Assessment of Chanting Effects Using EEG Signals

Das, Kritiprasanna; Verma, Pankaj; Pachori, Ram Bilas

doi:10.1109/dspa53304.2022.9790754

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…FBSE spectrum of a signal

of length S samples can be obtained using zero-order Bessel functions. The magnitude of the FB coefficients

can be computed mathematically as follows [ 39 , 40 , 41 , 42 ]:

where

is the zero-order and

is the first-order Bessel functions. Positive roots of zeroth order Bessel function are denoted by

, which are arranged in ascending order, where

.…”

Section: Methodsmentioning

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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“…FBSE spectrum of a signal

of length S samples can be obtained using zero-order Bessel functions. The magnitude of the FB coefficients

can be computed mathematically as follows [ 39 , 40 , 41 , 42 ]:

where

is the zero-order and

is the first-order Bessel functions. Positive roots of zeroth order Bessel function are denoted by

, which are arranged in ascending order, where

.…”

Section: Methodsmentioning

confidence: 99%

Automated Emotion Identification Using Fourier–Bessel Domain-Based Entropies

Nalwaya

Das

Pachori

2022

Entropy

View full text Add to dashboard Cite

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“…Considering the nonlinearity and complexity of EEG signals, the application of dynamic systems analysis methods to EEG-based emotion recognition will be potentially adaptive [8], [9]. Recently, to analysis EEG rhythms, Fourier-Bessel series expansion (FBSE) based methods [10]- [13] have been widely applied to decompose EEG signals. Sharma et al [14] proposed the discrete wavelet transform as decomposition method to explore the nonlinear dynamics of each subband signal.…”

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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