Citation: Nalwaya, A.; Das, K.;
Pachori, R.B. Automated Emotion
Identiﬁcation Using Fourier–Bessel
Domain-Based Entropies. Entropy
2022,24, 1322. https://doi.org/
Academic Editor: Daniel Abasolo
Received: 7 August 2022
Accepted: 16 September 2022
Published: 20 September 2022
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Automated Emotion Identiﬁcation Using Fourier–Bessel
Aditya Nalwaya *, Kritiprasanna Das and Ram Bilas Pachori
Department of Electrical Engineering, Indian Institute of Technology Indore, Indore 453552, India
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, electrocar-
diogram (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
Keywords: Fourier–Bessel series expansion; spectral entropy; ECG; EEG; FBSE-EWT
Nowadays, humans are becoming more and more dependent on computers for their
various day-to-day tasks. Thus, the need for making computers more user-friendly and
engaging is becoming more and more common rather than just being logically or com-
putationally efﬁcient [
]. To make computers more user-friendly, it must recognize the
emotion of the user interacting with it, as emotion is the most basic component that is
responsible for human attention, action, or behavior in a particular situation [
for the applications related to human–computer interaction (HCI), an emotion recognition
system is very useful [
]. Further, such HCI system can be very useful in areas related to
mental health care [
], smart entertainment [
], and an assistive system for a physically
disabled person .
Emotion recognition systems generally have two different approaches for recognizing
emotion, i.e., explicit approach, which includes expression on the face, speech, etc., that are
visible and can be easily captured for analysis; however, the problem with such an approach
is that the signals obtained in this approach are not very reliable because the subject under
consideration can hide his/her emotion, so these might not show the actual emotional
state of the subject. An alternative way of recognizing emotion is an implicit approach
where physiological signals, such as electroencephalogram (EEG), electrocardiogram (ECG),
galvanic skin response (GSR), etc., are being captured and analyzed. Such signals are not
visible and are generated inside the body by the autonomous nervous system (ANS).
Any change in the emotional state of a person is reﬂected in the respiration rates, body
Entropy 2022,24, 1322. https://doi.org/10.3390/e24101322 https://www.mdpi.com/journal/entropy
Entropy 2022,24, 1322 2 of 22
temperature, heart rate, and other physiological signals, which are not under the control of
the subject .
The brain is a central command center. It reacts to any external stimuli by ﬁring
neurons inside the brain, causing changes in ANS activity, due to which, the routine activity
of the heart and other peripheral organs varies. In addition, change in the emotional
state affects heart activity [
]. EEG and ECG signals are the biopotential that reﬂect the
activities of the brain and heart, respectively. Thus, in this study, to recognize the emotion
of the subject, physiological signals namely, EEG and ECG are considered. Although in
the literature, some other physiological signals are also used to determine the emotional
state of a person, such as changes in the conductivity of the skin, which is measured
using GSR [
]. In [
], the authors have extracted time–frequency domain features
using fractional Fourier transform (FrFT); the most relevant features are selected using
the Wilcoxon method, which is then given as input to a support vector machine (SVM)
classiﬁer for determining the emotional state of the person. Similarly, the respiration rate
also helps in determining the emotional state of the person. In [
], authors have used
deep learning (DL)-based feature extraction method and logistic regression, determining
different emotional states from the obtained features.
In recent years, several emotion recognition frameworks have been established using
different physiological signals. The authors in [
] have proposed a methodology that uses
ECG signal features derived from different intrinsic mode functions (IMFs) for emotion
classiﬁcation. Using bivariate empirical mode decomposition (BEMD), IMFs are obtained.
The instantaneous frequency and local oscillation feature are computed, which are given
as input to the linear discriminant classiﬁer to discriminate different emotional states.
A method is proposed to determine negative emotion through a single channel ECG
]. Different features, extracted from the ECG signals, are linear features, i.e., mean
of RR interval. Nonlinear features consist of bispectral analysis, power content in different
frequency bands, time–domain features contains different statistical parameters of ECG
signal, and time–frequency domain features were obtained using wavelet-based signals
decomposition. Based on these features, different emotions were detected using several
machine learning classiﬁers. The authors of [
] have extracted four different features from
the ECG signal, i.e., heart rate variability (HRV), with-in beat (WIB), frequency spectrum,
and signal decomposition-based features. To evaluate the performance of the obtained
features, ensemble classiﬁers are used. In [
], the authors have extracted ECG signal
features at different time scale using wavelet scattering, and features obtained are given as
an input to various classiﬁers for evaluating their performance.
Similarly, different emotion recognition techniques have been proposed based on
EEG signals. Anuragi et al. [
] have proposed EEG based emotion detection framework.
Using the Fourier–Bessel series expansion (FBSE)-based empirical wavelet transform (EWT)
(FBSE-EWT) method, the EEG signals are decomposed into four sub-band signals from
which features such as energy and entropy are extracted. The features are selected using
neighborhood component analysis (NCA), using different classiﬁers, such as k-nearest
neighborhood (KNN), ensemble bagged tree, and artiﬁcial neural network (ANN), emotion
class is identiﬁed. Sharma et al. [
] have used discrete wavelet transform (DWT) for EEG
signal decomposition. Third-order cumulant nonlinear features are extracted from each
sub-band, and using swarm optimization features, dimensionality is reduced. To classify
the emotional states, a long short-term memory-based technique is used. Bajaj et al. [
have used multiwavelet transform to decompose EEG signals into narrow sub-signals.
Using Euclidean distance, features are extracted that are computed from a 3-D phase
space diagram. Multiclass least squares support vector machines (MC-LS-SVM) classiﬁer
is used for identifying class of emotion; however, the authors in [
] have computed
features, namely entropy and ratio of the norms-based measure, are computed from the sub-
signals that are obtained after decomposing EEG signal using multiwavelet decomposition.
The feature matrix is given as input to MC-LS-SVM for determining the emotional state.
], features related to changes in the spectral power of EEG signal are extracted
Entropy 2022,24, 1322 3 of 22
using a short-time Fourier transform (STFT). The best 55 features among 60 features are
selected using F-score. A feature vector is formed from the selected features which are
given as an input to the SVM classiﬁer for determining the emotional state of the subject.
Liu et al. [
] have extracted fractal dimension and higher-order crossing (HOC) as features
via the sliding window approach from the given EEG signal; based on these features, an
emotion class is determined using an SVM classiﬁer. In [
], the authors have decomposed
EEG signals in different sub-bands using DWT. From the obtained wavelet coefﬁcient
features, entropy and energy of the coefﬁcients are calculated. Finally, using SVM and KNN
classiﬁers, emotional states are obtained.The authors in [
] have used a multi-channel
signal processing technique called multivariate synchrosqueezing transform (MSST) for
representing EEG signals in time–frequency domain. Independent component analysis
(ICA) is used to reduce dimensionality of the obtained high-dimensional feature matrix.
In addition, its performance is compared with non-negative matrix factorization (NMF),
which is an alternative feature selection method. The reduced feature matrix is passed
to different classiﬁers such as SVM, KNN, and ANN to discriminate different emotions.
Gupta et al. used ﬂexible analytic wavelet transform for decomposing EEG signals into
narrow-band sub-bands [
]. From each sub-band, information potential (IP) is computed
using Reyni’s quadratic entropy, and using moving average ﬁlter the extracted features
are smoothed. To determine the different emotion classes, a random forest classiﬁer is
used. Ullah et al. segmented the large duration EEG signal into smaller epoch [
each segment features; speciﬁcally, Fisher information ratio, entropy, statistical parameters,
and Petrosian fractal dimension, are computed. The above feature vector is passed to a
sparse discriminative ensemble learning (SDEL) classiﬁer to identify the emotion class.
Bhattacharyya et al. decomposed EEG signals into different modes using FBSE-EWT [
From each mode of KNN entropy, multiscale multivariate Hilbert marginal spectrum
(MHMS), and spectral Shannon entropy, features are calculated. Features are smoothed and
fed to a classiﬁer called sparse autoencoder-based random forest (ARF) for determining
the class of human emotion. Nilima et al. used the empirical mode decomposition (EMD)
method for decomposing EEG signal [
]. The second-order difference plot (SODP) features
are calculated from each IMF. A two-hidden layer multilayer perceptron is used for multi
class classiﬁcation and SVM is used for binary classiﬁcation. Nalwaya et al. [
], 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.
Further, some of the studies use both ECG and EEG signals. In [
], the authors
have recorded EEG and ECG signals while the subject is exposed to immersive virtual
environments to elicit emotion. From ECG signal, time–domain features, frequency domain
features, and non-linear features are calculated. Whereas from EEG signal band power and
mean phase connectivity features are calculated. Both EEG and ECG features are combined
to form a single feature matrix whose dimensionality is then reduced using principal
component analysis (PCA). Then, the reduced matrix is passed to an SVM classiﬁer to
determine the emotional state of the subject.
The literature review of previous studies on human emotion identiﬁcation shows
emerging research trends in ﬁnding appropriate signal decomposition techniques, dis-
tinctive features, and machine learning algorithms to classify emotional states. Most of
the studies carried out previously used a single modality, i.e., either EEG, ECG, or other
peripheral physiological signals, and very few studies have been conducted using multiple
modalities. Despite this, there is still scope for improvement in the classiﬁcation accuracy
of the earlier proposed methods.
In this article, an automated emotion identiﬁcation framework using EEG and ECG
signals is developed. Furthermore, the Fourier–Bessel (FB) domain has been explored
instead of working in traditional Fourier domain, due to various advantages associated
with latter one. FBSE uses Bessel functions as basis functions whose amplitude decay with
time and are damped in nature, which make them more suitable for non-stationary signal
Entropy 2022,24, 1322 4 of 22
analysis. FBSE spectrum has twice the resolution as compared to Fourier domain spectral
representation. Thus, looking at such advantages, FBSE-EWT is used instead of EWT for
extracting different modes from the given signal. New FB-based spectral entropy, such as
Shannon spectral entropy (SSE), log energy entropy (LEE), and Wiener entropy (WE), have
been proposed, which are used as features from the obtained modes. Then, smoothing of
the feature values is performed by applying moving average over obtained features values,
which is then given as input to SVM and KNN classiﬁers for emotional class identiﬁcation.
The block diagram of the proposed methodology is shown in Figure 1.
Figure 1. Block diagram for the proposed emotion detection methodology.
The rest of the article is organized as follows: In Section 2, the material and method-
ology are discussed in detail. In Section 3, results obtained after applying the proposed
methodology are presented. Section 4presents the performance of the proposed method
with the help of the results obtained, compares it with other exiting methodologies, and
highlights some of its limitations. Finally, Section 5concludes the article.
2. Materials and Methods
The emotion recognition framework presented in this article consists of preprocessing,
signal processing, feature extraction, feature smoothing, and classification steps.
2.1. Dataset Description
A publicly available DREAMER dataset is used to evaluate the proposed methodology.
It contains raw EEG and ECG signals, which are recorded from 23 healthy participants
while the subject is watching audio and video clips. Emotions are quantiﬁed in terms of
three different scales: arousal, valence, and dominance [
]. Each participant was shown
18 different clips of different durations with a mean of 199 s. The EPOC system by Emotive
was used, which contains 16 gold-plated dry electrodes placed in accordance with the
international 10–20 system EEG, and were recorded from, i.e., AF3, AF4, F3, F4, F7, F8, FC5,
FC6, T7, T8, P7, P8, O1, and O2; two reference electrodes, M1 and M2 were placed over
mastoid, as described in [
]. To obtain the ECG signals, electrodes were placed in two
vector directions, i.e., right arm, left leg (RA-LL) vector and left arm, left leg (LA-LL) vector.
ECG was recorded using the SHIMMER ECG sensor. Both EEG and ECG signals were
recorded at different sampling rates, i.e., 128 Hz, and 256 Hz, respectively. The sample EEG
and ECG signals obtained from the dataset are shown in Figure 2for different classes of
emotion (high arousal (HA) or low arousal (LA), high dominance (HD) or low dominance
(LD), high valence (HV) or low valence (LV)). The dataset also contains information relating
Entropy 2022,24, 1322 5 of 22
to the rating of each video on a scale of 1 to 5. Each participant rated the video as per
his/her level of emotion elicited in three different dimensions, i.e., valence (pleasantness
level), arousal (excitement level), and dominance (level of control). Rating between 1–5
was labeled as ‘0’ (low) or ‘1’ (high) for each dimension, with 3 as the threshold, i.e., if
a participant rates a video between 1 to 3, it will be considered as low or ‘0’ and a value
above 3 is considered as high or ‘1’ [33,34].
4200 EEG signal of LA class
EEG signal of HA class
EEG signal of LD class
EEG signal of HD class
20 40 60 80 100 120
EEG signal of LV class
20 40 60 80 100 120
EEG signal of HV class
ECG signal of LA class
2400 ECG signal of HA class
2362 ECG signal of of LD class
ECG signal of of HD class
50 100 150 200 250
ECG signal of of LV class
50 100 150 200 250
2400 ECG signal of HV class
Figure 2. Epochs of raw EEG and ECG signals obtained from the DREAMER dataset.
At this stage, typically, noise and artifacts from the raw signal are removed with the
help of ﬁlters and denoising algorithms. As in [
], the analysis of only the last 60 s of
signal was performed. Further, signals were segmented into small epochs of 1 s, which
were then used for further processing. The mean value was subtracted from each epoch.
Analyzing non-stationary signals such as EEG and ECG is difﬁcult as the signal is
time-varying in nature and its properties also change continuously. In order to understand
the properties of such signals, decomposing them into narrow-band simpler components
can help to make it easier to understand; therefore, the preprocessed signal was given to a
signal decomposition algorithm, which will decompose the input EEG and ECG signal into
FBSE-EWT is an improved version of the EWT, which has adaptive basis functions
derived from the signals. FBSE-EWT has been used for many biomedical signal processing
applications, such as epileptic seizure detection [
], valvular heart disease diagno-
], and posterior myocardial infarction detection [
]. FBSE-EWT technique ﬂow is
shown in Figure 3and its step by step working is as follows:
Entropy 2022,24, 1322 6 of 22
Figure 3. Flow diagram of FBSE-EWT based signal decomposition.
FBSE spectrum: FBSE has twice the frequency resolution as compared to the Fourier
domain. FBSE spectrum of a signal
samples can be obtained us-
ing zero-order Bessel functions. The magnitude of the FB coefﬁcients
computed mathematically as follows [39–42]:
K(i) = 2
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
1, 2, 3,
. A one to one relation between order
frequency is given by [39,42],
denotes the sampling frequency. Here,
continuous frequency, corresponding to the ith order and is expressed as ,
For covering whole bandwidth of
must vary from 1 to
. The FBSE spectrum is
the plot of magnitude of the FB coefﬁcient K(i)versus frequency fi.
Scale-space based boundary detection [
]: For FBSE spectrum, scale-space
representation can be obtained by convolving the signal with a kernel of Gaussian
type, which is expressed as follows:
q) = 1
1 with 3
is the scale
parameter. As the scale-space parameter, i.e.,
the number of minima decreases and no new minima will appear in the scale space
plan. The FBSE spectrum is segmented using the boundary detection technique. The
FBSE spectrum ranges from 0 to
and the FBSE spectrum segments are denoted as
are boundaries. Typically
boundaries are deﬁned between two local minima, which are obtained by the two
curves in the scale–space plane, whose length is greater than the threshold, which is
obtained by using Otsu method .
EWT ﬁlter bank design: After obtaining the ﬁlter boundaries, based on these param-
eters, empirical scaling and wavelet functions are adjusted and different band-pass
ﬁlters are designed. Wavelets scaling (
and wavelet functions (
constructed, and the mathematical expressions are given by ,
1 if |ω|≤(1−τ)ωj
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1 if (1+τ)ωj≤|ω|≤(1−τ)ωj+1
ωj) = δh|ω|−(1−τ)ωj
. To obtain the tight frames parameter,
0 if x≤0
and δ(x) + δ(1−x) = 1∀x∈[0, 1]
1 if x≥1
Filtering: Expression for detailed coefﬁcients from the EWT ﬁlter bank for the analyzed
signal a(m)is given by ,
Da,ν(j,n) = Za(m)νj(m−n)dm (9)
The approximate coefﬁcients from the EWT ﬁlter bank are computed by ,
Aa,Φ(0, n) = Za(m)Φ1(m−n)dm (10)
empirical mode is obtained by convolving the wavelet function with the detail
are different empirical modes. Original signal
can be reconstructed by adding all
reconstructed modes and one low-frequency
component; mathematically, both are expressed as below 
rj(n) = Da,ν(j,n)?νj(n)(11)
r0(n) = Aa,Φ(0, n)?Φ1(n)(12)
where ?denotes the convolution operation.
2.4. Feature Extraction
In order to understand and quantify information associated with any dynamically
changing phenomenon, entropy is the most widely used parameter [
]. Thus, in order to
understand the nonstationary behavior of physiological signals considered in this study,
entropies, as a feature set, are considered. Some of the advantages of FBSE representation
are that Bessel functions decay with time, and so provide more suitable representation of
the nonstationary signal, and that FBSE has a frequency resolution twice that of the Fourier
spectrum. In addition, there are many previous studies on emotion recognition where
entropies have been used [26,28,42,47]. SSE, WE, and LEE are deﬁned as follows:
Uniformity in the distribution of signal energy can be measured using spectral entropy.
Entropy measures the uncertainty, which has been derived from the Shannon’s expression.
The SSE is deﬁned based on the energy spectrum
obtained using FBSE. The energy
corresponding to the ith order FB coefﬁcient K(i)is deﬁned mathematically as 
Entropy 2022,24, 1322 8 of 22
The SSE is expressed mathematically as 
is the total number of FBSE coefﬁcients.
is the normalized energy distribution
over ith order is mathematically deﬁned as,
P(i) = Ei
It is another measure of ﬂatness in the distribution of signal spectral power. The WE is
also called the measure of spectral ﬂatness. It is calculated by taking the ratio of geometric
mean to arithmetic mean of the energy spectrum (
) of the FB coefﬁcient for order
expressed as ,
WE is a unitless quantity; its output is purely a numerical value ranging from 0 to 1, where
1 represents a uniform (ﬂat) spectrum and 0 indicates a pure tone.
Another variant of information measurement using LEE is deﬁned as logarithm of
P(i)in the FB domain and it is given by 
2.5. Feature Smoothing
The brain is the control center of all human activities and internal functioning. The
typical EEG signal obtained is thus a combination of various brain activity and other noise,
either related to the environment or body [
]. Rapid ﬂuctuations in feature value may
arise from these noises. Human emotion changes gradually [
]; in order to reduce the
effect of such variation on the emotional state-related feature values, a moving average
ﬁlter with a window size of 3 samples is utilized. The effect of the moving average ﬁlter on
the raw feature value of the ﬁrst channel’s ﬁrst epoch can be seen in Figure 4.
Feature vectors extracted from the EEG signals are used for the classiﬁcation of dif-
ferent emotions. Based on the feature vector, the classiﬁer will discriminate the data into
high and low dimensions of emotion, i.e., either HV or LV, HA or LA, and HD or LD. In
this study, SVM with the cubic kernel and KNN are used independently for the purpose of
SVM is a supervised machine learning algorithm that classiﬁes data by ﬁrst learning
from labeled data belonging to different classes. The main principle behind the classiﬁer
working is ﬁnding decision boundaries that are formed by a hyperplane, and it helps in
separating data into two separate classes. The hyperplane is constructed by training the
classiﬁer with sample data. The optimum location of the hyperplane or decision boundary
depends on the support vectors, which are the points nearest to the decision boundary [
Entropy 2022,24, 1322 9 of 22
SVM iteratively optimizes the location of hyperplane in order to maximize the margin. A
margin is a total separation between two classes. SVM can be linear or nonlinear classiﬁer
depending on kernels used. Generally, in the case of a linear SVM straight line, ﬂat plane, or
an N-dimensional hyperplane are the simplest way of separating data into two groups, but
there are certain situations where nonlinear boundary separates the data more efﬁciently.
In case of a nonlinear classiﬁer, different kernels can be polynomial, hyperbolic tangent
function, or Gaussian radial basis function. In this work, an SVM classiﬁer with a cubic
kernel is used. Generalized mathematical expression for deﬁning the hyperplane of the
SVM classiﬁer can be expressed as follows :
f(x) = sign"R
bifiK(x,xi) + c#(18)
is a positive real constant,
is total number of observations,
is a real con-
is a kernel or feature space,
input vector, and output vector is denoted
. For a linear feature space
xi) = xT
, for polynomial SVM of any order
xi) = (xT
deﬁnes the feature space, i.e.,
for quadratic polynomial and
(p=3)for cubic polynomial.
10 20 30 40 50 60
10 20 30 40 50 60
Smoothed SSE feature
10 20 30 40 50 60
10 20 30 40 50 60
Smoothed LEE feature
10 20 30 40 50 60
10 20 30 40 50 60
Smoothed WE feature
Feature values obtained from signal epochs is shown on the left and its smoothed version is
shown on the right.
The KNN is a non-parametric, supervised machine learning algorithm used for data
classiﬁcation and regression [
]. It categorizes data points into different groups based on
the distance from some of the nearest neighbors. For classifying any particular training
data, the KNN algorithm follows these steps:
Compute distance between sample data and other sample using anyone of the
distance metrics such as Euclidean, Mahalanobis, or Minkowski distance.
Rearrange the distant metric obtained from the ﬁrst step in ascending order and
top kvalues are considered with distance from current sample is minimum.
Entropy 2022,24, 1322 10 of 22
Class is assigned to the sample data depending on the maximum number of nearest
In order to evaluate the performance of our proposed methodology, a publicly avail-
able DREAMER dataset is used [
]. In this section, the results obtained after applying the
proposed emotion identiﬁcation method over the DREAMER dataset is discussed in detail.
The raw EEG data are segmented into an epoch of one second. An EEG epoch length is
128 samples and the ECG length is 256 samples. These epochs of the EEG signal are de-
composed into four modes using FBSE-EWT, as shown in Figure 5, and the corresponding
ﬁlter bank is shown in Figure 6. The decomposition of EEG and ECG signal epochs gives a
minimum of four number of modes. So, we set the required number of modes to four to
obtain a uniform number of features across different observations. From each mode, three
features are extracted. The EEG data consist of 14 channels; therefore, the dimension of
the feature vector is 168
. Similarly, from the two channels of the ECG, we
obtain a 24-element feature vector. The number of trials for each participant is 18 and there
are a total of 23 participants, which gives us a total of 24,840
or epochs. The ﬁnal dimensions of the feature matrices are 24, 840
168 and 24, 840
for EEG and ECG, respectively. For the multimodal case, both the feature from EEG and
ECG are combined, where we obtain a feature matrix with the dimension of 24,840
This feature matrix is classiﬁed using two classiﬁers: SVM and KNN. For KNN
Euclidean distance is used as a distance metric; for SVM, cubic kernels are used.
40 Mode 2
20 40 60 80 100 120
50 100 150 200 250
based signal decomposition of (
) EEG and (
) ECG epochs into different
Performance was evaluated using the classiﬁer learner application present in MATLAB
2022a. The system used for computing has an Intel i7 CPU with 8 GB of RAM. For three
different dimensions of emotion, i.e., arousal, dominance, and valence, three different
binary classiﬁcations are performed. Thus, each classiﬁer groups data into either high or
low arousal, high or low dominance, and high or low valence classes. Low and high class is
Entropy 2022,24, 1322 11 of 22
decided based on the rating given by the participant on a scale of 1 to 5, with 3 as threshold,
3 is consider low and all above it is considered as high [
], due to which,
unbalanced classes are obtained for some participants [
]. Further, the performance of
the proposed method is evaluated using the ﬁrst features obtained from EEG signals, then
ECG signals, and then using the combined features.
0 20 40 60
0 50 100
based ﬁlter bank used for decomposing (
) EEG and (
) ECG epochs into
different modes (ﬁlter corresponding to different modes are shown in different colors).
Features obtained from EEG and ECG signals of different subjects are combined
together and the classiﬁers are trained and tested subject independently. Both SVM and
KNN are evaluated independently using ﬁve-fold cross-validation, where different signal
feature observations are placed randomly independent to the subject. For the EEG feature
and arousal, the dimension accuracy obtained for SVM is 81.53% and for KNN it is 97.50%.
Similarly, performance was evaluated for the case of dominance and valence, which are
summarized in Tables 1–3. The tables consist of classiﬁcation results obtained by using
different modalities, i.e., EEG, ECG, and the combined multimodal approach, and the best
results obtained are highlighted using bold fonts. The confusion matrices for three different
emotion dimensions classiﬁcations based on EEG, ECG, and multimodal signals are shown
in Figures 7–9.
Table 1. Performance with different modalities for arousal emotion dimension.
Modality Model Accuracy * Sensitivity * Speciﬁcity * Precision * F1 Score *
(k= 1) 97.50 97.89 97.01 97.67 97.78
(Cubic) 81.53 82.04 80.81 86.02 83.98
(k= 1) 69.94 73.17 65.73 73.56 73.37
(Cubic) 63.84 65.09 61.35 77.11 70.59
(k= 1)97.84 98.10 97.51 98.06 98.08
(Cubic) 84.54 84.75 84.24 88.45 86.56
* in percent.
Accuracy parameters, such as sensitivity, speciﬁcity, precision, and F1 score, are shown
in Tables 1–3. From these tables, it may be observed that the accuracy obtained from EEG
and multimodal are almost similar; however, among the two, multimodal is still the winner
as it can be seen that all other parameters of classiﬁer reliability check are having sightly
better result than the unimodal EEG signal. Following mathematical expressions have been
used for calculating parameters namely sensitivity, speciﬁcity, precision, and F1 score [
TP + FN (19)
Entropy 2022,24, 1322 12 of 22
FP + TN (20)
TP + FP (21)
F1 Score =2TP
2TP + FP + FN (22)
where TP is the true positive, TN is the true negative, FP is the false positive, and FN is the
Table 2. Performance with different modalities for dominance emotion dimension.
Modality Model Accuracy * Sensitivity * Speciﬁcity * Precision * F1 Score *
(k= 1) 97.68 97.89 97.44 97.63 97.76
(Cubic) 81.53 82.04 80.81 86.02 83.98
(k= 1) 69.25 70.33 68.08 70.57 70.45
(Cubic) 62.30 62.90 61.56 66.81 64.80
(k= 1)97.91 97.95 97.86 98.02 97.99
(Cubic) 84.12 83.74 84.55 86.15 84.93
* in percent.
Table 3. Performance with different modalities for valence emotion dimension.
Modality Model Accuracy * Sensitivity * Speciﬁcity * Precision * F1 Score *
(k= 1) 97.55 97.97 96.89 97.98 97.98
(Cubic) 81.49 82.03 80.47 88.97 85.36
(k= 1) 68.82 74.59 60.22 73.69 74.13
(Cubic) 63.31 66.03 55.28 81.31 72.88
(k= 1)97.86 98.29 97.19 98.17 98.23
(Cubic) 84.10 84.77 82.90 89.93 87.28
* in percent.
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Figure 7. Confusion matrix for arousal emotion dimension.
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Figure 8. Confusion matrix for dominance emotion dimension.
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Figure 9. Confusion matrix for valence emotion dimension.
Although, in recent years, signiﬁcant research has been carried out on emotion recogni-
tion related topics, still, it is challenging due to the fuzziness in distinction among different
emotions. The study presented in this article demonstrated an approach for developing an
automated human emotion identiﬁcation that is able to categorize the three dimensions
of emotion, i.e., arousal, valence, and dominance, into high or low classes of a particular
dimension. In order to retrieve emotion-related information from the physiological signals,
the FBSE-EWT signal decomposition method is used. Then, various FB-based entropies,
such as SSE, WE, and LEE features, are computed from the modes obtained after signal
decomposition. Figures 10 and 11 show different boxplots of mentioned entropy features
obtained from EEG and ECG, respectively. The plots give information related to feature
value distribution among different classes. Due to difference in the interquartile distance
among different feature values good classiﬁcation accuracy is obtained. Then, the extracted
Entropy 2022,24, 1322 16 of 22
features are fed to two different classiﬁers: SVM and KNN. The above results show that
the KNN classiﬁer is found to have more accuracy than the SVM classiﬁer. Further, the
multi-model scenario is found to be more beneﬁcial to reliably identify the emotional
states of the subject. A statistical signiﬁcance test was performed to show that the accu-
racy improvement in the multimodal emotion recognition model is signiﬁcant [
analysis of variance (ANOVA) test was performed on the 5-fold accuracies of different
modalities, such as EEG, ECG, and multimodal. Figure 12 shows the accuracies of the
high and low states of arousal, dominance, and valance using boxplots. The p-values
for the statistical test for arousal, dominance, and valance are 1.33
, respectively, which indicates a signiﬁcant improvement in accuracies for
the multimodal emotion detection model. The proposed methodology is compared with
the various existing approaches to identify the emotional states. The proposed method
found to have superior performance as highlighted in Table 4using bold fonts. In [
various features related to EEG and ECG signals, such as power spectral density (PSD),
HRV, entropy, EEG-topographical image-based, and ECG spectrogram image-based DL
features, are calculated. Topic et al. [
] used EEG signals for emotion detection and has
derived holographic features and for maximizing model accuracy, only relevant channels
are selected. In [
], using data preprocessing, frame level features are derived from the
EEG signal, from which, effective features are extracted using a teacher–student frame-
work. In [
], the deep canonical correlation analysis (DCCA) method is proposed for
emotion recognition. From the table, it may be noted that currently, various studies are
being performed for emotion detection based on a DL-related framework [34,58,60–62] as
it automatically ﬁnds the most signiﬁcant features; however, it is difﬁcult to understand
or to ﬁnd the reason behind the results obtained. Moreover, DL models are computation-
ally complex and require a large amount of data to train [
]. Feature extraction in other
existing state-of-the-art methods is a complex process and less accurate than the study
presented here, which differentiates itself from the other existing studies as the results are
encouraging. Thus, the proposed method has several advantages over the various listed
DL-based feature extraction methods, such as being easy to comprehend, less complex,
and more reliable. The disadvantage of the proposed multimodal approach is that its time
complexity has increased compared to a single EEG-only modality. Still, the increased
reliability makes it more suitable for feature extraction. As the results obtained using the
proposed methodology have only been tested on a small size dataset, in the future, they can
be tested on a larger database in order to verify their validity. Furthermore, in this study, a
ﬁxed 60 s duration physiological signals was used; this can also be made adaptive in order
to select the time portion, which can make the methodology more efﬁcient and adaptive.
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Comparison of existing methodologies applied on DREAMER dataset with the proposed
emotion recognition method.
Authors (Year) Methodology Modality Accuracy (%)
LA-HA LD-HD LV-HV
Katsigiannis et al.  (2018) PSD features and SVM classiﬁer EEG & ECG 62.32 61.84 62.49
Song et al.  (2018) DGCNN EEG 84.54 85.02 86.23
Zhang et al.  (2019) PSD features and GCB-net classiﬁer EEG 89.32 89.20 86.99
Bhattacharyya et al. [
MFBSE-EWT based entropy features and ARF classiﬁer EEG 85.4 86.2 84.5
Cui et al.  (2020) RACNN EEG 97.01 - 95.55
Kamble et al.  (2021) DWT, EMD-based features, and CML and EML-based classiﬁer EEG 93.79 - 94.5
Li et al.  (2021) 3DFR-DFCN EEG 75.97 85.14 82.68
Siddharth et al.  (2022)
PSD, HRV, entropy, DL based feature, and LSTM based classiﬁer
EEG & ECG 79.95 - 79.95
Topic et al.  (2022) Holographic features and CNN EEG 92.92 92.97 90.76
Gu et al.  (2022) FLTSDP EEG 90.61 91.00 91.54
Liu et al.  (2022) DCCA EEG & ECG 89.00 90.7 90.6
Proposed work FBSE-EWT-based entropy features and KNN classiﬁer EEG & ECG 97.84 97.91 97.86
PSD = power spectral density, SVM = support-vector machines, DGCNN = dynamic graph convolutional neural network,
GCB-net = graph convolutional broad network, MFBSE-EWT = multivariate FBSE-EWT,
ARF = adaptive
RACNN = region-based convolutional neural network, DWT = discrete wavelet transform, EMD = empirical mode decom-
position, CML = conventional machine learning, 3DFR-DFCN = 3-D feature representation and dilated fully convolutional
network, HRV = heart rate variability, DL = deep learning, LSTM = long short term memory, CNN = convolutional neural
network, FLTSDP = frame level teacher-student framework with data privacy, DCCA = deep canonical correlation analysis.
Figure 10. Box plots for the features calculated using EEG signals.
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Figure 11. Box plots for the features calculated using ECG signals.
Accuracy (in %)
Accuracy (in %)
Accuracy (in %)
(a) (b) (c)
Box plots showing statistical signiﬁcance of accuracies for different modalities (
(b) dominance, and (c) valence.
This study presents an automated emotion identiﬁcation by a multimodal approach
using FB domain-based entropies. The publically available DREAMER data set was used to
evaluate the performance of the proposed algorithm. Physiological signals obtained from
the dataset, namely EEG and ECG, are decomposed using the FBSE-EWT into four modes.
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From these modes, several new FB-based entropy features, such as FB spectral-based SSE,
LEE, and WE were computed. The dataset consisted of three-dimensional emotional space,
i.e., arousal, valence, and dominance. Each of the dimensions are categorized into high and
low classes based on the rating provided along with the dataset. The proposed method
using the KNN classiﬁer provides the highest accuracies of 97.84%, 97.91%, and 97.86% for
arousal, valence, and dominance emotion classes, respectively. After comparing it with
results obtained from current methods, signiﬁcant improvement in the results is obtained.
Moreover, the multimodal approach is found to be the most accurate in terms of identifying
human emotional states.
A.N., K.D. and R.B.P. contributed equally to this work. All authors have read
and agreed to the published version of the manuscript.
This study is supported by the Council of Scientiﬁc & Industrial Research (CSIR) funded
Research Project, Government of India, Grant number: 22(0851)/20/EMR-II.
Institutional Review Board Statement: Not applicable.
Data Availability Statement:
The EEG and ECG data are provided by the Stamos Katsigiannis
collected at University of the West of Scotland.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
The following abbreviations are used in this manuscript:
ANN Artiﬁcial neural network
ANOVA Analysis of variance
ANS Autonomous nervous system
ARF Autoencoder-based random forest
BEMD Bivariate empirical mode decomposition
DCCA Deep canonical correlation analysis
DL Deep learning
DWT Discrete wavelet transform
EMD Empirical mode decomposition
EWT Empirical wavelet transform
FBSE Fourier–Bessel series expansion
FBSE-EWT FBSE-based EWT
FN False negative
FP False positive
FrFT Fractional Fourier transform
GSR Galvanic skin response
HA High arousal
HCI Human–computer interaction
HD High dominance
HOC Higher-order crossing
HRV Heart rate variability
HV High valence
ICA Independent component analysis
IMF Intrinsic mode functions
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IP Information potential
KNN K-nearest neighbors
LA Low arousal
LA-LL Left arm and left leg
LD Low dominance
LEE Log energy entropy
LV Low valence
MC-LS-SVM Multiclass least squares support vector machines
MHMS Multivariate Hilbert marginal spectrum
MSST Multivariate synchrosqueezing transform
NCA Neighborhood component analysis
NMF Non-negative matrix factorization
PCA Principal component analysis
PSD Power spectral density
RA-LL Right arm and Left leg
SDEL Sparse discriminative ensemble learning
SODP Second order difference plot
SSE Shannon spectral entropy
STFT Short time Fourier transform
SVM Support vector machines
TN True negative
TP True positive
TQWT Tunable Q-factor wavelet transform
WE Wiener entropy
WIB With-in beat
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