection methods enhance deep learning models, achieving up to 99% accuracy with minimal physiological data.

Keywords:

electroencephalogram (EEG)emotion recognition feature extraction feature selection machine learning photoplethysmography (PPG)

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Area of Science:

Affective computing
Biomedical signal processing
Machine learning for healthcare

Background:

Human emotion estimation often relies on expensive and cumbersome physiological sensors.
Existing models may suffer from reduced accuracy due to the inclusion of irrelevant physiological indicators.

Purpose of the Study:

To develop a cost-effective and user-friendly emotion classification model.
To investigate the impact of feature selection on model accuracy using physiological signals.
To validate the efficacy of inexpensive sensors for emotion recognition.

Main Methods:

Utilized single-channel electroencephalogram (EEG) and photoplethysmography (PPG) sensors.
Collected physiological data from 25 participants.
Employed deep learning algorithms and proposed feature selection methods for emotion classification within the Arousal-Valence space.
Performed stratified 10-fold cross-validation for accuracy assessment.

Main Results:

Achieved high model accuracies ranging from 90% to 99%.
Demonstrated that a limited set of physiological features from inexpensive sensors can yield accurate emotion classification.
Validated the effectiveness of the proposed feature selection techniques.

Conclusions:

Inexpensive physiological sensors combined with appropriate feature selection can create accurate emotion classification models.
This approach reduces cost and time requirements for emotion recognition systems.
The findings have potential applications in various fields requiring emotion estimation.