Emotion Identification From TQWT-Based EEG Rhythms

Nalwaya, Aditya; Das, Kritiprasanna; Pachori, Ram Bilas

doi:10.4018/978-1-6684-3947-0.ch011

Cited by 8 publications

(2 citation statements)

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“…Sharma et al [14] proposed the discrete wavelet transform as decomposition method to explore the nonlinear dynamics of each subband signal. Nalwaya et al [15] proposed tunable Q-factor wavelet transform to separate EEG rhythms. Kritiprasanna et al [16] proposed a EEG rhythms segmentation method using multivariate iterative filtering.…”

Section: Introductionmentioning

confidence: 99%

A Brain Network Analysis-Based Double Way Deep Neural Network for Emotion Recognition

Niu

Sun

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Constructing reliable and effective models to recognize human emotional states has become an important issue in recent years. In this article, we propose a double way deep residual neural network combined with brain network analysis, which enables the classification of multiple emotional states. To begin with, we transform the emotional EEG signals into five frequency bands by wavelet transform and construct brain networks by inter-channel correlation coefficients. These brain networks are then fed into a subsequent deep neural network block which contains several modules with residual connection and enhanced by channel attention mechanism and spatial attention mechanism. In the second way of the model, we feed the emotional EEG signals directly into another deep neural network block to extract temporal features. At the end of the two ways, the features are concatenated for classification. To verify the effectiveness of our proposed model, we carried out a series of experiments to collect emotional EEG from eight subjects. The average accuracy of the proposed model on our emotional dataset is 94.57%. In addition, the evaluation results on public databases SEED and SEED-IV are 94.55% and 78.91%, respectively, demonstrating the superiority of our model in emotion recognition tasks.

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Section: Introductionmentioning

confidence: 99%

A Brain Network Analysis-Based Double Way Deep Neural Network for Emotion Recognition

Niu

Sun

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…A two-hidden layer multilayer perceptron is used for multi class classification and SVM is used for binary classification. Nalwaya et al [ 30 ], have used tunable Q-factor wavelet transform (TQWT) to separate various rhythms of EEG. Different statistical and information potential features are computed for each rhythm, which are then fed to SVM cubic classifier for emotion identification.…”

Section: Introductionmentioning

confidence: 99%

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Nalwaya

Das

Pachori

2022

Entropy

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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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