System Architecture for Brain-Computer Interface based on Machine Learning and Internet of Things

(IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 13, No. 3, 2022
474 | P a g e
www.ijacsa.thesai.org
System Architecture for Brain-Computer Interface
based on Machine Learning and Internet of Things
Shahanawaj Ahamad
Department of Information and Computer Science
College of Computer Science and Engineering
University of Hail, Hail City
Saudi Arabia
Abstract—Brain functions are required to be read for curing
neurological illness. Brain-Computer Interface (BCI) connects
the brain to the digital world for brain signals receiving,
recording, processing, and comprehending. With a Brain-
Computer Interface (BCI), the information from the user’s brain
is fed into actuation devices, which then carry out the actions
programmed into them. The Internet of Things (IoT) has made it
possible to connect a wide range of everyday devices.
Asynchronous BCIs can benefit from an improved system
architecture proposed in this paper. Individuals with severe
motor impairments will particularly get benefit from this feature.
Control commands were translated using a rule-based
translation algorithm in traditional BCI systems, which relied
only on EEG recordings of brain signals. Examining BCI
technology’s various and cross-disciplinary applications, this
argument produces speculative conclusions about how BCI
instruments combined with machine learning algorithms could
affect the forthcoming procedures and practices. Compressive
sensing and neural networks are used to compress and
reconstruct ECoG data presented in this article. The neural
networks are used to combine the classifier outputs adaptively
based on the feedback. A stochastic gradient descent solver is
employed to generate a multi-layer perceptron regressor. An
example network is shown to take a 50% compression ratio and
89% reconstruction accuracy after training with real-world,
medium-sized datasets as shown in this paper.
Keywords—Brain-computer interface; machine learning;
internet of things; EEG; system architecture
I. INTRODUCTION
Brain-Computer Interface (BCI) also known as Mind
Machine Interface (MMI), is a technology that connects the
brain to a computer or other electronic device in order to
investigate the normal brain’s functioning, including its
original output level and muscle pathway [1]. In order to create
a link between the brain and the computer, two requirements
must be met:
 Various states of the brain should be distinguished.
 Detection and classification of similar features
practically.
It is possible to monitor brain activity using a variety of
methods, the most common of which being
Electroencephalograms (EEG), Electrocardiograms, Magnetic
Resonance Imaging, Magneto-Encephalograms, and Positron
Emission Tomograms (PET) [2].
A variety of approaches can be used to detect or measure
these electrical and chemical impulses. There are issues and
advancements to be made, just like with any other system, so it
works on them. Both the efferent and afferent systems may be
affected. The brain can be connected directly to its
environment if the neurological system is effectively bypassed
[3-5]. This can be done through BCI. While first designed to
provide alternative methods of communication for those with
disabilities, these devices now have the potential to provide
“other sensations” for those who are unable to do so. Brain-
computer interfaces (BCIs) provide direct communication
between the brain and the world around it.
A patient can use a BCI to control a specific computer
application, such as a computer cursor or a robotic limb.
Patients with lock-in syndrome, for example, can benefit from
developing a communication network even when they are
paralyzed. Many researchers have been working on BCIs to
capture and analyze EEG patterns associated with mental states
throughout the past few years [6, 7, 8, 9, 10]. EEG activity in
the left side of the motor cortex is associated with visualizing a
change in the posture of the right hand. Other often employed
mental exercises include moving the left hand, toes, and tongue
around the mouth and feet. The block diagram for BCI can be
shown in Fig. 1.
Fig. 1. BCI Framework.

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A Brain-Computer Interface (BCI) based on
electroencephalography (EEG) is used in this study. Depending
on where the electrode is placed, there are two main
approaches to collecting EEG data. Non-invasive electrodes
are attached to the scalp while invasive electrodes are attached
directly to the cerebral cortex [11]. The non-invasive approach
is less intrusive and more portable than fMRI, making it suited
for performance arts.
Improvements in biomedical signal processing have led to
Electroencephalography (EEG) signals being utilized to
diagnose brain illnesses and widely used in Brain-Computer
Interface (BCI) devices. In order to operate external equipment
like a wheelchair or a computer, BCI employs electrical
impulses from the brain, in the form of EEG waves, as an
input. Electroencephalography (EEG) is a method for
recording electrical brain wave activity along the scalp as a
result of neural activation in the brain. An
electroencephalogram (EEG) is a short-term recording of the
electrical activity of the brain for a short period of time
(usually 20–40 minutes), which is accomplished by attaching
electrodes to the scalp of the subject. EEG can be used to
diagnose a variety of neurological illnesses, although it is most
commonly used to treat epilepsy. EEG can be useful in the
early stages of some serious disorders, such as coma, brain
death, and encephalopathies. There are various different
diseases for which electroencephalography (EEG) can be used
as a first-line diagnostic approach. EEG is still the most
commonly used method for diagnosing even if MRI and CT
scans are increasingly prevalent currently. In the EEG
approach, evoked potentials (EP) are utilized to average the
EEG activity to stimulus responses in visual, auditory, or
somatosensory activity. In addition, event-related potentials
(ERPs) are employed in cognitive sciences, psychophysiology,
and cognitive psychology for averaging EEG responses to
complicated stimuli.
This paper has been organized in five sections. Section 1 is
introduction that refers the basics and contexts of research with
introduction to various ongoing and future aspects. Section 2 is
for background study and research that mentions the
contributions of significant research in the field, their relevance
and reference to this research work. Section 3 is about research
methodologies that specify the adopted research processes and
methods to carry out this research work. Section 4 and
Section 5 are about implementation and results, and
conclusion, respectively.
II. BACKGROUND STUDY AND RESEARCH
In order to identify changes in the surface of the head as a
result of electrical activity in the brain, EEG is largely used in
neuroscience and clinical neurological procedures. Wet
(gelled) silver/silver chloride electrodes (Ag/AgCl), typically
with the help of an EEG cap, are used in the state-of-the-art
approach. It takes a long time and a lot of effort to get ready for
an event like this. Electrolyte stability also limits the wear time
(gels). Brain-computer interfaces (BCIs) are a new field of
application for EEG [12-15]. Silver-chloride-coated metal
discs, often composed of stainless steel, tin, gold, or silver, are
applied to the scalp in specific places. The International 10/20
system is used to specify the positions. All electrodes are
numbered, and a letter is assigned to each one. The letter
identifies the location of the electrode in relation to the brain.
Examples include the F-Frontal lobe and T-temporal lobe.
Dry contact electrodes must have acceptable hair layer
penetration, biocompatibility, electrochemical stability, and
signal quality comparable to typical wet electrodes in order to
be viable. Aside from the long-term applicability and patient
comfort that we strive for, we also want to ensure compatibility
with bio-signal amplifiers, ease of use, and preparation time for
patients [16]. As a result, three major types of electrodes have
been created: Titanium nitride covers the first two gold multi-
pin electrodes as well as (ii) polyurethane multi-pin electrodes.
In order to make gold multi-pin electrodes, the electrical
precision brass pins are gold-coated and soldered to an epoxy
baseplate.
Fig. 2 depicts the four stages of the BCI configuration for
this project, from data collection via EEG and analog-to-digital
conversion to digital signal processing, feature selection, and
control of external mechatronic devices [17]. In the first step,
non-invasive EEG data is collected, and in the second step,
digital EEG signals are processed with filters. Custom
algorithms and mechatronics have been developed in the
second and third phases to monitor the appearance of
individual brain waves and produce the desired sounds. For
PDR detection, a unique feature selection technique is used in
the third phase. In the final stage, eight wirelessly connected
modules of custom percussion instruments produce
synchronized sounds [18]. As a result, the BCI system
generates repetitive noises that prompt the performer to record
reliable EEG data.
OpenBCI’s high-quality open-source BCI solution is
employed in this research. The system architecture is built
using frameworks that are commonly used to run BCIs.
OpenBCI’s STL files are used to 3D print the headgear used
for the initial round of EEG data collecting [19, 20, 21, 22]. A
Cyton board, an Arduino-compatible, eight-channel neural
interface with a 32-bit processor, is attached to the headwear's
rear. For example, the OpenBCI GUI can analyze the digitized
EEG data from this board to remove artifacts and identify
certain features.
Analog signals are detected by placing electrodes on one’s
scalp for EEG-based BCI. For use with the Ultra-Cortex Mark
III-Nova and Ultra-Cortex Mark IV 3D-printed EEG headgear,
electrodes are mounted according to International 10-20 system
specifications [23]. Conductive silver chloride (AgCl) is used
to coat the dry electrode, making it easier for the performer to
use and more durable for long-term performances.
Fig. 2. System Architecture for Four Stages of the BCI Configuration.

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III. RESEARCH METHODOLOGY
The Welch method uses windowed Fourier transformations
of signal segments to calculate the Fourier spectral
characteristics. Using only frequency data is the fundamental
shortcoming of the method, as it does not use time-domain data
[24]. However, studies have shown that EEG signals can be
improved by combining frequency and time-domain data. The
band power in multiple frequency bands is calculated from the
AR spectrum and the power sum is used as independent
parameters. Fig. 3 shows the block diagram of the proposed
methodology for this research work.
A. Classification
Research on BCI-specific machine learning models using
data from children with impairments is few, to our knowledge.
It was in 2020 that Aydin applied LDA, KNN, and SVM to
two fNIRS datasets containing typical developing adults
(average age 28.5 3.7 years). For motor imagery fNIRS-BCI
signals, LDA showed the maximum classification accuracy,
while SVM had the best performance for mental arithmetic.
The best KNN outcomes were achieved with a low k. It was
determined that the number of features should be proportional
to the value of k in this investigation [25]. When compared to a
single powerful learner, ensemble classifiers have improved
classification accuracy. SVM, LDA, and rLDA were compared
against bagging using four different data sets involving motor
imagery, mental arithmetic, and word production tasks. There
were no significant differences in bitrate or classification
accuracy between bagging and alternative algorithms across
the datasets. According to their last remarks, they urged the
development of novel ensemble classification methods. Seven
models were chosen in this work because of the lack of
research on classifying learners for a fNIRS-BCI. Based on
past research, we chose KNN, SVM, LDA, and Bagging as our
top four models. Random Forests, AdaBoost, and Extra Tree
were introduced to the ensemble of classifiers. 10-fold
stratified cross-validations were conducted on each model's
hyperparameters to measure classification accuracy [26, 27,
28]. Accuracy, sensitivity, and specificity were used to
measure classification performance.
B. Feature Selection
Spontaneous electrical activity in neurons, as measured by
EEG, is examined in general for its amplitude and frequency.
Using scalp EEG, the experiment collects data and focuses on
the frequency of the scalp EEG in the visual cortex. EEG data
is transformed from the time domain to the frequency domain
using the fast Fourier transform (FFT) technique. To monitor
PDR and control sound mechanisms, the Open Sound Control
protocol uses a special algorithm that analyses the frequency
information from the digitalized EEG data after the FFT
method is applied in the GUI of OpenBCI (OSC). However,
EEGs can vary greatly depending on the individual, their age,
their gender, and their overall health. There is a 7.5-15Hz PDR
tolerance. As we get older, the EEG slows down, and this
development is more pronounced for males than for women.
Young people's EEG data isn’t yet mature because they’re
under the age of 26. Children under the age of 8 Hz can have a
PDR that is slower than 8Hz. As a result, it's critical to check
the performer’s PDR scope before running the function.
Fig. 3. Block Diagram for the Proposed Methodology.
C. Dataset
The proposed approaches are evaluated using the ECoG
recordings from Dataset I of the BCI Competition III, as
depicted in Fig. 4. BCI experiment participants conducted
hypothetical motions of their left little finger or tongue to
record motor imagery signals. Recorded ECoG signals were
sampled at 1000 Hz. Amplification and storage at microvolt
values made it easier to classify the recorded potentials.
Sample recordings of either imaginary finger or tongue
movement were recorded for three seconds. To avoid visual
evoked potentials, the recording began 0.5 seconds after the
visual round ended.
D. Auto-Regressive Features
Automatic regression (AR) models are used to represent
random processes in statistics and signal processing, and they
are particularly useful for modeling time-varying processes.
Output variables are modeled by the AR to be reliant on their
prior values. AR denotes an autoregressive model with order p.
To put it another way, the AR (p) model is
∑ (1)
The backshift operator B can be used to write this in the
same way:
∑ (2)
So that, ( ) (3)
Thus, an AR model can be thought of as the output of a
white noise-inputted all-pole infinite impulse response filter.
E. Data Acquisition
To ensure accuracy and quality, it compares the target
signal to background noise. Once this digital brain data has
been decoded into the intended action, it can be classified, and
the necessary attributes calculated and selected from the neural
data.
Fig. 4. The ECoG Recordings from Dataset I of the BCI Competition III.

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Fig. 5. Neural based Data Acquisition for Machine Learning.
In addition, optional feedback is sent to BCI users (which
generates brain signals) and the cycle is again repeated. An
anti-clockwise motion (as seen in Fig. 5) stimulates the
simulation of data to begin.
The input from the stimulation data decoder is processed
using various machine learning (ML) methodologies (neural
networks). It’s important to note that the generated input
provides high-level information about the multimedia devices
that will be triggered, their frequencies, and the timing of their
activation.
BCI technology-related parameters were subsequently
specified using thresholding patterns that received neural input
in the intended action and generalized form have been shown
in the Fig. 6. To display multimedia-based actions, the process
moves from brain data simulation to user interface and
interactivity.
Fig. 6. Neural Networks based Threshold or Firing Pattern.
IV. IMPLEMENTATION AND RESULTS
The computer-based assessment test of visuomotor skills
was divided into two blocks, one standard, and one non-
standard for participants. This necessitated the use of
cognitive-motor integration in order to treat the condition
(CMI). Participants had to navigate a cursor as quickly and
accurately as possible from the tablet’s center to one of four
peripheral goals using their dominant index finger (up, down,
left, or right). Users clicked on an 8 millimeter wide solid
green circle in the middle of the screen to begin their
experiment. Upon seeing an open green 10 mm diameter
peripheral target after a 200ms center hold period, the
participant was given the "Go" signal and was free to begin
moving. A green cursor was positioned over an open green
target by sliding the touch screen with one’s fingers.
Upon reaching the peripheral goal and remaining there for
500 milliseconds, the experiment was over. Next, the central
target was presented after a 200-ms intertrial interval. At 37.5
millimeters from the tablet’s central starting point, the tablet’s
peripheral targets were (center-to-center distance). Overall,
there were 20 trials per task, with five trials for each goal.
Participants used their fingers to move the cursor under their
fingers to engage directly with the target in the conventional
condition. As a result of the non-standard CMI scenario, a
white line separated the display into two halves. Participants
had to pay attention to the tablet’s upper half for the targets and
cursor. Participants had to glide their fingers along the tablet
screen’s bottom to move the cursor.
Fig. 7. Proposed Methodology for the Classification of Cognitive Load
using EEG Data.
The collected data will be used to improve classification
accuracy on IVa datasets in our experimental assessment.
Fig. 7’s SCU architecture is modified to categorize these
images using an SSVEP-based model. As a data reference
channel, we employed the frontal cortex (Fz) instead of the
nine sensor channels originally used to simplify SSVEP
classification training. They demonstrated that this model
outperformed both standard approaches and time-series
specialized models like Recurrent Neural Networks when it
came to recognize SSVEP EEG signals (RNN). All tests utilize
one-dimensional convolutional layers as depicted in Fig. 8,

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batch normalization, and maximum pooling. After applying a
bandpass filter between 9 and 60 Hz, as well as another filter at
50 Hz, the EEG channels are initially pre-processed before the
data is eventually normalized between 0 and 1.
Fig. 8. Layer Representation of Neural Networks.
For the experiments, dataset IV has been used from the BCI
competition. The accuracy is the actual fraction of the samples
that are correctly classified from the samples obtained in (4):
(4)
The Precision is the percentage of relevant samples,
evaluated as in (5):
(5)
The Recall measures the positives identified correctly as in
(6):
(6)
Where, TP-True Positive, TN-True Negative, FP-False
Positive and FN-False Negative.
Based on the findings provided in Table I, it appears that
the Random Forest method outperformed the Neural Network
and Gradient Boosting classifier. An individual’s maximum
level of accuracy was 70.2 percent, which was a reasonable
improvement above the random guess accuracy of 33 percent.
Because we used to leave one out cross-validation in this
situation, the inter-subject accuracy of 56.8% was also quite
encouraging. It was shown that training a model on an
individual’s data yielded better results for intra-class
classification than utilizing data from various persons, which
could raise concerns about privacy.
The use of the supervised forward feature selection method
resulted in considerable improvements to our results. Reducing
60 feature set from 60 to 10 was done by removing
unnecessary and distracting features. We were able to enhance
our performance on the validation set as a result of our
methodology’s ability to reduce overfitting. This method had a
10 percent improvement in average accuracy and the maximum
accuracy we could achieve with wearable devices was 80.6
percent, well above the accuracy of any previous method for
EEG classification based on RGB colors. Fig. 9 and 10
represent the graphs for accuracies at 200ms time window
using all and 10 features respectively. The classifier was able
to correctly classify 72% of the subjects on average. The
accuracy went up to 95% of the time Intra-subject
classification was likewise superior to inter-subject
classification in this situation. The accuracy of inter-subject
classification was raised by 1.3 percent. In this situation, the
Random Forest approach outperformed both the neural
network and the gradient boosting techniques. Table II shows
the results.
TABLE I. ACCURACY USING ALL FEATURES AT 200MS
Metrix SVM KNN
Gradie
nt
Boost
Rando
m
Forest
Logistic
Regressio
n
Neural
Networ
k
Inter-
Subject
Accuracy
0.456 0.446 0.572 0.668 0.508 0.590
Average-
Subject
Accuracy
0.574 0.514 0.708 0.725 0.606 0.623
Best-
Subject
Accuracy
0.678 0.613 0.769 0.802 0.700 0.800
Fig. 9. Graph for accuracy using all features at 200ms
TABLE II. ACCURACY WITH 10 FEATURES AT 200MS TIME WINDOW
Metrix SVM KNN
Random
Forest
Gradient
Boost
Logistic
Regression
Neural
Network
Inter-
Subject
Accuracy
0.466 0.477 0.681 0.511 0.488 0.575
Average-
Subject
Accuracy
0.587 0.592 0.820 0.697 0.592 0.613
Best-
Subject
Accuracy
0.678 0.613 0.906 0.820 0.690 0.866
Fig. 10. Graph for Accuracy with 10 Features at 200ms Time Window.

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Two types of classification were carried out: intra-subject
and inter-subject. We used to leave out one subject cross-
validation for the second classification, where we trained the
model using seven subjects’ data and validated it using a single
subject’s data, then repeated it for all subjects. The average
cross validation accuracy, ROC-AUC, and MCC of our model
were used to assess its performance. The original and
condensed datasets are also included in our results. Fig. 11 and
12 show the average accuracy, the average AUC score, and the
average MCC score for both intra-subject and inter-subject
categorization. Our greatest results were achieved in a 200ms
time frame. A total of 200ms of experimentation was placed
throughout this time. All peak frequencies and its harmonics
have been shown in the Fig. 13 for fast Fourier transform
(FFT) method that has an important role in processing for the
discrete signals.
Fig. 11. Average Accuracy, ROC-AUC and MCC Scores for Inter-Subject
Categorization.
Fig. 12. Average Accuracy, ROC-AUC and MCC Scores for Intra-subject
Categorization.
Fig. 13. Peak Frequency and its Harmonics for FFT throughout all Nine
Disciplines.
V. CONCLUSION
To help people with disabilities, brain-computer interfaces
(BCI) have been developed. However, new uses for this
technology have emerged, such as the expansion of human
potential. This work presented novel system architecture for
the BCI, and this article has shown to be easily configured and
modular to access the different EEG signals. Locked-in
individuals have no other way to communicate or exert control
over their surroundings but through the use of a Brain-
Computer Interface (BCI). When conducting a BCI,
electrocorticography (ECoG) gives superior resolution to non-
invasive methods. EEG signals, on the other hand, have a
limited range of signal amplitude and bandwidth. ECoG can
identify gamma activity more quickly than EEG because these
high frequencies are more closely linked to specific sections of
the motor, linguistic, and intellectual functions. Classification
is done using a variety of machine learning algorithms. So,
SVMs can be used to make generalizations that are good out of
sample, if the parameters are specified correctly. This suggests
that SVMs can be robust, even if the training sample has some
bias, by selecting an acceptable generalization grade.
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