














































The Role of the Gut Microbiome in Neuromodulation Therapies as a Potential Treatment Adjunct for Multiple Sclerosis


Berkeley
Pharma Tech
Journal of Medicine

Correspondence: 
roshni.nandwani@emory.edu

Keywords:
Brain-Machine Interface 
Neuroengineering
Paralysis
Communication Disabilities 
EEG

Submitted July 07, 2024 
Accepted July 08, 2024

Published December 27, 2024

Full Open Access

Creative Commons Attribution 
License 4.0

Abstract
Brain-machine connections and neuroengineering are technologies that can be used to 
act as a direct communication pathway between the brain’s electrical activity and an 
external device. This paper explores the realm of Brain-Machine Interfaces (BMIs) and 
neuroengineering technologies, focusing on their potential to address cognitive and 
mobility impairments. This comprehensive literature review delves into the current state 
of BMI technology, emphasizing its applications in stroke rehabilitation, paralysis 
management, prosthetics, memory enhancement, and communication solutions for 
speech impairments. Various methodologies, from invasive to non-invasive techniques, 
are examined for their impact on patients’ quality of life. While these advancements hold 
great promise, they also pose ethical and practical challenges, including the need for 
further clinical testing, algorithm refinement, and affordability concerns. The future of 
BMIs is envisioned considering the role of artificial intelligence in expanding their 
possibilities and reflecting on the ethical considerations in deploying these technologies. 
This review provides valuable insights into the current landscape of BMIs, their potential 
for enhancing human capabilities, and the critical issues that must be addressed as the 
field progresses.

Cognitive and Mobility Enhancement 
through Brain-Machine Interfaces and 
Neuroengineering
By: Roshni Nandwani, Srilakshmi Palanikumar, Sahaj Sidhu Singh and Jehan Balading



1. Introduction

The brain is often considered one of the most important organs in the
human body because of the wide range of functions it performs. It is also
extremely complicated, with over 100 billion neurons.1 Equal numbers of
neuronal and nonneuronal cells make the human brain an isometrically
scaled-up primate brain.2 As a result, managing diseases and impairments of
the brain presents significant challenges. Modern science has begun to move
away from looking at individual neurons, as it did in the past. Now,
scientists are pivoting towards observing neural networks and neural
ensembles. BMIs emerged from this direction of research.

A BMI is a computer-based system that acquires brain signals, analyzes
them, and translates them into commands that are related to output devices.
These commands then carry out desired actions, aiding the user to do things
they previously would not have been able to.4

Berkeley Pharma Tech Journal of Medicine | 23



Figure 1. A   Decoding Process Using an Invasive Method of EEG Recording to Yield
Motor Function. The EEG recording decodes the brain’s intentions, resulting in motor
control of prosthetics and other external devices.

Research on brain-machine interfaces and brain-computer interfaces
began as early as the 1970s. Tests were initially conducted on primate
subjects until they were considered safe enough to be tested on humans in
the 1990s.5 As this technology evolved, ethical concerns increased in
importance. Like with other kinds of medical procedures, finding a
balance between repeated procedures on a highly sensitive organ and being
able to have the BMI be as effective as possible poses various ethical
concerns. Additionally, because BMIs are often used in treating cognitive
afflictions, it is important to consider the patient’s agency and consent.6

Furthermore, the concerns related to data privacy and the potential for
misuse of neural data upon conducting continuous monitoring of brain
activity further raises questions regarding BMI technology ethics. The
high amount of interest from the scientific community comes from the
potential for BMIs to aid the rehabilitation of devastating diseases that are
difficult to manage. In the past, they have shown capability in in regard to
helping patients who are afflicted by strokes, chronic pain, paralysis, and
other cognition and mobility-related problems.7

While current BMIs already offer significant amounts of aid in helping
patients manage neurological disease, there are many open avenues for
research in this field. For instance, artificial intelligence could bring various
new possibilities to the applications of BMIs. Additionally, predictive
models are believed to be able to bring function back to amputees and
more severe cases of paralysis.8

In this review, though, we focus on the BMI and neuroengineering
technologies that currently exist and have the potential to resolve various
cognitive and mobility impairments. We review current scientific literature
to gauge the potential of this technology in enhancing quality of life
through BMI in memory, mobility, and home improvement technologies.
We also look into potential areas of future research including the use of
artificial intelligence with BMIs. Finally, we discuss the role that clinical

Berkeley Pharma Tech Journal of Medicine | 24



trials have played in the current research in human subjects and their
success/areas of improvement going forward.

1.2 Methods

In this review, we conducted a literature search of the sources, key studies,
and findings describing issues and conditions related to BMI and cognitive
and mobility impairments. Our literature review focused on the current
technologies that exist to resolve and enhance the quality of life of those
with mobile and cognitive impairments. Studies included in Table 1 met
the criteria of being a recent study, published within the last 10 years, as
well as fitting the keywords of BMI, neuroengineering, EEG, and the name
of the condition or improvement technology. Our sources were collected
from globally acknowledged databases, such as PubMed and
ResearchGate. Prominent journals include the American Physiological
Society, Science, Frontiers Neural Interface, and Journal of
Neuroengineering. The information provided in the selected recent
studies (ie. purpose of study, results of scientific findings, and impact of
paper’s outcome) were carefully evaluated and discussed in the following
section.

2. Literature Review

2.1 Mobility Conditions

2.11 Stroke

Stroke is a sudden interruption of blood supply to the brain, causing
damage to brain tissue. There are two primary types of strokes: ischemic
and hemorrhagic, and both can be highly dangerous.9 They often lead to
motor deficits, communication impairments, and cognitive challenges.10

Current stroke rehabilitation approaches include physical therapy, speech
therapy, and pharmaceutical interventions. However, due to the
complexity of stroke recovery, innovative methods like BMI are being
explored as potential game-changers in rehabilitation.11

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BMIs designed for stroke recovery work by decoding neural signals from
the brain and translating them into meaningful actions.12 This
technology’s application in stroke rehabilitation is a beacon of hope,
particularly for those who have limited mobility or communication
abilities. By allowing individuals to interact with the external world and
control assistive devices directly with their thoughts, BMIs offer a new
frontier for enhancing independence and recovery post-stroke.

There are two types of BMI based approaches to stroke rehabilitation:
invasive and noninvasive. A popular technique associated with both of
these approaches is signal acquisition. Invasive techniques use intracranial
microelectrode arrays, while noninvasive techniques use
electroencephalograms (EEG).13 Both of these methods collect brain
signals, which are then processed and decoded. A long-standing issue at
this point is noise in signal decoding. They have been tested in clinical
settings, and the findings are promising; they solidify the presence of BMIs
as a method of rehabilitation for mobility conditions.

For example, a study exists that shows that BMI-based rehabilitation shows
long-term improvements in the motor function of people with severe
hand paresis. They found that BMI-based therapy, specifically targeting
movements via EEG-triggered robotic assistance, led to lasting
improvements in motor function six months after the intervention.14

Similarly, another study was conducted in which 28 patients with motor
deficits were treated with motor imagery (MI) training, and only half of
them received BMI-based rehabilitation in addition. Fugl–Meyer
Assessments showed that BMI-assisted rehabilitation was significantly
more effective than pure MI treatments.15

Despite their potential, BMIs for stroke recovery face several challenges
that must be addressed to fully recognize their benefits. One significant
challenge is the need for more extensive clinical testing to validate the
efficacy and safety of BMI interventions across diverse patient populations.
Though initial studies have already indicated that BMI-assisted
rehabilitation compared to motor imagery training alone have led to better

Berkeley Pharma Tech Journal of Medicine | 26



outcomes, these findings need to be replicated in a larger, more diverse
setting in order to ensure generalizability and further reliability.

Another critical issue is the fine-tuning of algorithms used in BMIs. The
accuracy of such fine-tuning systems is dependent on their ability to
decode neural signals precisely. Advances in machine learning and signal
processing are imperative to create more robust and adaptive algorithms
that can learn from patient data and improve performance over time.

Moreover, affordability is a major concern, as the high cost of developing
BMI technology limits its widespread adoption. There are significant costs
associated with intracranial surgeries and proper training and equipment,
limiting accessibility to a broader patient population.

Figure 2. Functional Near-Infrared Spectroscopy (fNIRS) in Action. fNIRS is placed
on the patient’s head. The detector collects the data from the light emitter and then the
data is transported and decoded (a computer). This data is used to control basic motor
functions in a prosthetic.

2.12 Paralysis

BMIs have revealed potential in aiding individuals with paralysis resulting
from amyotrophic lateral sclerosis (ALS), a disease that leads to complete

Berkeley Pharma Tech Journal of Medicine | 27



loss of motor function and speech. In particular, BMIs have been
instrumental in helping individuals communicate when they are in a
completely locked-in state (CLIS). There are various types of BMIs,
including EEG-based BMIs for ALS patients with functioning vision and
eye control, and fNIRS-based BMIs (functional near-infrared
spectroscopy) that offer spatial and temporal resolution, making them
suitable for bedside applications. While EEG-based BMIs have achieved
success in ALS patients with some motor function, they are limited in
CLIS patients. On the other hand, fNIRS-based BMIs have facilitated
communication for individuals in the most severe stages of ALS,
providing hope for enhanced quality of life.

The electroencephalography (EEG) based BMI’s main task is to restore
communication and control for individuals with ALS. These BMIs
employ EEG to locate and interpret brain signals associated with
movement. Through decoding these signals, the BMIs enable the signal to
output desired physical actions. These signals allow one’s mind to control
assistive technology, proving autonomous control over assistive
technology. This technology enhances the independence of individuals
with ALS. The cognitive abilities needed to interact with the environment
are restored.

Meanwhile, Functional Near-Infrared Spectroscopy (fNIRS) based BMIs
work through decoding neural activity. They achieve this through
measuring changes in blood oxygenation in the brain. These are
non-invasive BMIs, unlike EEG. They work by shining near-infrared light
into the scalp, which penetrates the skull and interacts with oxygenated
and deoxygenated hemoglobin in the brain's blood vessels. Through
detecting variations in light, the BMI maps brain activity. Finally, this
information is decoded and used to control external devices such as
prosthetics or computer devices. fNIRS-based BMIs are promising for
individuals with motor disabilities or conditions like paralysis as they serve
to regain control and improve quality of life through thought-based
communication and control.

Berkeley Pharma Tech Journal of Medicine | 28



One of the downsides of fNIRS is that they provide lower spatial
resolution compared to other invasive BMIs like EEG since they only
capture visual information from outside the brain. However, since they
are non-invasive, the technology is more accessible and safer to implement.

Figure 3. EEG-Based Brain-Machine Interfaces for Mobility and Communication.
Combination for emplace electrodes and EEG based BMIs not only provide mobility
through a wheelchair. This also connects with compatible tablets and computers giving
autonomous control over the software.

2.13 Prosthetics

Prostheses are substitutes for parts of the body lost due to accidents,
amputation, or birth defects. Types of prosthetics include arm and leg
prostheses that fit over joints, artificial eyeballs, and hearing aids.

The way BMI is incorporated to develop new prosthetics, specifically
neural prosthetics, is by recording signals from neuron populations and
decoding the signals through mathematical algorithms. They then translate
the intended action into physical limb movement.15 Specific feedback calls

Berkeley Pharma Tech Journal of Medicine | 29



like somatosensory feedback are used to provide a more natural sensation
for prosthetics.

Somatosensation is a system of biological organs that respond to inputs
and body states. The basis of this paradigm makes it so that the prosthetic
user can use patterns of intensity and frequency of stimulation that are
critical to perceived sensation. Benefits to using such a system is true
embodiment of the limb; normal motor function; decrease in negative
psychological impact of limb loss.14,15

2.2 Cognitive Impairments

2.21 Memory

While BMI technology has traditionally focused on restoring or enhancing
sensory perceptions and muscular abilities, scientists are now venturing
into a new realm of BMIs. These emerging technologies expand mobile
conditions and incorporate implanted computers to amplify the
formation and retrieval of memories within the brain.16 Following aging or
memory related diseases such as dementia and Alzheimer’s, certain regions
of the brain become slower due to a reduction in both neurotransmitters
and blood flow to these areas.17

Following this trajectory, developers of BMI have utilized non-invasive
BMIs.18 Electromagnetic stimulation and biofeedback techniques as a part
of a rehabilitation strategy to adjust brain activity. In addition, by focusing
on specific brain wave patterns in the alpha and theta band regions,
scientists can predict memory performance and enhance episodic memory.
This usage of the technology is especially helpful as episodic memory - the
long-term memory associated with previous experiences and associated
emotions19 - is the memory system that is most rapidly affected in response
to both normal and unhealthy aging. Furthermore, electrophysiological
signals could be connected to particular behavioral conditions whereby
altering the timing of specific stimuli could enhance memory encoding. In
other words, if these signals can serve as a “memory enhancement” just
before engaging in specific tasks, elderly patients could give themselves a
cognitive boost before remembering where they parked their car or

Berkeley Pharma Tech Journal of Medicine | 30



comprehending their physician’s instructions for medication use. Such
technologies have the potential to allow patients with pathological
memory loss the ability to perform daily life activities.

The use of BMI and neuroengineering technologies to resolve memory
decline serves almost as a cognitive prosthesis, which is specifically useful
for patient populations that need restoring their ability to form new long
term-memories, especially after the natural process of aging or
post-Alzheimer’s disease.

2.22 Communication and Speech Impairments

Individuals affected by neurological conditions like brainstem stroke or ALS
(4.2 Paralysis) often encounter profound difficulties with both their
mobility and their speech functions.20 For the latter, in some instances, these
patients may experience a total inability to communicate verbally, a
condition known as locked-in syndrome. To address this condition,
researchers and engineers have worked on speech-based BMI programs to
resolve such speech-based cognitive issues and ultimately improve
communication disabilities.

One such example of this speech-based BMI program is a technology called
a neuroprosthesis, which is an approach that decodes words and sentences
directly from the cerebral cortical activity of impaired individuals. This
technology involves surgically placing a high-density electrode on the
outermost layer of the sensorimotor cortex, which is the part of the brain
most responsible for controlling speech and having the patient attempt to
speak with computer models connected.21 As the participant attempts to
articulate words from a list of basic vocabulary words, advanced
deep-learning algorithms and computer models recognize and categorize
words based on patterns in the brain activity. Using these computational
models and a natural-language model that predicted the likelihood of the
next word based on the words preceding it, the participants’ words are
translated into complete sentences.22

Berkeley Pharma Tech Journal of Medicine | 31



Figure 4. Advanced Neuroprosthetics for Speech Communication. Invasive BMI
systems record accurate speech data through EEGs which are converted to words via
UMA- BCI (University of Málaga-Brain-Computer Interfaces). Voice assistants like
Google, Siri and Alexa are used for voice commands.

Though this technology is limited to vocabulary that is not very advanced,
future directions in the field are working on mitigating this and expanding
current technologies to include vocabulary lists with over 150,000 words
with less than 9% error.23 Additionally, when looking at these technologies
that are directly related to the brain and speech, the implications of their
value from an ethical standpoint must be considered. Though
neuroprosthetics for speech decoding have the potential to inform consent
and capacity - allow for agency and consent to medical treatment - this
technology, for the same reason, may cause harm.24

Neurotechnology-mediated communication can pose a risk to privacy, and
in the case of defamation, threats, or harassment that is recorded from the
participant, poses the question of whether or not they should be held
responsible.25

Berkeley Pharma Tech Journal of Medicine | 32



These technologies, as powerful as they are in showing feasible paths for
restoring rapid communication with those with communication
disabilities, must be tweaked to account for both ethical and practical
concerns as the technology advances.

2.3 Life-Enhancing Technologies

2.31 Home Improvement and Life Enhancing BMI Technologies

In order to help mitigate the effects of these impairments on the

populations they affect, developers and businesses have incorporated

artificial intelligence (AI) technology and electronics with home

improvement tools to ease daily life functions. Whether its voice

controlled speakers, AI cellphone assistants, or clap activated lights,

the use of BMI and neuroengineering take home improvement

advancements to a new level.

Building on this foundation of technological innovation, BTC (Brain to

Thing Communication) represents a significant leap forward. BTC is a

system that establishes direct connection from the human brain to

outside devices such as TV’s and other smart objects. This development

has been helpful, as it can improve the lives of those disabled or

advance home improvement in general.

The features of the BTC system consists of nearby smart objects that

will be embedded with systems running sensing and actuation

services. This makes the system aware of where the subject is around

the house and adjusts accordingly. In fact, users can access the system

throughout their daily lives no matter what and can remotely send

brain commands via the application. In other words, patients with

paralysis can perform different applications (control a wheelchair,

nursing bed, lights, TV, and phone) inside a room through BMI based

Berkeley Pharma Tech Journal of Medicine | 33



on biological signals (ie. left eye blink, right eye blink, eyebrow

raise).26

Nevertheless, these technologies do come with various challenges.

First, real-time EEG signal processing can pose difficulties whereby

all commands should be processed in real time in order for the system

to validate the action request. Secondly, while previous studies have

demonstrated the accuracy of these prototypes, it is crucial to

acknowledge that the system still has limitations in distinguishing

specific patterns that differentiate user actions and requests. This is an

area that requires significant improvement in the technology in the

years to come.27

Figure 5. Enhancing Mobility with Assistive Exoskeletons. Assistive Exoskeleton is
one of the examples of life-enhancing technologies. With the use of non-invasive BMI’s the
suit increases mobility in the elderly and heavily reduces the risk of critical falls.

Berkeley Pharma Tech Journal of Medicine | 34



2.4 Literature Review Summary

Table 1. Literature Review Study Examples of Related Work for BMI Applications
Organized by Mobile, Cognitive, and Home Improvement Technologies. The table
provides an overview of key findings and their implications for advancing BMI research.

3. Discussion

3.1 Key Findings and Implications of Research

In terms of stroke rehabilitation, BMIs allow individuals with limited
mobility and communication abilities to regain independence. For those

Berkeley Pharma Tech Journal of Medicine | 35



with full paralysis, EEG-based BMIs provide communication and control
options for ALS patients, while fNIRS-based BMIs offer potential for those
in a completely locked-in state. For this reason, both invasive and
non-invasive approaches have shown improvements in motor function, but
challenges remain, including the need for further clinical testing, algorithm
refinement, and affordability concerns.

From the cognition standpoint, emerging BMI technologies are targeting
memory improvement, especially for individuals dealing with
memory-related diseases like Alzheimer's. Non-invasive approaches, such as
electromagnetic stimulation and biofeedback, aim to enhance episodic
memory and cognitive function. Meanwhile, speech-based BMIs have the
potential to restore communication for individuals with severe speech
disabilities. These technologies can decode words and sentences directly
from brain activity, allowing patients to express themselves. However,
ethical concerns related to privacy, consent, and accountability must be
addressed as these technologies advance.

Lastly, BTC systems enable direct communication between the brain and
external devices, offering the potential to both enhance and improve the
lives of individuals with disabilities and advance home automation.

3.2 Limitations

As mentioned, BMIs hold great potential to revolutionize the way we
interact with technology and understand the human brain. However, BMIs
are not without their limitations, which span economic, ethical, and
scientific domains.

First, BMIs are intricate systems that depend on knowledge of the human
brain, as well as the creation of incredibly sensitive hardware and software.
The need for better signal processing algorithms to increase the accuracy
and reliability of BMIs is one scientific challenge among many.
Furthermore, significant obstacles to research studies are posed by the
variability of brain activity and the possibility of signal noise. Concerns
about potential risks and side effects are also raised due to the fact that the

Berkeley Pharma Tech Journal of Medicine | 36



long-term effects of brain-computer interfaces are still not fully
understood.28

Second, a primary limitation of BMIs is their cost. Developing and
deploying BMI technology is expensive, making it inaccessible to many
individuals and institutions. The economic barriers limit the widespread
adoption of BMIs, particularly for medical purposes. Patients with
neurological disorders who could benefit from BMIs often struggle to
afford the technology, creating a disparity in access to potentially
life-changing treatments. Addressing these economic challenges through
cost reduction and better funding mechanisms is a pressing concern for
BMI development.29

Third, ethical dilemmas surrounding BMIs are significant and multifaceted.
For instance, BMIs raise significant ethical issues related to defending the
agency of patients who may not have full cognitive ability or the capacity to
provide informed consent. Protecting the rights and dignity of individuals
with cognitive impairments is a crucial concern. Ensuring that BMIs are
used to enhance the quality of life of these patients without infringing on
their autonomy is paramount. Developing ethical guidelines and regulations
that prioritize the welfare and agency of vulnerable populations is
essential.30 Another ethical concern arises when considering individuals'
perspectives on invasive procedures. Invasive techniques have more
associated risks because of the requirement for surgery, as well as the
possibility of scarring.31

Despite these challenges, the development of BMIs continues to progress.
Efforts are underway to reduce costs, establish ethical guidelines, and
advance scientific research.

4. Future Directions

BMIs hold great potential for enhancing the quality of life for individuals
with cognitive or mobility-based impairments, motivating sustained
innovation in this field. BMIs are expected to witness substantial
enhancements in terms of control precision, communication speed, and

Berkeley Pharma Tech Journal of Medicine | 37



versatility. These advancements will likely involve the integration of
cutting-edge artificial intelligence (AI) techniques for more accurate signal
processing and decoding algorithms, offering individuals with disabilities
more practical and intuitive ways to interact with the external world.
Moreover, ongoing research will continue to focus on neurorehabilitation,
where AI-powered BMIs can revolutionize the recovery process for
individuals affected by neurological disorders. Personalized rehabilitation
programs, guided by AI and responsive to patients' unique needs and
progress, could significantly improve the effectiveness of recovery exercises.

Additionally, the development of neuroprosthetics, including limbs and
sensory devices, will drive future BMI research. These devices aim to
replicate the natural functions of missing or damaged body parts, and AI
will play a pivotal role in making them adaptable and responsive to users'
movements. Moreover, as BMIs expand beyond medical applications, they
have the potential to transform human cognition. Researchers are exploring
ways to use BMIs, enhanced by AI, for cognitive augmentation, memory
improvement, and knowledge transfer, opening up new horizons for human
potential. Overall, the future of BMIs is not only exciting but also
transformative, offering unprecedented possibilities to enhance human lives,
both in healthcare and daily interactions.

4. Conclusion

The field of BMIs and neuroengineering holds great power for improving
the lives of individuals with cognitive and mobility impairments. Continued
research and development are essential to address technical challenges, refine
algorithms, and improve the affordability and accessibility of BMI
technologies. The integration of artificial intelligence (AI) with BMIs
presents new possibilities for enhancing the effectiveness of these
technologies and expanding their applications. Future studies should focus
on long-term outcomes, safety, and usability to ensure that BMIs and
neuroengineering technologies are effective and practical solutions for
patients.

Berkeley Pharma Tech Journal of Medicine | 38



5. Acknowledgements

We would like to acknowledge Berkeley Pharma Tech for connecting us
with the resources and the outlet to discuss our findings in this review
paper.

Berkeley Pharma Tech Journal of Medicine | 39



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