A machine learning algorithm for classification of mental tasks.pdf

Computers and Electrical Engineering 99 (2022) 107785
Available online 17 February 2022
0045-7906/© 2022 Elsevier Ltd. All rights reserved.
A machine learning algorithm for classification of mental tasks
Hariprasath Manoharan a
, Sulaima Lebbe Abdul Haleem b
, S. Shitharth c
,
Pravin R. Kshirsagar d,*
, Vineet Tirth e
, M. Thangamani f
, Radha Raman Chandan g
a
Department of Electronics and Communication Engineering, Panimalar Institute of Technology, Poonamallee, Chennai, Tamil Nadu, India
b
Department of Information and Communication Technology, South Eastern University of Sri Lanka (SEUSL), Sri Lanka
c
Department of Computer Science and Engineering, Kabri Dehar University, Ethiopia
d
Department of Artificial Intelligence, G. H. Raisoni College of Engineering 440016, Nagpur India
e
Mechanical Engineering Department, College of Engineering, King Khalid University, Abha, Asir 61411, Saudi Arabia
f
Department of Information Technology, Kongu Engineering College, Perundurai, Tamil Nadu, India
g
Department of Computer Science & Engineering, Shambhunath Group of Institutions, Prayagraj, India
A R T I C L E I N F O
Keywords:
Brain-computer interface (BCI)
Electroencephalogram, (EEG)
Mental task
Discrete wavelet transform, (DWT)
B-alert machine
Classification
Support vector machine, (SVM)
Accuracy
Error
Artificial neural network, (ANN)
A B S T R A C T
In this article, a contemporary tack of mental tasks on cognitive parts of humans is appraised
using two different approaches such as wavelet transforms at a discrete time (DWT) and support
vector machine (SVM). The put forth tack is instilled with the electroencephalogram (EEG)
database acquired in real-time from CARE Hospital, Nagpur. Additional data is also acquired from
a brain-computer interface (BCI). In the working model, signals from the database are wed out
into different frequency sub-bands using DWT. Initially, updated statistical features are obtained
from different frequency sub-bands. This type of representation defines the wavelet co-efficient
which is introduced for reducing the measurement of data. Then, the projected method is real
ized using SVM for segregating both port and veracious hand movement. After segregation of EEG
signals, results are achieved with an accuracy of 92% for BCI competition paradigm III and
97.89% for B-alert machine.
1. Classification of mental task- an introduction
Brain-computer interface (BCI) consisting of processors analyses the signals on the brain and decodes the observed signals to a set of
commands for enchanting the desired exploit. However, there is no need to use standard paths present at the output of peripheral
nerves and muscles for such a conversion process. The EEG signal is a vital part of the correspondence between human considerations
and PC. It is also a new research field for neurologists. For classifying various signals depending on mental task, a transformation
process is needed where signals monitored on EEG will be converted to control signals, thus, providing robust operation. Few mental
states can be dependably recognized by perceiving designs in EEG, at this juncture, a paralyzed individual could communicate to any
gadget like a wheelchair by making arrangements out of these psychological states. Estimating mind action through EEG prompts the
obtaining of an enormous measure of information. Therefore, to get the ideal exhibitions, it is essential to work with fewer qualities
that describe the applicable properties of the signal. Feature extraction can be characterized by an activity that changes one or a few
signals into a feature vector. It is conceded by discrete wavelet transform (DWT), which is introduced in 1976, where the main
Edited by: Dr. Carlos Montenegro.
* Corresponding author.
E-mail addresses: hari13prasath@gmail.com (H. Manoharan), pravinrk88@yahoo.com (P.R. Kshirsagar).
Contents lists available at ScienceDirect
Computers and Electrical Engineering
journal homepage: www.elsevier.com/locate/compeleceng
https://doi.org/10.1016/j.compeleceng.2022.107785
Received 16 September 2021; Received in revised form 26 January 2022; Accepted 1 February 2022

2
principle is to decay discrete-time signals. This algorithm aims to decompose EEG signals into a set of orthogonal wavelets to its
translations and scaling. Thus, it is suggested for feature extraction. Support vector machine (SVM) is one of the best machine learning
algorithms used mostly for pattern recognition. SVMs can also be applied to classification problems where a more effective technique
for data classification and prediction of the mental task will be acquired.
1.1. EEG signals
An electroencephalogram is the fundamental building block for BCIs. For most of the parts, the EEG acknowledgement strategy
includes highlight extraction from EEG signals and characterization of various mental tasks. Electroencephalography is a technique
utilized as a part of estimating the electrical movement of the brain. Billions of nerve cells called neurons create this action. The
estimation of neurons mirrors the electrical action of the human mind. It is a promptly accessible test that additionally confirms how
the brain capacities function over time. Amid EEG tests, the number of the little disc called electrode set on various areas on the scalp
surface with brief paste.
These electrodes are made up of Ag-AgCl and are associated with an amplifier. Electrical signs from the human brain changed into
wavy lines on PC screen and the outcomes are recorded. Fig. 1 speaks about the position of anodes that assume the vital part in EEG.
Diverse lobes of the cerebral cortex are in-charge of preparing distinctive sorts of exercises. In this manner, the electrode’s position on
the mind is resolved by a universal 10–20 framework. In the proposed method, sampling period and frequency are provided equally in
such a way that all samples are converted to equally spaced Fourier based transforms. Also classification is processed based on different
frequency signals that allow the users to process a complex value added function in classification of images. Since the analysis is made
using simulation setup, Fourier transform services are implemented for observing the input and output values and absolute images are
converted into continuous signals thus making the process to be better when compared with discrete transforms. The mathematical
model for conversion process implements the power spectral density for classifying the selected signals using four different frequency
bands. The relationship of EEG with Fourier transform can be established using the windowing function as,
EEGi =
1
n
∑
n
i=1
ρ2
(i) (1)
Where,
ρ(i) denotes the windowing function for ith iteration
After determining the windowing function in Fourier transform, the power spectral density for similar periods can be determined
using the following Equation,
PSDi =
∑
n− 1
i=1
liρi
[
e− j2πfi
]
(2)
Where,
li represents the sample periodic length in combination with normalized factor values
In digital processing of EEG signals it is required that in pre-processing step, filters should be used in the corresponding frequency
ranges. Since more amount of noise is present in the classified signal, a set of casual filters is used. Further in three-dimensional filtering
scheme, frequencies are varied with respect to distance of electrical field where Laplacian values are calculated using finite difference
values. Then a common average value is represented and it is estimated on spherical line based on location of the electrodes. This
filtering method is used to overcome the issues in active development process and any disturbance in these state conditions will be
Fig. 1. Experimental setup for B-alert machine.
H. Manoharan et al.

3
avoided. Moreover here the filters are used only for avoiding noise in classified signals and it is not executed in a single iteration case.
Therefore, this filter can be applied directly without any biasing terminologies.
1.2. Discrete wavelet transform
For exploration and amalgamation, adequate information should be provided where an original signal can be recovered with a
reduction in the computational time. In the biomedical meadow, EEG signals frequency is distinctive among the most imperative
parameters to examine the brain exercises and it relies on fundamental requirement of Fourier transform. As EEG signal is non-
stationary, Fourier transform is not a reasonable strategy for discrete signals. As a result, the wavelet transform is the best appro
priate system for additional processing of EEG signals. It is productive for the nearby investigation of non-stationary and quick
transient wide-band signals. Wavelet transforms shown in Fig. 2 depicts about the next logical advance i.e a windowing method with
variable-sized areas.
The major importance of including medical image in the proposed method is that the classified mental task procedure can be used
for early detection of diseases and even diagnosis can be accomplished at earlier stage. In addition, it is possible to identify the presence
or absence of a disease using the principle of medical detection. Since the images are processed using a computer based system, it is
necessary to recognize appropriate images and enhanced procedure should be practised. In line with the above concern, the image
detection system will have high expanse of noise not treatable by normal procedural methods. Therefore, a new bio medical signal
detection technique has been incorporated to extract all features within a short period of time. Moreover, the applied signal detection
procedure saves the time of analysing various functions of different body components in the system.
1.3. Significance of DWT
In the classification method, multi-model signals are introduced for intensifying the delivery of decomposition in terms of scale and
orientation. To make a common methodology, a three dimensional setup has been introduced for decomposition technique by
calculating the convolution represented by the impulse response. Further wavelet decomposition techniques have been employed in
many engineering applications generally in image processing and compression techniques where fine details of all images are
collected. In addition, even though filters are used, wavelet decomposition can be able to further mitigate the noise present in the
classification methods. The primary purpose of discrete wavelet transform is to check the time and frequency periods using transient
analysis where all non-stationary signals are converted to stationary on the basis of incoherence. Further multi resolution images are
present in mental task process which helps to increase the computational effect even at low frequency bands. This type of discrete
wavelet transform is used for boosting the signals to large extent with high compressions standards. Also another major intention of
introducing DWT is to monitor the activities in brain without any divergence. Since most of the processes in classification of images are
based in time and frequency, it is necessary to use a multi modal analysis which is possible by DWT. While monitoring the movement of
signals, more extents of non-stationary values can be observed to make the process stationary. It is necessary to use DWT. Moreover, all
physical details of various signals can be easily identified using DWT where a varying window size can be recognized without any loss
of information. Subsequently DWT can be defined as a representation of continuous time signals varying with respect to time scale
series. Thus the time series of DWT can be represented in mathematical form as,
Δ(t) =
∫n
i=1
y(t)φ(t, n)dt (3)
The above equation represents the operation of DWT with corresponding variables which are represented as a part of continuous
signals and the integral components represent that entire system to be divisible into separate branches that represent the structure of
node trees.
1.4. Support vector machine
An SVM is a controlled machine learning that has a high capacity for providing solutions related to statistical learning. The SVM is
mostly applied for classification of problems where different types of patterns can be obtained either in linear or non-linear modes.
Fig. 2. Wavelet transform.
H. Manoharan et al.

4
Also, the neural network is most commonly used in classifier technique. But this technique suffers from a drawback that local optima
solutions cannot be obtained within a short span of time and it is challenging to find the number of neurons in one complete network.
During this process, the convergence rate will also be much higher. Thus, occurrence of an exceptional solution has least probability.
These classification problems are designed to provide an output obtained in previous cases using wavelet transform where the term
‘class’ is used to represent different mental tasks performed by the BCI operator. The outcome of the classification process is char
acterized as ‘classifiers’ to detect feature vectors related to the set of the training processes.
1.5. Paper organization
In the put forth article, remaining parts of the sections are organized as follows: Section 2 analyses the conventional works of
mental task procedure and new mathematical models are designed in Section 3 which determines the flow of proposed methodology.
The methodology is appraised using real time simulation results with pre-determined data in Sections 4 and 5 concludes the research
work on task classified mechanisms.
2. Relevant works
For classifying the different mental tasks, new techniques for evaluation processes such as multi-dimensional procedure with
classification mechanisms for integrating brain systems with SVMs have developed [1,2]. After introducing the technique mentioned
above, it is necessary to filter the noise present in the EEG data segment [3]. This filtering process is possible only when newton
methods with unbiased terminologies are integrated [4]. For such a filtering process, efficiency will be increased to about 95% in
C-SVM and 70% for standard SVM. Therefore, for segregating anomalous and standard issues, C-SVM is used where SVM is best
suitable for classifying images in cognitive methods and motor-powered tasks [5]. In the proposed method, six illusory motor tasks,
two moving tasks and three intellectual tasks are defined. Whereas a decomposition technique has been applied for mental task
classification and wavelet packets are distributed for execution of two different tasks namely cognitive and affective. The accuracy for
the motor task is 83.0%, the cognitive task is 70.6% and the effective task is 86.5%. Fault data is expected to be improved only by
expanding the training set with noise present in output time periods.
In addition, energy signals were normalized during the decomposition process after excluding necessary packets. But, if moving
tasks are neglected, it is expected to become tedious to classify sub-bands in the entire region. The computational footprint is probably
higher due to high dimensional image. Therefore, to avoid high dimensional image, a new tack for classifying cognitive, motor-
powered and moving tasks are introduced [6] and EEG signals are classified using the entropy process. Also, wavelet decomposi
tion is performed using seven different decomposition methods where all channels have EEG of db4. Since four different spectrums are
allocated, an entropy-based algorithm is used for representing each band. The outputs of each entropy process are fedback as input for
testing the trained data. In an experiment consisting of five different mental tasks, two different subjects are conferred on an accuracy
of 93% and 88% and are compared with SVM using different kernel levels for obtaining better performance.
In this study, [7] describes BCIs feature extraction and classification of EEG signals using wavelet transform, SVM and artificial
neural networks are used. In this research, five different methods are used for detecting trials containing artefacts. Finally, two
different neural networks and SVM classify the features that are extracted by wavelet transform. In comparison with the neural
network classifier, SVM classifier has better training and test accuracy because of the nature of SVM classifier. This classifier is more
general than the neural network. This specification is significant in the use of classifiers. The study proposed by [6,7] results in the
classification of EEG signal using wavelet transform with the application of an artificial neural network. The artificial neural network
used in the classification is a three-layered feed-forward network which implements the back-propagation of error learning algorithm.
After training, the network with wavelet coefficients correctly classified over 66% of the normal class and 71% of the schizophrenia
class of EEG’s. The wavelet transform thus provides a potentially powerful technique for preprocessing EEG signals prior to
classification.
The system proposed by Xu et al. [8] is based on the classification of left and right motor imagery EEG signals using wavelet-based
features along with fuzzy SVM as a classifier. The feature vectors calculated by the wavelet coefficients of the EEG signals in two
sub-bands reflecting the β and μ rhythms were used as inputs. FSVM and SVM classifiers with the wavelet-based feature extraction
outperform the winners of BCI competition 2003. The FSVM classifier with the put forth features ranked second behind the first
winning method of the BCI competition 2005 in terms of mean maximal MI steepness in three subjects of the Graz dataset. In contrast,
FSVM and SVM classifiers perform much better than the first winning method on the EEG signals from the subject O3 according to the
maximal MI steepness criterion. The FSVM classifier outperforms the SVM method. Therefore, the FSVM classifier is a promising
method and provides a new way to classify EEG signals inBCIs.
Rahman et al. [9] proposes the comparison of feature selection and classification methods for a BCI driven by non-motor imagery.
The obtained result by SVM and DWT is quite adequate for the classification of BCI. Further, the mental task classification method
using reflection coefficients is deliberated by [9] to enhance the classification of high-frequency signals. A reflection coefficient is
utilized for classification instead of auto regressive parameters in addition to high-frequency signals. For attaining classification re
sults, auto correlation function avoids matrix inversion. Even though the classification function provides the advantage, no real-time
database obtained is considered to be a major drawback. Moreover, the classification of EEG signals is much precise if a pattern
recognition technique is introduced. Thus, [10] includes the same classification technique where a wavelet-based feature extracts all
decompositions and necessary co-efficient with computation of corresponding wavelet energies. But the method is not intended for
saving testing time because wavelet energies are stabilized to the value of zero with a unit variance computation by Fisher’s
H. Manoharan et al.

5
discriminant ratio (FDR) and principal component analysis.
To mitigate the disadvantages of the existing method, [11] puts forth mental task classification using independent components
extracted from mixed speech signals. The channel signals obtained from EEG electrodes is an ensemble of brain signals, but the same
signals result during EEG classification, generating the same outcomes as that in existing methods. However, direct application of
extraction is procured owing to the classification of independent components. A noble four-channel approach has also been imple
mented by Saeed et al. [12] where five different channels classify mental tasks. For classifying long-term human stress, SVM is put
under use and the accuracy of prediction is investigated to be much higher than existing methods [13–20]. In real-time, the tack is
analyzed with 33 disordered persons, and SVM is proven to be much better in classification. Thus, machine learning techniques help to
classify the mental tasks forming the base work of the put forth approach.
3. Proposed mathematical model
The approach helps physically paralyzed patients as their body is hindered but the mind depends on working conditions. In this
proposed work, EEG signal data from the brain is received from two different paradigms during online (BCI) and offline modes (B-
Alert). Therefore, for the EEG signals a time invariant auto regressive model is constructed applying input noise with zero mean and
variance as in Eq. (1).
ARi(n) =
∑
n
i=1
ARi(n − 1) + Ni (4)
Where,
ARi(n) and ARi(n − 1) denotes the past and present time period of EEG signals
Ni represents the input noise present for extracting the feature of individuals
Since Eq. (1) denotes the auto regressive function an autocorrelation function with full length value as in Eq. (2).
σ(i) =
1
li
∑
n
i=1
ARi(n) ∗ ARi(n + 1) (5)
Where,
li denotes the total length value of autocorrelation function
ARi(n + 1) represents the next time period that is present in the loop
In Fig. 3, the brain signals applied to EEG machines in progress to BCI paradigm and B-alert machine. Once the paradigm values are
Fig. 3. Flow of methodology.
H. Manoharan et al.

6
obtained with output of B-alert machine, EEG signals are classified by supplying different electrodes on an average of 10 to 20 periods.
After this classification mechanism, DWT is applied for feature extraction where complete noise in the images is removed. Then the
output vectors directed to the classifier separating two distinct materials namely grey and white. Finally the classified images detect
the mental task of every individual.
In this research work, we used DWT for feature extraction on EEG signal to get feature vector instead of using different feature
extraction techniques like principal component analysis, genetic algorithm (GA), ICA, particle swarm optimization (PSO) and hybrid
combination of GA and PSO. For mental task classification, SVM yields better results than other classifiers available in other researches
like neural networks, fuzzy logic, linear classifier, and linear discriminant analyzer. In case if mental task is classified with respect to
kernel function, then an empirical value will be used and it can be modeled as,
α(x) =
∑
n
i=1
e(i)Ki(x) + N (6)
Where,
e(i) and Ki(x) denote the empirical value and kernel value for the classified functions
Eq. (3) represents the reference functionality for the defined kernel function but the original signal is found using residue signal as
in Eq. (4).
ri =
∑
n
i=1
di(n) − IMF(i) (7)
Where,
di(n) represents the decomposition function of classified signals
IMF stands for the intermediate functions extractable from the original signal
Eq. (4) represents the relationship for connecting a single channel but in proposed work exact scenario depends on multiple channel
relationship to be established in multicasting mode. Therefore, for multi-channel relationship Eq. (5) is jotted down as,
r(i, n) =
∑
n
i=1
ϵ(i, n)
̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
ϵ(i, i) ∗ ϵ(n, n)
√ (8)
Where,
ε(i, n) denotes the channel capacity of ith and nth matrices,
Since multiple channels are accessed for classifying different tasks using EEG signals the probability of classification to be measured
is calculated using Eq. (6) as follows,
ρ(i, n) =
1
nr
(9)
Where,
nr represents the number of samples per unit time
Eq. (6) indicating the number of samples per unit time should be equal to 1 which in turn indicates that probability of classification
should always be equal to 1. The decision vector of classification mechanism is enhanced by a percentage degree of 95 to achieve a
good classification mechanism. In the projected technique, both classification and pattern recognition problems are maximized using a
set of classifiers. If data is located at farther distance, margin is maximized for separating the tuples from set of available data. The
process of estimating optimum hyper plane is mathematically represented as,
ϑt
xi = 0 (10)
Where,
xi represents a set of initial data points
Once the data points are initialized, the number of hyper planes are selected thus in the researched tack, three different hyper
planes are selected and above mentioned equation is modified as,
ϑ ∗ t + z = τ (11)
Where,
τ denotes the binary data points assigned as -1 and +1
After maximizing the data points, the margins are maximized using SVM by varying the bias values as follows,
βi =
∑
n
i=1
number of support vectors
n − 1
(12)
Thus if above mentioned three steps are accomplished, both the separable and non-separable data are included for to achieve a non-
trivial solution for pattern recognition problems. The whole classification mechanism detects the motion of individuals where it seems
difficult to get the reflection of signals using ensemble of sampled function. Nevertheless, an auto regressive model is applied for all
techniques as a common procedure and therefore as an advanced method automatic technology detects the reflections at different
H. Manoharan et al.

7
points. Since the classified values are difficult to operate in manual mode of operation, this type of co-efficient detects 10 different
sequences at a frame rate of 24fps. The reflection co-efficient for classification is mathematically expressed as,
Ri =
∑
n
i=1
[αicosθ − βicosθ]
/
[γicosθ − nicosθ] (13)
From the above equation, events are obviously classified into four different types namely alpha, Beta, Gamma and total number of
classifications of n different variables. But in autoregressive model, the none distributing symbol period yields the rate of probability
difficult to be computed. Therefore, a computational language using machine learning is integrated in research tack based on non-
linear equations.
The classifier signals in FSVM evaluate the best membership functions from all available data set in the system. However, an
interface is not provided during the classification technique thus it is classified after BCI. Beside membership functions, FSVM classifies
the signals using fuzzy logic systems to be mathematically expressed as follows,
∑
n
i=1
ωin = 1 (14)
The above equation represents a membership function where the values are set to 1. Subsequently, the membership function is
directly associated with distance of separation and after each iteration it gets updated the equation is modified as,
ωin =
∑
n
i=1
1
(ωi, xi)2
(15)
Where,
xi represents a range of values of points varying from local minima
In the proposed method a clustering algorithm is applied for separating different classes of variances based on the scattering
contrivance. In this tack high dimensional data is projected in a single line where the distance is maximized between two different
classes using a linear projection system as represented in the following equation.
FDRi =
∑
n
i=1
(M1 − M2)2
(V1 − V2)2
(16)
Where,
M1, M2, V1and V2 represent the mean and variance of different time samples
From above equation, it is stated that both inter and intra cluster data points are attained using a simple classifier and as a result
FDR provides high accuracy rate. If the rate of dimensionality is higher, it is separated in two different parts thus following individual
data points i.e., an initial value is defined and if the value goes higher than expected one, it is classified to corresponding set of groups.
3.1. EEG signal by experimental BCI competition paradigm
The dataset contains information from three diverse ordinary subjects amid four non-criticism sessions. The subjects are assembled
in a typical seat and loose missiles lying on their poles. There are three undertakings:
(1) Imaginings of the monotonous self-paced movements in the left hand (left, class 2)
(2) Imaginings of tedious self-paced movements in right hand (right, class 3)
(3) The cohort of disputes, with the beginning of a similar random letter (word, class 7).
Every one of the four sessions for a given subject is obtained around the same time, each enduring four minutes at a period of 5–10
min. As per the administrator’s demand, each subject is asked to play out a given undertaking for around 15 s and after that, arbitrarily
changed to another assignment. Therefore, EEG information is not split in preliminaries since the subjects are constantly playing out
any psychological task haphazardly. The calculation ought to give EEG signal output in every 0.5 s utilizing the last second of in
formation. EEG signals recorded with a framework of 32 integrated electrode terminals put to the brain as per standard places of the
international 10–20 framework. The examining rate was 512 Hz. Signs procured at full DC. No artefact rejection or revision is utilized.
The main functionality of BCI is to acquire the signals from brain, convert them to a different form in the form of commands. The
contextual motive for the abovementioned conversion process is the preferred action to be taken at the output. BCI acquires signals
using four distinct modes contributing acquisition of signals, extraction of images, transition of images to suitable form and analysing
the segmented output. Also, BCI is preferred to solve the problem of disability present in elderly people and this type of computer
interface will monitor the differences that are present in central part of brain.
3.2. Back propagation for classification signals
In the projected tack a three layered feed forward mechanism is incorporated for detecting the errors in classification signals. Here,
the image classified with respect to different probabilities where all features of the image can be extracted with the three layers namely
H. Manoharan et al.

8
input, hidden and output layers. The relationship can be established using the parameters as,
W(i)→
f
f(W, X, Z)→
f
Z(i) (17)
Where,
X and Z denotes the parameters that are used for classification of images
If the above mentioned equation is assimilated, the images are classified based on the efficient detection process. Initially the same
tack is applied for classifying seven different face recognition systems yielding a maximum value of 1 for appropriate images with three
different neurons. All information is processed in forward direction on the basis of different sampling periods. Further a non-linear
model is implemented for classification process which indicates that hidden layers change the data representation for defined func
tions in all three layers. The model of three layered neural network is elucidated in Fig. 4.
3.3. EEG signal by experimental B-alert EEG machine
A 25 year old candidate is put under investigation as indicated by the administrator’s demand, the person (subject) is asked to
perform five distinctive mental assignments like creative activities of the left hand, right hand, left leg, right leg and jaw development.
The experimental setup for EEG samples is carried out in CARE Hospital, Nagpur. Fig. 5 shows EEG task gained from the B-alert
machine considering nine channels. As indicated by mental movement, the mind signals produced and recorded by the B-alert machine
are exhibited in Fig. 6 and the readings of nine channels F3, Fz, F4, C3, CZ, C4, P3, POz, and P4 are plotted in a graph representing axises
along the abscissa as time variable in minutes and the ordinate as amplitude of signal in micro volt. Each of the four assignments is
taken for over three minutes and the fifth task is taken for less than three minutes with sampling rate of 256 Hz.
The proposed method is an advanced processing technique for detecting cognitive state matrices. Therefore, in this case a B-alert
wireless acquisition tool provides output states within a short period of time. Also when compared to other conventional methods, B-
alert provides high signal quality irrespective of work sections. Since the work sections are classified on the basis of offline and online
systems. Most of the users are able to analyze this B-alert state by combined techniques highly flexible for offline users. The integrated
B-alert machine of 20 different channels is operated and since three neurons are reflected, only three channels are assumed to be idle.
Further the encumbrance of B-alert is less than 110 g so it can be easily placed at top, sides and forehead of human bonce. Once it is
placed in the desired location, both the midpoint and adjacent lines allow EEG signals for exploiting multiple prospects at 256 Hz.
Subsequently, as EEG signal varies with different time periods, an autocorrelation function provides consequences as inverse of
power spectrum. It is also unconditional that if voltage levels are varied, the static results are not attained. Therefore, an autocor
relation function is preferred for stationary process which is expressed in mathematical terms using measurement test as,
τ2
=
∑
n
i=1
(
e2
i − o2
i
)
ei
(18)
Where,
ei and oi denote the expected and observed values of different time periods
The formulated auto correlation function for EEG signals denotes frequency of event occurrence to be integrated with chi square
distribution. The major advantage of integrating frequency components in EEG is reduction in periodic components with noise
increment in signal detection method.
Fig. 4. Three layer feed forward neural networks.
H. Manoharan et al.

9
3.4. Feature extraction techniques using DWT
Results are obtained after applying the wavelet transform. For B-alert machine, waveforms are obtained by applying coiflets and
‘coif3 with 4th level decomposition as shown in Fig. 7. The plot below exibits approximation (A4) and detail (D1–D4) of raw EEG signal.
Notes: For left-hand imaginary and right-hand imaginary movement for subject 1 detailed coefficients for four levels and
approximation coefficients at 4th
level. Result obtained after applying the wavelet transform. For BCI competition III waveforms are
obtained by applying Daubechies ‘db3’ with 8th level decomposition. The plots in Fig. 8(a) and (b) show approximation (A8) and detail
(D1–D8) of raw EEG signal for right and left-hand movement, respectively. In most of the classification techniques image extraction is
managed by reducing the number of dimensions that are managed by a set of resources. If feature extraction is merged with classi
fication segment, large number of variables are handled, thus allocating sufficient resources for generation. Also feature extraction is
applied to record the motion of digital images without losing any important classified data.
Moreover, the type of feature selection in the proposed tack is used with forward transmission process where missing value ratio is
sensed. In line with above concern, feature extraction is carried out with large set of data where in the first step, data is standardized.
Once data is standardized feature extraction process is carried out with a set of covariance matrix and in the matrix all Eigen values are
arranged in descending order. The above mentioned step is carried out for detecting the changes present in few signals and when the
signal changes for each and every time, the values are arranged in descending order which proves that normalized features are
extracted from the classified images. It is observed form Fig. 5 that a pair of electrodes is used for classifying the mental task where any
difference between voltage signals is calculated with respect to changes in reference electrode. Also raw EEG signals in Fig. 6 are
plotted with a horizontal meridian denoting as upward direction of corresponding waves. Further after applying the electric signals it is
observed that within a short time electrical movements are generated but negative values of reference electrodes are present. In Fig. 7,
the electrical movement of signals are captured, thus in y-axis voltage is represents and after feature extraction time period is measured
in x-axis. Suppose if irresistible conditions are present, more number of filters are used to avoid interference activity of all the selected
Figu. 5. Experimental setup for B-alert machine.
Fig. 6. Raw EEG signal.
H. Manoharan et al.

10
neurons.
However, the neurons are not activated using the basic Neuron functions therefore EEG has been integrated with three layer neural
networks and in this case both grey and white matters are wed out by feeding local current plotted in Figs. 7 and 8. In final stage, the
separated matters are combined with potential activities using different frequency ranges i.e., when eyes are closed, a particular
frequency is generated and in this case, the alpha parameters are segregated. In the next case, if the movements are detected, three
different parameters are obtained and it is defined as the main characteristic of EEG signals.
3.5. Classifier using SVM
Classification using SVM is mainly integrated for assigning outputs observed in the previous state. Thus, type of allocation is an
automatic mechanism belonging to a class of mental task being performed by BCI. An algorithm termed as classifiers is used for
automatic integration to identify the different classes of the feature vectors. Pattern recognition technique is one of the important
applications of machine learning algorithm where SVM secures high efficiency for simplification process because all neural networks
are combined in comprehensive mode. An optimum hyperplane for classification problem and pattern recognition are developed for
maximizing the boundary so as to mark the next adjacent hyperplane. If the margins are maximized, the classification of patterns
becomes much easier for providing a highly efficient process.
4. Results and discussion
BCI competition III database V was taken for three different subjects available on open source. An applied different mother wavelet
technique for feature extraction, different kernel functions and algorithm of SVM for classification of the different mental tasks are
presented in Tables 1–3 for three different subjects by BCI competition III database V.
For BCI competition III database V, two tasks namely left hand and right-hand imagination movements are considered. The feature
is extracted by applying different types of wavelet families and classification is carried out with different kernel functions for SVM. By
applying symlets, Daubechies, coiflets wavelet families and different kernel functions like linear, polynomial in addition to the
application of different SVM algorithms like least square, quadratic programming, the accuracy is enhanced. The highest accuracy for
subject 1, subject 2 and subject 3 are obtained to be 91.6667%, 66.6667% and 70.2128%, respectively.
The B-alert machine is employed for two different subjects of real-time databases. Tables 4 and 5 present an applied different
mother wavelet technique for feature extraction with varying SVM functions for the classification of different mental tasks for two
different subjects by B-alert machine. For the B-alert machine, two tasks, namely, left-hand and right-hand movement, are obtained
Fig. 7. Approximation and detailed coefficient of coif lets order four.
H. Manoharan et al.

11
from the B-alert machine for two persons aged between 20 and 30 years. For feature extraction, wavelet families like coif lets,
Daubechies and symlets are used for classification by applying kernel functions like quadratic, linear and polynomial. By applying the
SVM algorithm like least square, sequential minimal optimization, the accuracy is increased. The highest accuracies are observed to be
97.2222% and 61.1111% for subject 1 and subject 2, respectively.
5. Conclusion
A new method of classifying mental tasks is analyzed and solved using two different methods subject to constraint under three
subjects of level 1 and 2. The major importance of the proposed work relies on classification technique where, DWT is integrated for
Fig. 8. Approximation and detailed coefficient of Daubechies order eight.
H. Manoharan et al.

12
acquiring optimal solutions. Also, a system is designed not only to classify different signals but also to predict the signals for different
mental tasks in paralyzed patients. This type of design is wide comfortable for testing various EEG signals and the observed results are
also plotted. The proposed method saves the testing time and it is consistent in all cases. The proposed method is much helpful to all
doctors as the tasks are classified within short duration of time at both analyzed levels. In case of B-alert machine with SVM algorithm
Table 1
Result for subject 1.
S.No Wavelet Transform Support Vector Machine Accuracy
Type of mother wavelet 8th level approximation co efficient Kernal Function Algorithm
1 db3 1 × 8 Linear Quadratic programming 91.6667%
2 sym3 1 × 8 Linear Quadratic programming 91.6667%
3 db3 1 × 8 Linear Least square 91.6667%
4 sym3 1 × 8 Linear Least square 91.6667%
5 db10 1 × 22 Quadratic Least square 68.75%
6 db5 1 × 12 Polynomial Least square 47.9167%
Table 2
Type of mother Wavelet 8th
level approximation co efficient Kernal Function Algorithm
1 sym4 1 × 10 Polynomial Least square 66.6667%
6 db2 1 × 6 Radial basis function Least square 52.0833%
7 sym5 1 × 12 Quadratic Least square 54.1667%
Table 3
Type of mother wavelet 8th
level approximation co efficient Kernal Function Algorithm
1 coif1 1 × 8 Polynomial Least square 70.2128%
2 db9 1 × 20 Linear Quadratic programming 56.3830%
Table 4
Type of mother wavelet 4th level approximation co efficient Kernel Function Algorithm
1 coif1 1 × 76 Quadratic Sequential minimal optimization 97.2222%
3 db5 1 × 80 Radial basis function Sequential minimal optimization 73.6111%
4 db6 1 × 80 Radial basis function Sequential minimal optimization 73.6111%
Table 5
Type of mother wavelet 4th level approximation co efficient Kernel Function Algorithm
2 db5 1 × 80 Polynomial Least square 61.1111%
3 db6 1 × 82 Polynomial Sequential minimal optimization 61.1111%
4 sym6 1 × 82 Polynomial Sequential minimal optimization 61.1111%
7 coif4 1 × 93 Radial basis function Least square 55.5556%
H. Manoharan et al.

13
the accuracy of test bench is greatly enhanced above 90%. Even during other comparative analysis, a sequential optimization is not
arranged in existing methods to prevent the accuracy from reaching the expected increment in percentage functions. Also the per
centage of accuracy is based on frame rate inclusion after applying neuron function for better real time classification as number of
samples is much lesser for all brain interface computer applications. In medical applications all EEG signals are restructured with low
cost model and the exact analysis of special case patients in hospitals is easily evaluated if they are self-confessed in infirmaries. This
method is easy to implement in all hospitals as it carries easily installable trivial equipment. In addition if much lesser samples are
loaded in the application initially, the load is balanced in ordered to be easily transported. Further, in future the proposed design is
expendable for the classification and prediction of chaotic benchmark databases.
Declaration of Competing Interest
The authors declared that they have no conflicts of interest to this work.
Acknowledgments
The author thankfully acknowledges the Deanship of Scientific Research, King Khalid University, Abha, Asir, Kingdom of Saudi
Arabia for funding the project under the grant number RGP 2. /58/42.
References
[1] Su C, Xu Z, Pathak J, Wang F. Deep learning in mental health outcome research: a scoping review. Transl Psychiatry 2020;10. https://doi.org/10.1038/s41398-
020-0780-3.
[2] Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, et al. Artificial intelligence for mental health and mental illnesses: an overview. Curr Psychiatry Rep 2019;
21. https://doi.org/10.1007/s11920-019-1094-0.
[3] Cabrera AF, Farina D, Dremstrup K. Comparison of feature selection and classification methods for a brain-computer interface driven by non-motor imagery.
Med Biol Eng Comput 2010;48:123–32. https://doi.org/10.1007/s11517-009-0569-2.
[4] Chen X, Chen J, Cheng G, Gong T. Topics and trends in artificial intelligence assisted human Brain research. PLoS One 2020;15:1–27. https://doi.org/10.1371/
journal.pone.0231192.
[5] Sherwood J, Derakhshani R. On classifiability of wavelet features for EEG-based brain-computer interfaces. In: Proceedings of the international joint conference
on neural networks.; 2009. p. 2895–902. https://doi.org/10.1109/IJCNN.2009.5178939.
[6] Bickman L. Improving mental health services: a 50-year journey from randomized experiments to artificial intelligence and precision mental health. Adm Policy
Ment Health 2020;47:1–49. https://doi.org/10.1007/s10488-020-01065-8.
[7] Kousarrizi MRN, Ghanbari AA, Teshnehlab M, Aliyari M, Gharaviri A. Feature extraction and classification of EEG signals using wavelet transform, SVM and
artificial neural networks for brain computer interfaces. In: Proceedings of the international joint conference on bioinformatics, systems biology and intelligent
computing, IJCBS; 2009. p. 352–5. https://doi.org/10.1109/IJCBS.2009.100.
[8] Xu Q, Zhou H, Wang Y, Huang J. Fuzzy support vector machine for classification of EEG signals using wavelet-based features. Med Eng Phys 2009;31:858–65.
https://doi.org/10.1016/j.medengphy.2009.04.005.
[9] Rahman MM, Chowdhury MA, Fattah SA. An efficient scheme for mental task classification utilizing reflection coefficients obtained from autocorrelation
function of EEG signal. Brain Inform 2018;5:1–12. https://doi.org/10.1007/s40708-017-0073-7.
[10] Amin HU, Mumtaz W, Subhani AR, Saad MNM, Malik AS. Classification of EEG signals based on pattern recognition approach. Front Comput Neurosci 2017;11:
1–12. https://doi.org/10.3389/fncom.2017.00103.
[11] Qu H, Shan Y, Liu Y, Pang L, Fan Z, Zhang J, et al. Mental workload classification method based on EEG independent component features. Appl Sci 2020;10.
https://doi.org/10.3390/app10093036.
[12] Saeed SMU, Anwar SM, Khalid H, Majid M, Bagci U. EEG based classification of long-term stress using psychological labeling. Sensors 2020;20:1–15. https://
doi.org/10.3390/s20071886 (Switzerland).
[13] Gupta A, Agrawal RK. Relevant feature selection from EEG signal for mental task classification. Lect Notes Comput Sci (Incl Subser Lect Notes Artif Intell Lect
Notes Bioinform) 2012:431–42. https://doi.org/10.1007/978-3-642-30220-6_36. 7301 LNAI.
[14] Tao J, Yin Z, Liu L, Tian Y, Sun Z, Zhang J. Individual-specific classification of mental workload levels via an ensemble heterogeneous extreme learning machine
for EEG modeling. Symmetry 2019;11. https://doi.org/10.3390/sym11070944 (Basel).
[15] Vatsal R, Gupta MK. Motor imagery and mental task classification. Algorithms Non Invasive Brain Comput Interface 2020;7:2437–43.
[16] Davenport T, Guha A, Grewal D, Bressgott T. How artificial intelligence will change the future of marketing. J Acad Mark Sci 2020;48:24–42. https://doi.org/
10.1007/s11747-019-00696-0.
[17] Huang Z, Wang M. A review of electroencephalogram signal processing methods for brain-controlled robots. Cogn Robot 2021;1:111–24. https://doi.org/
10.1016/j.cogr.2021.07.001.
[18] Ke Y, Jiang T, Liu S, Cao Y, Jiao X, Jiang J, et al. Cross-task consistency of electroencephalography-based mental workload indicators: comparisons between
power spectral density and task-irrelevant auditory event-related potentials. Front Neurosci 2021;15:1–14. https://doi.org/10.3389/fnins.2021.703139.
[19] Varshney A, Ghosh SK, Padhy S, Tripathy RK, Acharya UR. Automated classification of mental arithmetic tasks using recurrent neural network and entropy
features obtained from multi-channel EEG signals. Electronics 2021;10:1079. https://doi.org/10.3390/electronics10091079.
[20] Holtrop JS, Scherer LD, Matlock DD, Glasgow RE, Green LA. The importance of mental models in implementation science. Front Public Health 2021;9. https://
doi.org/10.3389/fpubh.2021.680316.
Hariprasath Manoharan is working as Assistant Professor in the Department of Electronics and Communication Engineering, Panimalar Institute of Technology,
Poonamallee, Chennai,. He has published 32 research articles in well indexed Journals. He has also published a book entitled ‘Computer Aided State Estimation for
Electric Power Networks’ and published more than 15 Patents. Email: hari13prasath@gmail.com.
Dr.Sulaima Lebbe Abdul Haleem is currently serving as Senior Lecturer in Information & Communication Technology at the Faculty of Technology, South Eastern
University of Sri Lanka He is the prime architect of many honors degree curriculum at the Faculty of Technology, and also contributed at the Faculty of Applied Sciences,
Faculty of Engineering, Faculty of Management & Commerce. Email: ahaleemsl@seu.ac.lk.
Dr. S. Shitharth received his Ph.D. degree in the Department of Computers Science & Engineering, Anna University. He is currently working as Associate Professor in
Kabri Dehar University, Ethiopia. He has published more than 10 International Journals along with 3 patents. He is also an active member in IEEE Computer society and
in 5 more professional bodies. Email: shitharth.it@gmail.com.
H. Manoharan et al.

14
Pravin R. Kshirsagar presently serving as Professor in the Department of Artificial Intelligence, G.H. Raisoni College of Engineering, Nagpur. He worked as Vice-
Principal, Dean, Head of the Department in various Institute across India. He has served as reviewer in many international journals such as Inderscience, Spinger,
Elsevier and IEEE Transactions. Email: pravinrk88@yahoo.com.
Vineet Tirth is working as Associate Professor in Mechanical Engineering Department, College of Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia
since 2015. He has over 50 research publications, 5 patents and more than 25 presentations and invited talks in conferences and seminars. He has completed 15
sponsored research projects and organized many national and international conferences. Email: vtirth@kku.edu.sa.
Dr.Thangamani has published nearly 150 articles in indexed journals and presented over 120 papers in national and international conferences in above field. She has
delivered more than 80 Guest Lectures in reputed engineering colleges. She has got best paper awards, best scientist award and best researcher award from various
education related social activities in India and Abroad. Email: manithangamani2@gmail.com.
Dr. Radha Raman Chandan is currently working as Associate Professor & Head at the Department of Computer Science & Engineering, Shambhunath Group of In
stitutions, Prayagraj, India. He has also received various reputed awards in his profession & academics. He has been invited as a key resource person in various National
& International Workshops & Conferences. Email: rrcmiet@gmail.com.
H. Manoharan et al.

A machine learning algorithm for classification of mental tasks.pdf

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Similar to A machine learning algorithm for classification of mental tasks.pdf

Similar to A machine learning algorithm for classification of mental tasks.pdf (20)

Recently uploaded

Recently uploaded (20)

A machine learning algorithm for classification of mental tasks.pdf