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www.setjournal.com https://doi.org/10.54327/set2023/v3.n2.84
Corresponding author: Anwar Almofleh (anwar.almofleh@khuh.org.bh)
Received: 27 April 2023; Accepted: 6 June 2023; Published: 19 June 2023
© 2023 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License
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Brain Computer Interfaces: The Future of Communication Between the
Brain and the External World
Mohamed Alseddiqi1, Anwar AL-Mofleh1, Osama Najam1, Leena Albalooshi1, Abdulla Alheddi2,
Ahmed Alshaimi3
1Clinical Engineering Directorate, King Hamad University Hospital, Building: 2435,
Road 2835, Block 228 P.O. Box 24343, Busaiteen, Kingdom of Bahrain
2Salmaniya Medical Complex, Rd No 2904 Salmaniya, Kingdom of Bahrain
3Al Amal hospital W.L. L: 1751, Road: 5445, Block:754, Hamad Town: Buri
P.O. Box:38634, Kingdom of Bahrain
Abstract
This paper provides a comprehensive review of the current state of research on Brain Computer Interfaces (BCIs)
and their potential applications. The objective of this study was to gather information from various sources, including
journal articles, conference papers, and books, to analyze the advancements and limitations of BCIs. A systematic
literature review was conducted using databases such as PubMed, IEEE Xplore, and Google Scholar, with specific
keywords related to BCIs and their applications. The selected studies were thoroughly analyzed to identify common
themes, methodologies, and key findings. The main contributions of this review include an overview of different types
of BCIs, their applications in fields such as medicine, entertainment, and education, and the challenges and limitations
faced by BCI technology. The findings highlight the potential of BCIs in restoring motor function, improving the quality
of life for individuals with various conditions, and enhancing human-technology interaction. Furthermore, the review
identifies future research directions, including signal processing improvements, exploring hybrid and multimodal
approaches, conducting long-term real-world studies, addressing ethical considerations, and prioritizing user-centred
design. This comprehensive analysis of existing literature provides valuable insights for researchers and practitioners in
the field of BCIs and sets the stage for future advancements in this rapidly evolving domain.
Keywords: Brain Computer Interfaces (BCIs), electroencephalography (EEG), classification, feature extraction,
signal acquisition.
1. Introduction
Brain Computer Interfaces (BCIs) represent a new and
exciting technology that allows for direct communication
between the brain and an external device or system [1].
The potential applications of BCIs are vast and varied,
spanning across many areas of life, from medicine to
entertainment [2,3]. In this review research paper, we will
provide an overview of the current state of BCI research,
including the various types of BCIs, their applications,
and their limitations. Additionally, we will discuss
potential future directions for BCI research, highlighting
areas for further study and development. BCIs hold great
promise for revolutionizing many aspects of human life.
In medicine, for example, BCIs have already been used to
restore lost motor function to paralyzed patients [4-6]. By
detecting and interpreting the electrical signals generated
by the brain, BCIs can enable individuals with paralysis
to control robotic limbs or other external devices with
their thoughts [7,8]. BCIs also hold potential for
improving the lives of individuals with a range of other
conditions, including neurological disorders, hearing and
visual impairments, and chronic pain [9-11]. Beyond
medicine, BCIs have the potential to enhance the way we
interact with technology and the world around us. In
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entertainment, for instance, BCIs can be used to create
immersive experiences that respond to the user's thoughts
and emotions. They can also be used to create new forms
of interactive media that allow users to control games,
movies, and other content using their thoughts. In
education, BCIs can be used to provide personalized
learning experiences that adapt to each individual
student's needs and preferences [12-15].
Despite their potential, however, BCIs face a number
of challenges and limitations. One of the biggest
challenges is the development of reliable and accurate
methods for detecting and interpreting brain signals.
Current BCI technology is often limited by the signal-to-
noise ratio of the brain signals, which can be affected by
a range of factors, such as movement, fatigue, and
distractions. Additionally, there are concerns around the
privacy and security of the data collected by BCIs, as well
as the potential for misuse of this technology [16-19].
Despite these challenges, researchers and developers
are actively working to improve BCI technology and
expand its applications. There are a range of different
types of BCIs currently being developed, including
invasive, partially invasive, and non-invasive BCIs.
Invasive BCIs involve implanting electrodes directly into
the brain tissue, while partially invasive BCIs use
electrodes that are placed on or beneath the skull but not
directly in the brain tissue. Non-invasive BCIs, on the
other hand, use sensors placed on the scalp or other parts
of the body to detect brain signals. Each type of BCI has
its own advantages and limitations, and researchers are
exploring ways to improve their effectiveness and
reliability [20].
Looking to the future, there are many exciting
developments on the horizon for BCI research. One area
of focus is the development of hybrid BCIs, which
combine different types of BCI technology to create more
effective and versatile systems [21,22]. Another area of
focus is the development of closed-loop BCIs, which use
real-time feedback to adjust the stimulation or
intervention being delivered by the BCI [23,24]. This
could have implications for the treatment of conditions
such as epilepsy, where timely intervention can prevent
seizures. Additionally, researchers are exploring the
potential for BCIs to enhance cognitive and emotional
states, such as improving attention or reducing anxiety
[25-27].
The objectives of this paper are to provide an overview
of the current state of BCIs research, discuss the various
types of BCIs and their applications, highlight the
challenges and limitations faced by BCIs, and present
potential future directions for BCI research. The paper
aims to contribute to the existing knowledge by providing
insights into the advancements, opportunities, and areas
for further exploration in the field of BCIs.
In this paper, a comprehensive review of the literature
on BCIs were presented. The paper begins with an
introduction to BCIs, their potential applications, and the
challenges they face. This is followed by a detailed
methodology section that explains the process of literature
review and data collection. The results section provides a
summary of key studies in the field, focusing on the use
of BCIs for controlling robots. The discussion section
analyzes the findings, highlights the contributions of the
reviewed studies, and identifies their limitations. Finally,
the conclusion section summarizes the main insights
gained from the literature review and outlines future
research directions.
2. Main Components of A BCI System
Brain-Computer Interface (BCI) is a system that
allows direct communication between the human brain
and an external device, such as a computer. The BCI
system works by detecting, processing, and interpreting
brain signals in real-time, enabling users to control
external devices using their thoughts, the main component
of the system is described in Figure 1.
Figure 1. The main components of a BCI system.
Signal Acquisition
Signal acquisition is the process of recording the
electrical signals generated by the brain using various
methods such as electroencephalography (EEG)
magnetoencephalography (MEG), or functional magnetic
resonance imaging (fMRI) as showing in Figure 2. In a
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BCI system, EEG is the most commonly used method for
signal acquisition. EEG electrodes are placed on the scalp
to record the electrical activity of the brain. These signals
are then amplified, filtered, and digitized for further
processing [28].
Figure 2. Signal Acquisition.
Signal Processing
Signal processing is the next step in BCI applications
and involves several steps, including pre-processing,
filtering, and data normalization. Pre-processing includes
artefact removal, noise reduction, and signal denoising to
ensure that the acquired signals are reliable and accurate.
Filtering is used to remove unwanted noise from the signal
and improve its quality. Data normalization is used to
scale the features to the same range, which is essential for
accurate classification [29,30].
Feature Extraction
Feature extraction is a process that aims to identify and
extract relevant features from the signal that can be used
for further processing and classification. The extracted
features are usually based on frequency, time, or space
domain analysis, and they are used to identify patterns in
the data that are relevant to the task at hand. For example,
in a motor imagery BCI task, the features extracted may
include the power spectral density in the alpha or beta
frequency bands, which are known to be associated with
motor activity [30,31].
Classification:
The classification component is responsible for
categorizing the brain patterns extracted by the feature
extractor. It transforms the independent variable into the
dependent variable by using various classification
algorithms, which can either be linear, such as Linear
Discriminant Analysis (LDA) and Support Vector
Machine (SVM), or non-linear, such as neural networks
[30].
Control Interface
The control interface is the component of the BCI
system that translates the user's intention into a control
signal that can be used to operate an external device. This
interface can be a computer screen, a robotic arm, or any
other device that can be controlled by a computer. The
interface is designed to be intuitive and easy to use,
allowing the user to control the device with their thoughts
in real-time [30].
Feedback
Feedback is a critical component of a BCI system as it
provides the user with information about the system's
performance and their own brain activity. Feedback can
be visual, auditory, or tactile, and it is used to inform the
user whether their intended action has been correctly
identified and executed. Feedback is essential for BCI
training and can help users learn to control their brain
signals more effectively over time [32-34].
3. Electroencephalography (EEG)
EEG is a non-invasive technique used to measure the
electrical activity of the brain. It involves placing
electrodes on the scalp to record the electrical signals
produced by the neurons in the brain. The 10-20 system is
a widely accepted technique for defining and utilizing the
placement of scalp electrodes during an EEG assessment
as presented in Figure 3.
EEG signals are typically used in BCI applications to
detect changes in brain activity and translate them into
commands that can be used to control devices or
communicate with the environment. EEG is a commonly
used technique in neuroscience research and clinical
applications, as it provides insights into the brain's
functional and pathological states [35-36]. The
classification of EEG signals is based on their frequency,
ranging from 0 to 100 Hz. EEG signals can also be
categorized based on their amplitude, and be recognized
based on their shape as illustrated in Table 1 [37-38].
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Figure 3. Types of BCIs.
Table 1. The classification of EEG signals.
Frequency
Characteristic
Image
Delta
(0.1-4) Hz
Deepest level
of relaxation
deep sleep
Theta
(4-8) Hz
Rapid eye
movement
sleep deep and
raw emotions
cognitive
processing
Alpha
(8-13) Hz
Relaxation
drowsy state
Beta
(13-30) Hz
Conscious
state
Gamma
(30-100) Hz
Two different
senses at the
same time
Table 2 presents a summary of recent EEG devices
commonly used in BCI applications. These devices vary
in terms of the number of channels, electrode types,
sampling rates, and connectivity options. The devices
listed include both research-grade and consumer-grade
EEG systems. Research-grade systems, such as those
manufactured by Brain Products, g.tec, and BioSemi,
offer higher channel counts and sampling rates, as well as
advanced signal processing and analysis tools. Consumer-
grade systems, such as those offered by Emotiv and
NeuroSky, are designed for personal use and offer lower
channel counts and sampling rates. The choice of EEG
device depends on the specific BCI application, the user's
requirements, and the available budget [39-43].
3.1 Event-Related Potentials
Event-Related Potentials (ERPs) are changes in the
brain's electrical activity that are time-locked to specific
events or stimuli. ERPs are a type of EEG measurement
that is widely used in neuroscience research and clinical
settings to study cognitive processes such as attention,
perception, memory, and decision-making. One
promising application of ERPs is in BCIs, which are
systems that allow individuals to communicate or control
external devices using their brain signals. EEG-based
BCIs have become increasingly popular due to their non-
invasive nature and high temporal resolution. ERP
components have been used as control signals in EEG-
based BCIs, as they can be reliably elicited by specific
events or stimuli, and are easy to interpret. By using ERP-
based control signals, BCIs can provide more natural and
intuitive control for individuals with motor disabilities,
allowing them to communicate and interact with their
environment in ways that were previously impossible.
ERP graph is a graphical representation of the brain's
electrical activity in response to a specific stimulus or
event as demonstrated in Figure 4.
It is usually plotted as voltage (y-axis) over time (x-
axis). The graph shows a series of peaks and troughs in
voltage, known as components, that occur at specific time
points after the onset of the stimulus. The ERP waveform
typically consists of several components that reflect
different cognitive processes, such as perception,
attention, and memory. Each component has a specific
latency (the time between the stimulus and the peak of the
component) and amplitude (the height of the peak).
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Table 1. Summary of recent EEG devices commonly used in BCI applications
EEG Manufacturer Model Sensor Channel Sampling Rate Communication
Emotiv INSIGHT 2.0 Dry 5 128 [Hz] Wireless
EPOC+ Wet 14 - Wireless
EPOC FLEX Wet 32 - Wireless
MN8 Dry 2 - Wireless
Interaxon Muse 2 Dry 4 256 [Hz] Wireless
Muse S (Gen 2) Dry 4 256 [Hz] Wireless
NeuroSky MindWave Mobile 2 Dry 1 512 [Hz] Wireless
Open BCI ULTRACORTEX MARK IV Dry 8, 16 250, 125 [Hz] Wireless
EEG Electrode Cap Kit Wet 21 - Wireless
Advanced Brain Monitoring B-Alert X10 Wet 9 256 [Hz] Wireless
B-Alert X24 Wet 20 256 [Hz] Wireless
Neuroelectrics Enobio 20 Wet 20 500 [Hz] Wireless
Brain Products LiveAmp Wet 8, 16, 32, 64 250, 500, 1.000 [Hz] Wireless
G.Tec g.Nautilus Dry / Wet 8, 16,32, 64 250 / 500 [Hz] Wireless
BioSemi ActiveTwo AD-box - 16, 32, 64 2, 4, 8, 16 [kHz] Wired
Neurosity Crown Dry 8 256 [Hz] Wireless
Figure 3. ERP Graph.
The earliest component of the ERP is the P1, which
reflects early sensory processing. This is followed by the
N1, which reflects attention allocation and perceptual
processing. Later components such as the P300 or N400
may reflect cognitive processes such as decision-making,
memory, or semantic processing. The ERP graph is
typically analyzed by comparing the waveform of
different conditions or groups to determine if there are
differences in the timing or amplitude of specific
components. These differences can provide insights into
the underlying cognitive processes and neural
mechanisms involved in the processing of the stimulus or
event [20].
4. Types of BCIs
There are different types of BCIs that vary in their
invasiveness, from fully invasive to non-invasive as
shown in Figure 5. Each type of BCI has its own
advantages and limitations, making them suitable for
different applications. Invasive BCIs are implanted
directly into the brain tissue, allowing for the highest level
of signal resolution and accuracy. These BCIs involve
electrodes that are implanted in the cortex of the brain and
are used to record neural activity. Invasive BCIs are
primarily used in research and medical settings to help
individuals with paralysis or neurological disorders to
control prosthetic devices or to restore lost function. The
main advantage of invasive BCIs is their high level of
accuracy in signal detection, which allows for precise
control of devices or systems [3,20,44].
Figure 4. Types of BCIs [45].
Partially invasive BCIs are those that are implanted
under the scalp or on the surface of the brain. These BCIs
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involve electrodes that are placed either directly on the
surface of the brain or beneath the skull, but not implanted
into the brain tissue. Partially invasive BCIs are less
invasive than fully invasive BCIs, but they still provide a
high level of signal resolution. Partially invasive BCIs are
used in research and medical settings to help individuals
with neurological disorders or paralysis to control
prosthetic devices or to restore lost function. The main
advantage of partially invasive BCIs is their ability to
provide a high level of signal detection accuracy while
being less invasive than fully invasive BCIs [3,20,44].
Non-invasive BCIs are those that do not require any
surgical intervention and are applied to the scalp or the
surface of the head. These BCIs involve the use of
sensors, such as EEG or MEG, to record neural activity.
Non-invasive BCIs are the least invasive type of BCI and
are commonly used in research and medical settings to
help individuals with neurological disorders to control
prosthetic devices or to restore lost function. The main
advantage of non-invasive BCIs is their ease of use and
non-invasiveness, which makes them suitable for a wide
range of applications [3,20,44].
5. Applications of BCIs
Brain Computer Interfaces (BCIs) have a wide range
of applications, from communication to entertainment,
and from medicine to robotics. In this article, we will
discuss the various applications of BCIs and provide
examples of how they are being used in different areas.
Communication: BCIs can be used to assist
individuals with disabilities to communicate, such as
those with locked-in syndrome, cerebral palsy, or
amyotrophic lateral sclerosis (ALS) [45-47]. By
interpreting the user's brain signals, BCIs can help
individuals with limited or no physical movement to
communicate through a computer or other devices.
One example is the Brain Gate system, which has
been used to allow people with paralysis to type on a
computer, send emails, and even control a robotic
arm [48].
Assistive technology: BCIs can be used to control
prosthetic limbs, allowing individuals with
amputations or spinal cord injuries to regain some of
their lost mobility [49]. The user's brain signals are
translated into movements of the prosthetic,
allowing them to perform daily activities such as
grasping objects or walking [50].
Gaming: BCIs can also be used for gaming purposes.
One example is the Mindflex game, which uses EEG
technology to detect the user's brain signals and
move a ball through an obstacle course [51]. Another
example is the Puzzlebox Orbit, a BCI-controlled
helicopter that users can fly using their brain signals
[52].
Medical applications: BCIs have significant
potential for medical applications, such as diagnosis,
treatment, and rehabilitation. For example, BCIs can
be used to monitor brain activity during surgeries,
diagnose neurological disorders, or treat conditions
such as chronic pain or epilepsy [9-11, 25-27].
Robotics: BCI can be used to control robotic
prosthetic limbs or exoskeletons, allowing
individuals with disabilities to regain mobility and
independence. Robotics can also be used to assist in
the training and rehabilitation of individuals
providing a more interactive and engaging
experience, BCIs can also be used to control
humanoid robots, which can assist in caregiving or
other tasks [7-11,30].
Entertainment: BCIs can also be used for
entertainment purposes, such as virtual reality or
immersive experiences. For example, a BCI-
controlled video game can provide a more immersive
and interactive experience for players [53,54].
6. Methodology
For this research, a comprehensive literature review
was conducted to gather information on the current state
of the field of BCIs and their potential as a means of
communication between the brain and the external world.
The literature review identified several key challenges in
the field of BCIs. The parameters for comparisons were
selected based on their relevance to the research objective
and study focus, ensuring meaningful insights and
comprehensive evaluation. These challenges include the
need for improved signal detection and interpretation
methods, addressing the signal-to-noise ratio, and
enhancing privacy and security of collected data. These
areas represent important avenues for future research in
the field of BCIs. The review methodology involved a
systematic sampling strategy to select relevant studies and
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sources from prominent databases such as PubMed, IEEE
Xplore, and Google Scholar. Specific keywords,
including "brain computer interfaces," "brain machine
interfaces," "EEG controlling robots," "EEG controlling
machine," "EEG BCI," and "BCI algorithms," were used
to identify studies published between 2010 and 2022 that
focused on the application of classification algorithms in
controlling robots through BCIs. The literature review
included both primary studies and secondary sources,
including studies that were cited by or cited the identified
studies, to ensure a comprehensive coverage of the topic.
Data collection involved reading and thorough analysis of
the selected studies and sources. Detailed notes were
taken on the research questions, methods, findings, and
conclusions of each study, allowing for a comprehensive
understanding of the research landscape in this area.
Common themes and patterns in the literature were
identified, and any gaps or inconsistencies were carefully
noted.
The review encompassed a total of 70 studies that met
the inclusion criteria, which consisted of studies published
within the specified timeframe, written in the English
language, and focused on BCIs. Studies that were
published before 2010 or after 2022, as well as those that
were deemed irrelevant or non-BCI related, were
excluded from the review. The search strategy and study
selection process ensured a rigorous and systematic
approach to capturing the most relevant and up-to-date
information on the topic. By employing this robust
methodology, the review provides a comprehensive
overview of the current state of the field, with a specific
focus on the application of classification algorithms in
BCI-based control of robots. The findings of this review
contribute to the existing body of knowledge and offer
valuable insights for researchers and practitioners in the
field of BCIs.
7. Results
Table 3 provides a summary of studies exploring the
use of BCIs for controlling robots through motor imagery
or hybrid BCI approaches. The studies utilized various
classifier algorithms, such as SVM, LDA, and artificial
neural networks (ANN), to control different types of
robots, including robotic arms, mobile robots,
quadcopters, and exoskeletons. The output commands
varied depending on the control object and included
movements such as turning left and right, going forward
and backward, lifting and dropping, and flexion and
extension of the hand fingers. These studies demonstrate
the potential of BCIs in enabling individuals with motor
impairments to control robots and devices through their
thoughts, opening up possibilities for assistive technology
and neurorehabilitation [55-68].
Specifically, one study [55] utilized an interval type-2
fuzzy logic based multiclass ANFIS algorithm to control
a robotic manipulator with a 3-fingered hand, with output
commands including relaxation, left and right movement,
and forward and backward movement. Another study [56]
utilized an SVM algorithm to control a mobile robot, with
output commands including turning left and right, and
going forward and backward. A study [57] constructed
neural nets in a BCI for robot arm steering, with output
commands including left and right movement, and start
and stop. Another study by [58] used an artificial neural
network to control a quadcopter, with output commands
including left and right movement. In a study [59], a
motor imagery-based BCI coupled to a robotic hand
orthosis was used for neurorehabilitation of stroke
patients, with output commands including flexion and
extension of the hand fingers. Another study [60] utilized
an RBF-SVM algorithm to control a robotic arm, with
output commands including right and left movement of
the base and upward movement of the elbow. A study [61]
used an LDA algorithm to control a mobile robot, with
output commands including turning left and right, and
going forward and stopping.
A study [62] used an SVM algorithm to control a dual-
arm robot, with output commands including lifting and
dropping. Another study [63] utilized an LDA algorithm
to control a robotic arm, with output commands including
left and right movement. In a [64] study an SVM
algorithm was used to control a robotic arm, with output
commands including turning the link clockwise or
counterclockwise and moving the link forward.
In a study [65], an LDA algorithm was used to control
a quadcopter, with output commands including left and
right movement, and forward and backward movement.
Another study [66] utilized LDA and SVM algorithms to
control an exoskeleton robot, with output commands
including moving left and right. A study by [67] used an
SVM algorithm to control a quadcopter, with output
commands including activating, turning left and right.
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Table 3. A summary of different studies that have explored the use of BCIs for controlling robots.
Paper Title Year of Publication Classifier Algorithm
Control
Objects
Output Commands Reference
Interval type
-
2 fuzzy
logic based multiclass
ANFIS algorithm for
real-time EEG based
movement control of a
robot arm
2015 OVO-IT2FLF-
ANFIS
axis robotic
manipulator
with a3
fingers hand
Relax, left and right,
forward and
backward [55]
Autonomuos
robot
control based on EEG
and cross
-
correlation
2016 SVM Mobile robot
Turning left and
right, going forward
and backward
[56]
Construction of neural
nets in brain-computer
interface for robot arm
steering
2016 Artificial neural
networks Robotic arm Left and right, start
and stop [57]
A performance study of
14-channel and 5-
channel EEG systems
for real-time control of
unmanned aerial
vehicles (UAVs)
2018 Artificial neural
networks Quadcopter Left and right [58]
Motor imagery
-
based
brain-computer interface
coupled to a robotic
hand orthosis aimed for
neurorehabilitation of
stroke patients
2018 LDA Robotic hand
orthosis
Flexion and
extension of the hand
fingers [59]
EEG based brain
computer interface for
controlling a robot arm
movement through
thought
2018 RBF-SVM Robotic arm The base moves right
and left, the elbow
points up [60]
Robot navigation using
a brain computer
interface based on motor
imagery
2019 LDA Mobile robot Turning left and
right, going forward
and stopping [61]
Motor
-
imagery
-
based
teleoperation of a dual-
arm robot performing
manipulation tasks
2019 SVM Dual-arm
robot Lift and drop [62]
Shared control of a
robotic arm using non-
invasive brain–computer
interface and computer
vision guidance
2019 LDA Robotic arm Left fornt and right
front movement [63]
Motor imagery and error
related potential induced
position control of a
robotic arm
2017 SVM Robotic arm
Turn the link
clockwise or
counterclockwise,
move the link
forward
[64]
Quadcopter
control
system using a hybrid
BCI based on off-line
optimization and
enhanced human-
machine interaction
2019 LDA Quadcopter Left and right,
forward and
backward [65]
Hybrid MI
-
SSSEP
Paradigm for classifying
left and right movement
toward BCI for
exoskelet
on control
2019 LDA and SVM Exoskeleton
robot Moving left and right [66]
Application of hybrid
brain-computer interface
with augmented reality
on quadcopter control
2020 SVM Quadcopter Activating, turning
left and right [67]
A hybrid brain-
computer interface for
closed-loop position
control of a robot arm
2020 LSVM, RBF-SVM Robot arm
Link selection,
motion initiation,
automatic reversal,
oscillation continues,
object position
[68]
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Finally, a study [68] utilized LSVM and RBF-SVM
algorithms to control a robot arm, with output commands
including link selection, motion initiation, automatic
reversal, oscillation continues, and object position.
Overall, these studies demonstrate the potential of BCIs
in enabling individuals with motor impairments to control
robots and devices through their thoughts, providing new
avenues for assistive technology and neurorehabilitation.
8. Discussion
The results of this literature review demonstrate the
potential of BCIs in enabling individuals with motor
impairments to control robots and devices through their
thoughts. The findings suggest that the use of BCIs in
controlling robots can improve the quality of life of
individuals with motor impairments and provide new
possibilities for assistive technology and
neurorehabilitation. BCI have the potential to
revolutionize the way we interact with the world around
us, but there are several challenges and limitations that
need to be addressed in order to fully realize their
potential. In this regard, this section will discuss some of
the major challenges and limitations associated with the
use of BCIs. One of the biggest challenges associated with
BCIs is the need for highly skilled professionals to operate
them. The technical complexity of BCIs requires trained
professionals with a deep understanding of neuroscience
and computer engineering. Furthermore, BCI technology
is constantly evolving, which means that professionals
working with BCIs must stay up-to-date with the latest
developments in the field [69].
Another challenge associated with BCIs is the limited
availability of devices. Currently, the cost of BCI devices
is relatively high, and they are not widely available to the
general public. This limits the accessibility of BCIs to
those who could potentially benefit from them, such as
individuals with disabilities or neurological conditions. In
addition, achieving accurate and reliable signal detection
is a major challenge in BCI research. The signals that are
measured by BCIs are very small and can be easily
disrupted by other electrical activity in the brain or by
external factors such as movement or environmental
noise. This can lead to inaccuracies in the data collected
by BCIs, which can impact the effectiveness of the
technology [70].
Moreover, there are also challenges associated with the
physical design and comfort of BCIs. Some BCIs are
invasive, requiring surgery to implant electrodes directly
into the brain, which can carry risks and complications.
Partially invasive BCIs involve implanting electrodes on
the surface of the brain, while non-invasive BCIs rely on
external sensors to detect brain activity. However, non-
invasive BCIs can be uncomfortable to wear for extended
periods of time and can be affected by the user's hair, skin,
and other physical characteristics [71, 72].
9. Future Directions
The field of BCI is rapidly evolving, with new
developments and advancements being made on a regular
basis. BCI hold tremendous promise in revolutionizing
how humans interact with technology. In the future,
research in BCIs should focus on a number of key areas.
One important area is improving signal detection and
interpretation. This involves developing more reliable and
accurate methods for detecting and interpreting brain
signals, improving the signal-to-noise ratio, addressing
movement and distractions, and increasing privacy and
security of collected data.
Another important area of focus for future research in
BCIs is advancing the technology itself. This includes
exploring and developing different types of BCIs, such as
invasive, partially invasive, and non-invasive BCIs,
improving their effectiveness and reliability, and
exploring ways to combine different types of BCI
technology to create more effective and versatile systems.
Enhancing cognitive and emotional states through
BCIs is another area of potential research. BCIs could be
used to improve attention or reduce anxiety, for example,
by using real-time feedback to adjust stimulation or
intervention being delivered by the BCI. Closed-loop
BCIs, which use real-time feedback to adjust the
stimulation or intervention being delivered by the BCI,
could have implications for the treatment of conditions
such as epilepsy, where timely intervention can prevent
seizures.
Another important area of focus for future research in
BCIs is exploring new and innovative applications in
various fields, such as education, entertainment, and
sports. For example, personalized learning experiences
that adapt to each individual student's needs and
Science, Engineering and Technology Vol. 3, No. 2, Online First
Online First
preferences could be developed, and immersive
experiences that respond to the user's thoughts and
emotions could be created.
Finally, researchers must address ethical concerns
related to the use of BCIs, including issues around privacy
and security, as well as the potential for misuse of this
technology. Overall, future research in BCIs has the
potential to bring significant advancements to the field
and improve the quality of human life. By improving the
accuracy and reliability of BCIs, enhancing cognitive and
emotional states, developing new applications, and
addressing ethical concerns, researchers can make great
strides in this exciting and rapidly developing field
10. Conclusion
In conclusion, BCIs represent a revolutionary
technology that holds great promise for transforming
various aspects of human life, from medicine to
entertainment, education, and industry. BCIs have the
potential to restore lost motor function to paralyzed
patients, improve the lives of individuals with a range of
neurological disorders, and create immersive experiences
that respond to the user's thoughts and emotions.
However, BCIs face several challenges, such as
developing reliable and accurate methods for detecting
and interpreting brain signals, privacy and security
concerns, and potential misuse of the technology. To
overcome these challenges, researchers and developers
are actively working to improve BCI technology and
expand its applications. The future of BCI research looks
promising, with hybrid BCIs, closed-loop BCIs, and BCIs
that enhance cognitive and emotional states being
developed. While there is still much to be explored and
developed, the potential impact of BCIs on human life is
significant, and we can expect to see more advancements
and applications of this technology in the future.
Nomenclature
BCIs
Brain Computer Interfaces
EEG
electroencephalography
MEG
magnetoencephalography
fMRI
functional magnetic resonance imaging
LDA
Linear Discriminant Analysis
SVM
Support Vector Machine
ANN
Artificial neural networks
Declarations
Availability of data and material: The data and
materials used in this study are available upon request.
Authors' contributions
Mohamed Alseddiqi and Anwar AL-Mofleh: provided
the conception and design of the study, acquisition of data,
analysis and interpretation of data, drafting the article,
revised it critically for important intellectual content, and
final approval of the version to be submitted; Osama
Najam: supplied the acquisition of data, drafting of paper;
Leena Albalooshi: supplied the design of study, analysis
and interpretation; supplied the acquisition of data;
Abdulla Alheddi: was responsible for the article critically
for important intellectual content; and Ahmed Alshaimi:
provided the revised the article critically for important
intellectual content and gave final approval of the version
to be submitted.
Acknowledgements
I am grateful to all of those with whom I have had the
pleasure to work during this and other related research.
Each of the members of the paper Committee has
provided extensive personal and professional guidance.
References
[1] R. A. Ramadan and A. V. Vasilakos, "Brain computer
interface: control signals review," Neurocomputing, vol.
223, pp. 26-44, 2017.
[2] S. N. Abdulkader, A. Atia, and M. S. M. Mostafa, "Brain
computer interfacing: Applications and challenges,"
Egyptian Informatics Journal, vol. 16, no. 2, pp. 213-230,
2015.
[3] H. S. Anupama, N. K. Cauvery, and G. M. Lingaraju, "Brain
computer interface and its types-a study," International
Journal of Advances in Engineering & Technology, vol. 3,
no. 2, pp.739-745, 2012.
[4] N. Birbaumer, "Breaking the silence: brain-computer
interfaces (BCI) for communication and motor control,"
Psychophysiology, vol. 43, no. 6, pp. 517-532, 2006.
[5] N. Robinson, R. Mane, T. Chouhan, and C. Guan,
"Emerging trends in BCI-robotics for motor control and
Science, Engineering and Technology Vol. 3, No. 2, Online First
Online First
rehabilitation," Current Opinion in Biomedical
Engineering, vol. 20, pp. 100354, 2021.
[6] D. Mattia, F. Pichiorri, M. Molinari, and R. Rupp, "Brain
computer interface for hand motor function restoration and
rehabilitation," in Towards Practical Brain-Computer
Interfaces: Bridging the Gap from Research to Real-World
Applications, 2013, pp. 131-153.
[7] H. Rivera-Flor, D. Gurve, A. Floriano, D. Delisle-
Rodriguez, R. Mello, and T. Bastos-Filho, "CCA-Based
Compressive Sensing for SSVEP-Based Brain-Computer
Interfaces to Command a Robotic Wheelchair," IEEE
Transactions on Instrumentation and Measurement, vol.
71, pp. 1-10, 2022.
[8] A. Casey, H. Azhar, M. Grzes, and M. Sakel, "BCI
controlled robotic arm as assistance to the rehabilitation of
neurologically disabled patients," Disability and
Rehabilitation: Assistive Technology, vol. 16, no. 5, pp.
525-537, 2021.
[9] D. Kapgate, "Future of EEG based hybrid visual brain
computer interface systems in rehabilitation of people with
neurological disorders," International Research Journal
on Advanced Science Hub, vol. 2, no. 6, pp. 15-20, 2020.
[10] K. Laghari, R. Gupta, S. Arndt, J. N. Antons, R. Schleicher,
S. Moller, T. Falk, “Auditory BCIs for visually impaired
users: Should developers worry about the quality of text-
to-speech readers?,” In Proceedings of the Fifth
International Brain-Computer Interface Meeting 2013, pp.
3-7, 2013.
[11] A. Walter, G. Naros, A. Roth, W. Rosenstiel, A.
Gharabaghi, and M. Bogdan, "A brain-computer interface
for chronic pain patients using epidural ECoG and visual
feedback," in 2012 IEEE 12th International Conference on
Bioinformatics & Bioengineering (BIBE), 2012, pp. 380-
385.
[12] M. K. Kumar, B. D. Parameshachari, S. Prabu, and S.
liberata Ullo, "Comparative analysis to identify efficient
technique for interfacing BCI system," in Proceedings of
the IOP Conference Series: Materials Science and
Engineering, vol. 925, no. 1, September 2020.
[13] N. Birbaumer, C. Weber, C. Neuper, E. Buch, K. Haapen,
and L. Cohen, "Physiological regulation of thinking:
brain–computer interface (BCI) research," Progress in
Brain Research, vol. 159, pp. 369-391, 2006.
[14] C. Wegemer, "Brain-computer interfaces and education:
the state of technology and imperatives for the future,"
International Journal of Learning Technology, vol. 14, no.
2, pp. 141-161, 2019.
[15] A. Nijholt, J. L. Contreras-Vidal, C. Jeunet, and A.
Väljamäe, "Brain-Computer Interfaces for Non-clinical
(Home, Sports, Art, Entertainment, Education, Well-
Being) Applications," Frontiers in Computer Science, vol.
4, 2022.
[16] A. A. Khan, A. A. Laghari, A. A. Shaikh, M. A. Dootio, V.
V. Estrela, and R. T. Lopes, "A blockchain security
module for brain-computer interface (BCI) with
multimedia life cycle framework (MLCF)," Neuroscience
Informatics, vol. 2, no. 1, pp. 1-14, 2022.
[17] M. Hamedi, S. H. Salleh, and A. M. Noor,
"Electroencephalographic motor imagery brain
connectivity analysis for BCI: a review," Neural
Computation, vol. 28, no. 6, pp. 999-1041, 2016.
[18] S. Vaid, P. Singh, and C. Kaur, "EEG signal analysis for
BCI interface: A review," in Proceedings of the 2015 Fifth
International Conference on Advanced Computing &
Communication Technologies, pp. 143-147, February
2015.
[19] D. Bright, A. Nair, D. Salvekar, and S. Bhisikar, "EEG-
based brain controlled prosthetic arm," in Proceedings of
the 2016 Conference on Advances in Signal Processing
(CASP), pp. 479-483, June 2016.
[20] R. A. Ramadan, S. Refat, M. A. Elshahed, and R. A. Ali,
"Basics of brain computer interface," in Brain-Computer
Interfaces: Current Trends and Applications, pp. 31-50,
2015.
[21] Y. Kwak, W. J. Song, and S. E. Kim, "FGANet: fNIRS-
guided attention network for hybrid EEG-fNIRS brain-
computer interfaces," IEEE Transactions on Neural
Systems and Rehabilitation Engineering, vol. 30, pp. 329-
339, 2022.
[22] X. Li, J. Chen, N. Shi, C. Yang, P. Gao, X. Chen, and X.
Gao, "A hybrid steady-state visual evoked response-based
brain-computer interface with MEG and EEG," Expert
Systems with Applications, vol. 223, p. 119736, 2023.
[23] M. Angrick, M. Ottenhoff, L. Diener, D. Ivucic, G. Ivucic,
S. Goulis, and C. Herff, "Towards closed-loop speech
synthesis from stereotactic eeg: a unit selection approach,"
in Proceedings of the ICASSP 2022-2022 IEEE
International Conference on Acoustics, Speech and Signal
Processing (ICASSP), pp. 1296-1300, May 2022.
[24] A. Moly, T. Costecalde, F. Martel, M. Martin, C. Larzabal,
S. Karakas, and T. Aksenova, "An adaptive closed-loop
ECoG decoder for long-term and stable bimanual control
of an exoskeleton by a tetraplegic," Journal of Neural
Engineering, vol. 19, no. 2, pp, 1-25, 2022.
[25] D. Khodagholy, J. J. Ferrero, J. Park, Z. Zhao, and J. N.
Gelinas, "Large-scale, closed-loop interrogation of neural
circuits underlying cognition," Trends in Neurosciences,
2022.
[26] M. Arvaneh, I. H. Robertson, and T. E. Ward, "A P300-
based brain-computer interface for improving attention,"
Frontiers in Human Neuroscience, vol. 12, pp. 1-14, 2019.
[27] G. Perez-Garcia, M. Bicak, J. V. Haure-Mirande, G. M.
Perez, A. Otero-Pagan, M. A. G. Sosa, and G. A. Elder,
"BCI-838, an orally active mGluR2/3 receptor antagonist
pro-drug, rescues learning behavior deficits in the PS19
Science, Engineering and Technology Vol. 3, No. 2, Online First
Online First
MAPTP301S mouse model of auxopathy," Neuroscience
Letters, 2023.
[28] U. Salahuddin and P. X. Gao, "Signal Generation,
Acquisition, and Processing in Brain Machine Interfaces:
A Unified Review," Frontiers in Neuroscience, vol. 15,
2021.
[29] A. Alhudhaif, "An effective classification framework for
brain-computer interface system design based on
combining of fNIRS and EEG signals," PeerJ Computer
Science, vol. 7, 2021.
[30] S. Aggarwal and N. Chugh, "Signal processing techniques
for motor imagery brain computer interface: A review,"
Array, vol. 1, pp. 1-12, 2019.
[31] J. J. Shih, D. J. Krusienski, and J. R. Wolpaw, "Brain-
computer interfaces in medicine," in Mayo Clinic
Proceedings, vol. 87, no. 3, pp. 268-279, March 2012.
[32] M. Alimardani, S. Nishio, and H. Ishiguro, "The
importance of visual feedback design in BCIs; from
embodiment to motor imagery learning," PloS One, vol.
11, no. 9, p. e0161945, 2016.
[33] C. Jeunet, C. Vi, D. Spelmezan, B. N’Kaoua, F. Lotte, and
S. Subramanian, "Continuous tactile feedback for motor-
imagery based brain-computer interaction in a
multitasking context," in Proceedings of the Human-
Computer Interaction–INTERACT 2015: 15th IFIP TC 13
International Conference, Bamberg, Germany, September
14-18, 2015, Part I, pp. 488-505, Springer International
Publishing.
[34] Y. Ai, "A novel closed-loop BCI system based on
comparison of multiple methods," in Proceedings of the
International Conference on Computer, Artificial
Intelligence, and Control Engineering (CAICE 2022), vol.
12288, pp. 506-513, SPIE, December 2022.
[35] Emotiv, "EEG Guide," Retrieved from
https://www.emotiv.com/eeg-guide/ Accessed Feb 22
2023.
[36] Johns Hopkins Medicine, "Electroencephalogram (EEG),"
Retrieved February 23, 2023, from
https://www.hopkinsmedicine.org/health/treatment-tests-
and-therapies/electroencephalogram-eeg
[37] N. Sulaiman, K. S. Goh, M. Rashid, S. Jadin, M. Mustafa,
M. Z. Ibrahim, and F. Samsuri, "Offline LabVIEW-Based
EEG Signals Analysis to Detect Vehicle Driver
Microsleep," in Proceedings of the International
Conference on Movement, Health and Exercise, pp. 271-
289, Springer, Singapore, September 2019.
[38] R. Salmelin and R. Hari, "Spatiotemporal characteristics of
sensorimotor neuromagnetic rhythms related to thumb
movement," Neuroscience, vol. 60, no. 2, pp. 537-550,
1994.
[39] S. M. Nacy, S. N. Kbah, H. A. Jafer, I. Al-Shaalan,
“Controlling a servo motor using EEG signals from the
primary motor cortex,” American Journal of Biomedical
Engineering, vol. 6, no. 5, pp. 139-146, 2016.
[40] A. Aldridge, E. Barnes, C. L. Bethel, D. W. Carruth, M.
Kocturova, M. Pleva, and J. Juhar, "Accessible
electroencephalograms (EEGs): A comparative review
with openbci’s ultracortex mark IV headset," in
Proceedings of the 29th International Conference
Radioelektronika (RADIOELEKTRONIKA), pp. 1-6,
IEEE, April 2019.
[41] M. TajDini, V. Sokolov, I. Kuzminykh, S. Shiaeles, and B.
Ghita, "Wireless sensors for brain activity—a survey,"
Electronics, vol. 9, no. 12, pp. 1-26, 2020.
[42] M. Rashid, N. Sulaiman, P. P. Abdul Majeed, R. M. Musa,
B. S. Bari, S. Khatun, “Current status, challenges, and
possible solutions of EEG-based brain-computer interface:
a comprehensive review,” Frontiers in Neurorobotics, vol.
14, pp. 1-35, 2020.
[43] Neurosity. (n.d.). Home. Retrieved April 8, 2023, from
https://neurosity.co/
[44] N. Veena, N. Anitha, “A review of non-invasive BCI
devices,” International Journal of Biomedical
Engineering and Technology, vol. 34, no. 3, pp. 205-233,
2020.
[45] F. Yousefi, H. Kolivand, “Brain Signals as a New
Biometric Authentication Method Using Brain-Computer
Interface,” In Lee, N. (eds) Encyclopedia of Computer
Graphics and Games, pp. 1-14, 2019.
https://doi.org/10.1007/978-3-319-08234-9_370-1
[46] E. D. Floreani, D. Kelly, D. Rowley, B. Irvine, E. Kinney-
Lang, A. Kirton, “Iterative Development of a Software to
Facilitate Independent Home Use of BCI Technologies for
Children with Quadriplegic Cerebral Palsy,” In 2022 44th
Annual International Conference of the IEEE Engineering
in Medicine & Biology Society (EMBC), pp. 3361-3364,
July 2022.
[47] E. D. Floreani, D. Kelly, D. Rowley, B. Irvine, E. Kinney-
Lang and A. Kirton, "Iterative Development of a Software
to Facilitate Independent Home Use of BCI Technologies
for Children with Quadriplegic Cerebral Palsy," In 2022
44th Annual International Conference of the IEEE
Engineering in Medicine & Biology Society (EMBC),
Glasgow, Scotland, United Kingdom, pp. 3361-3364,
2022.
[48] G. Onose, C. Grozea, A. Anghelescu, C. Daia, C. J.
Sinescu, A. V. Ciurea, F. Popescu, “On the feasibility of
using motor imagery EEG-based brain-computer interface
in chronic tetraplegics for assistive robotic arm control: a
clinical test and long-term post-trial follow-up,” Spinal
Cord, vol. 50, no. 8, 599-608, 2012.
[49] M. Tariq, P. M. Trivailo, M. Simic, “EEG-based BCI
control schemes for lower-limb assistive-robots,”
Frontiers in Human Neuroscience, vol. 12, pp. 1-20, 2018.
[50] R. Spataro, A. Chella, B. Allison, M. Giardina, R. Sorbello,
S. Tramonte, V. La Bella, “Reaching and grasping a glass
Science, Engineering and Technology Vol. 3, No. 2, Online First
Online First
of water by locked-in ALS patients through a BCI-
controlled humanoid robot,” Frontiers in Human
Neuroscience, vol. 11, pp. 1-10, 2017.
[51] B. Zhang, J. Wang, T. Fuhlbrigge, “A review of the
commercial brain-computer interface technology from the
perspective of industrial robotics,” In 2010 IEEE
International Conference on Automation and Logistics,
pp. 379-384, August 2010.
[52] P. Sirikongtham, W. Paireekreng, S. C. Chit, “Brainwave
Detection Model for Panic Attacks Based on Event-related
Potential,” Journal of University of Babylon for Pure and
Applied Sciences, vol. 27, no. 1, pp. 333-344, 2019.
[53] V. Kohli, U. Tripathi, V. Chamola, B. K. Rout, S. S.
Kanhere, “A review on Virtual Reality and Augmented
Reality use-cases of Brain Computer Interface based
applications for smart cities," Microprocessors and
Microsystems, vol. 88, 2022.
[54] J. R. Munavalli, P.R. Sankpal, A. Sumathi, J. M. Oli,
“Introduction to Brain–Computer Interface: Applications
and Challenges,” In Brain-Computer Interface: Using
Deep Learning Applications, pp. 1-24, 2023.
[55] S. Bhattacharyya, D. Basu, A. Konar, D. N. Tibarewala,
“Interval type-2 fuzzy logic based multiclass ANFIS
algorithm for real-time EEG based movement control of a
robot arm,” Robotics and Autonomous Systems, vol. 68,
pp. 104-115, 2015.
[56] D. H. Krishna, I. A. Pasha, T. S. Savithri, “Autonomuos
robot control based on EEG and cross-correlation,” In
2016 10th International Conference on Intelligent Systems
and Control (ISCO), pp. 1-4, January 2016.
[57] M. Plechawska-Wojcik, P. Wolszczak, R. Cechowicz, K.
Łygas, “Construction of neural nets in brain-computer
interface for robot arm steering,” In 2016 9th International
Conference on Human System Interactions (HSI), pp. 348-
354, July 2016.
[58] A. Vijayendra, S. K. Saksena, R. M. Vishwanath, S. N.
Omkar, “A performance study of 14-channel and 5-
channel EEG systems for real-time control of unmanned
aerial vehicles (UAVs),” In 2018 Second IEEE
International Conference on Robotic Computing (IRC),
pp. 183-188, January 2018.
[59] J. Cantillo-Negrete, R. I. Carino-Escobar, P. Carrillo-
Mora, D. Elias-Vinas, J. Gutierrez-Martinez, J. “Motor
imagery-based brain-computer interface coupled to a
robotic hand orthosis aimed for neurorehabilitation of
stroke patients,” Journal of Healthcare Engineering, vol.
2018, Article ID 1624637, 2018.
[60] R. Bousseta, I. El Ouakouak, M. Gharbi, F. Regragui,
“EEG based brain computer interface for controlling a
robot arm movement through thought,” IRBM, vol. 39, no.
2, 129-135, 2018.
[61] M. Aljalal, R. Djemal, S. Ibrahim, S. “Robot navigation
using a brain computer interface based on motor imagery,”
Journal of Medical and Biological Engineering, vol. 39,
pp. 508-522, 2019.
[62] Y. Liu, W. Su, Z. Li, G. Shi, X. Chu, Y. Kang, W. Shang,
W, "Motor-imagery-based teleoperation of a dual-arm
robot performing manipulation tasks." IEEE Transactions
on Cognitive and Developmental Systems, vol. 11, no. 3,
pp. 414-424, 2018.
[63] Y. Xu, C. Ding, X. Shu, K. Gui, Y. Bezsudnova, X. Sheng,
D. Zhang, “Shared control of a robotic arm using non-
invasive brain–computer interface and computer vision
guidance,” Robotics and Autonomous Systems, vol. 115,
pp. 121-129, May 2019.
[64] S. Bhattacharyya, A. Konar, D. N. Tibarewala, "Motor
imagery and error-related potential induced position
control of a robotic arm," IEEE/CAA Journal of
Automatica Sinica, vol. 4, no. 4, pp. 639-650, 2017.
[65] N. Yan, C. Wang, Y. Tao, J. Li, K. Zhang, T. Chen, G.
Wang, "Quadcopter control system using a hybrid BCI
based on off-line optimization and enhanced human-
machine interaction," IEEE Access, vol. 8, pp. 1160-1172,
2019.
[66] J. Lee, K. Cha, H. Kim, J. Choi, C. Kim, S. Lee, "Hybrid
MI-SSSEP Paradigm for classifying left and right
movement toward BCI for exoskeleton control," In 2019
7th International Winter Conference on Brain-Computer
Interface (BCI), pp. 1-3, 2019.
[67] J. Choi, S. Jo, "Application of hybrid Brain-Computer
Interface with augmented reality on quadcopter control,"
In 2020 8th International Winter Conference on Brain-
Computer Interface (BCI), pp. 1-5, 2020.
[68] A. Rakshit., A. Konar, & A. K. Nagar, "A hybrid brain-
computer interface for closed-loop position control of a
robot arm," IEEE/CAA Journal of Automatica Sinica, vol.
7, no. 5, pp. 1344-1360, 2020.
[69] M. F. Mridha, S. C. Das, M.M. Kabir, A. A. Lima, M. R.
Islam, Y. Watanobe, "Brain-computer interface:
Advancement and challenges," Sensors, vol. 21, no. 17,
2021.
[70] Peterson, V., Galván, C., Hernández, H., & Spies, R.
(2020). "A feasibility study of a complete low-cost
consumer-grade brain-computer interface system."
Heliyon, 6(3), e03425.
[71] Ekandem, J. I., Davis, T. A., Alvarez, I., James, M. T., &
Gilbert, J. E. (2012). "Evaluating the ergonomics of BCI
devices for research and experimentation." Ergonomics,
55(5), 592-598.
[72] E. Hildt, "Brain-computer interaction and medical access
to the brain: individual, social and ethical implications."
Studies in Ethics, Law, and Technology, vol. 4, no. 3, 2011.
