Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Aditya Nalwaya · Kritiprasanna Das · Ram Bilas Pachori

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Heart Rate Variability and Autonomic Control

Abstract

Human dependence on computers is increasing day by day; thus, human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it; for this purpose, an emotion recognition system is required. Physiological signals, specifically, electrocardiogram (ECG) and electroencephalogram (EEG), were studied here for the purpose of emotion recognition. This paper proposes novel entropy-based features in the Fourier-Bessel domain instead of the Fourier domain, where frequency resolution is twice that of the latter. Further, to represent such non-stationary signals, the Fourier-Bessel series expansion (FBSE) is used, which has non-stationary basis functions, making it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes using FBSE-based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector, which are further used to develop machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals.

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References

2015Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks 2018EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks 2017DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices 2010EEG-Based Emotion Recognition in Music Listening 2022EEG Based Emotion Recognition: A Tutorial and Review 2011ECG Pattern Analysis for Emotion Detection 2022Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition 2018Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors

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2023A Brain Network Analysis-Based Double Way Deep Neural Network for Emotion Recognition 2023Deep time-frequency features and semi-supervised dimension reduction for subject-independent emotion recognition from multi-channel EEG signals

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