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Soroush Meghdadi Zanjani2and Ali Ghazizadeh1, 2
1Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
2School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
February 22, 2021
Brain-machine interface (BMI) has long been a candidate for treating neurological conditions and
for enhancing mental faculties in humans. Recently, the riveting advances in neuroscience and
neurotechnology fueled by large-scale global investments in research and development are giving
rise to high performance BMI solutions that are poised to enter every aspect of human life. Yet
the landscape of IT hardware and software infrastructures raises serious concerns about the ethical
violations in an era when human brains are to be exposed to contentious intentions by powerful
players in the ecosystem. The impeding risks are particularly heightened by the conveniences offered
by proprietary BMI solutions and the attractions of higher productivity especially with the general
public oblivious to the implications of the forthcoming BMI era. Here, we argue that despite the
rapidly closing window of opportunity, an alternate ecosystem based on four principles of openness,
modularity, offline deployability and least privilege can be implemented to create a safer playfield
with naturally implemented safe-guards for neuroprivacy, neurosecurity and agency while at the same
time facilitating a bright future with commercial high-performance BMI applications.
1 Preamble
The fact that the human brain is the seat of his intelligence and (at least) a contributory cause to all of his perceptions,
memories, thoughts and actions is agreed upon by all scientists and all philosophers of mind including dualists and
monists (barring fringe idealists). In a little more than a century, neuroscience, the modern science of studying the brain
guided by the neuron theory, has made great strides in understanding the human nervous system despite the astonishing
complexity of human brain structure and function. The recent decade has witnessed a renewed wave of organized effort
with brain initiatives across the world and with nations working in partnerships spending hundreds of millions of dollars
each year giving much needed impetus to spearhead development of new technologies for probing brain structure and
function [1
3]. As a result, unprecedented amounts of data on brain architecture and activity from macroscopic [4] to
microscopic [5] resolutions are generated and the causal roles of specific neuronal populations are now being deciphered
at ever increasing levels of spatio-temporal resolutions [6] and with improved behavioral specificities [7].
This riveting progress in neuroscience is probably only matched by another area of intellectual endeavour, the information
technology (IT), this one purely artificial and related to computers. At the hardware level, ever shrinking transistor
sizes and increased memory capacity have sustained exponential increase in computational power right at the finger
tips of the average user [8] and just as the conventional transistor technologies were about to loose steam, the recent
rise of quantum computing promises to enhance computational capacity by multiple orders of magnitudes at least for
a certain class of problems [9] even without requiring extreme conditions [10]. Similarly on the software side, fast
paced advances in machine learning and deep neural networks have already created a paradigm shift in data driven
technologies across the board [11–13] including in neuroscience research [14, 15].
It is not hard to imagine the supernova that will result when these two bodies of knowledge and technology from brains
and machines become fully entangled. Indeed, as far back as 1970s prototypical brain-computer interfaces (BCIs)
were envisioned and implemented starting from electroencephalography (EEG) signals to control simple computer
functions [16, 17]. In the years that followed, extensive research in animals [18
21] have succeeded in controlling more
complex functions such as robotics arms using using single cell electrophysiology. In humans, the main focus has
been on neurological disorders by directly recording and stimulating the nervous system to restore normal functionality.
This has resulted in significant progress in making neuro-prosthetic devices including bionic arms, cochlear implants
and retinal implants [22
25]. In addition to sensorimotor applications, closed loop neuro-feedback setups are also
becoming widely used for modifying cognitive functions in disorders such as depression, attentional disorders and
addiction [26, 27].
However, it is the emergence of high resolution invasive implants that allow for longitudinal recordings and even
selective stimulation of a large population of neurons in humans [24,28
30] that is about to exponentially expand
the repertoire of BMI capabilities. The list of currently realized possibilities that can get a boost from the high-
performance BMI, goes on from controlling real-world objects such as high-performance robotic arms, flying drones
and cyborged animals to immersive virtual realities created and navigated by mere thoughts [31
39] to generating
speech and text from brain signals as well as detecting attention, perceptions, emotions and lies [40
44]. There are
even proof of principle demonstrations for implicit skill transfer into the brain [45] and cooperative problem solving by
networked brains [46,47].Therefore, it is well conceivable that given reasonable effort and sufficient funding, non-verbal
communication between people, readout and induction of memories, knowledge and skills as well as direct interfacing
with artificial intelligence become right-around-the-corner realities [48] as the result of high-performance commercial
BMIs. Indeed, such a game changer prospect have already garnered the attention of diverse and powerful players from
the governments to business sectors alike.
While the possibilities born out of the marriage of brains and machines promise an unprecedented and exciting chapter
in the human history, a closer look at the IT landscape creates a pause in ones unconditional excitement. Despite the
exquisite advances, fundamental vulnerabilities and potentials for violation of human rights to privacy and agency
in the IT sector are aplenty [49]. Currently, many of such vulnerabilities are tolerated in return for the productivity
and conveniences offered, but the extension of the status quo to mental processes, ones most private asset, is highly
alarming. Notably, the concern for human right risks associated with BMI is not new. Indeed a number of groups have
previously voiced concern with regard to issues related to “neuroprivacy”, “neurosecurity” and “agency” [50
54] and
have even produced recommendations on regulatory safeguards and legal aspects arising from BMI usage. Recently
there have been some comprehensive surveys of security risks in BMI and proposals for countermeasures in different
scenerios [55] or data processing anonymizers to reduce privacy risks related to “brain fingerprinting” [56]. However,
little attention is paid to providing alternative and wholesome ecosystems to the risk-prone IT establishment itself that
are compatible with internationally accepted ethical values and standards.
In what follows, we will briefly expand on the rather grim prospects of the current social and technological IT ecosystem
with respect to the end-users rights, examining the major players and factual vulnerabilities along with a glimpse of
actual violations to date. Then, we propose an alternative ecosystem based on a set of principles to better safeguard
the end-users rights. We argue that while the proposed principles do not eliminate the risks completely, they can
nevertheless bring the related processes and products under self correcting checks and balances. We also argue that
these principles, have to be embarked upon at grassroots with urgency. In fact, we may be only a few years away from
the point of no return for protecting the end-user rights.
2 State of the Union
2.1 Ownership and Control
A critical component that has been progressively compromised in the past couple of decades in the IT industry is the
extent of end-user ownership over devices and services s/he legally owns. People are increasingly barred from full
control of the behavior of applications they own especially with respect to the way personal information is accessed and
used. Many applications gain access to personal hardware, data and network with little control over possible misuses.
This is not only true at the software level but also at hardware level where hidden back-doors by device manufacturers
and operating systems or privilege escalation by third party applications may breach agency and privacy [57] or take
control and turn a personal devices into a remote snooping gadget [58,59] or a dysfunctional “brick” [60].
In addition, many people are increasingly relying on exclusive and proprietary products and services, which trap them
into closed ecosystems. Such walled gardens while productive and convenient in many cases, often restrict the users
access to other platforms not deemed kosher by the owners of the ecosystem (e.g. airdrop , nearby share [61, 62])
turning their rights to privileges that can be revoked at the whim of the service provider (e.g. purchased products purged
from user’s “local” library after being removed from the store [63–65])
The end-user ownership is further compromised by the recent trends and the gradual paradigm shifts toward cloud based
computation and storage and the growing trend of network based “as a service” (aaS) products. Such trends reduce
the capacity for oversight, control and flexibility by the end-user. In extreme cases, such scenarios enable power term
dictation and denial of service at the discretion of a “big brother” [66]. More subtly, such violations can be engineered
to be practically indiscernible within the design of the systems, procedures and protocols. This could be especially
alarming as the repercussions of such violations often do not remain confined to the technological aspects per se but
grants the "real owners of products and services" the power to manipulate the environment in ways that can jeopardize
agency and freedom of end-user as evidenced by numerous records of such misuse in the past [67]. In the case of BMI
applications, such loose ownership can have catastrophic consequences for one’s most private asset i.e his mind, with
violations that can be as subtle as performing subliminal brain queries and brain fingerprinting ( [68–70]) to electrical
or magnetic stimulation delivered right into perceptual, motivational or decision making centers of one’s brain.
2.2 Transparency and Complification
The potential violations of end-user rights do not only stem from lack of sufficient control over products and applications
but also from lack of transparency [71]. In most cases the user is not aware of the other entities who share control
of the product, the extent of their control and the way personal information is being used by the trusted entities. As
for information misuse, it is practically impossible for an average user to monitor the inner workings of applications
or to inspect the network packets that are transmitted and received for the content and destinations. Indeed, tracking
and inspection of information access and transmission by applications and devices are hard enough that in many cases
violations of users privacy may go unnoticed for many months or even years.
As a measure to implement the users "right to know", today in most cases elaborate end-user license agreements (EULA)
and terms-of-service (ToS) are prepared for the users prior to service initiation. However, in most cases the complexity
of these legal forms prevents any reasonable understanding of the terms one is consenting to. The difficulty in EULA
comprehension is observed even in law students [72] raising the possibility of intentional "complifications" in ToS
terminology. Aside from the esoteric language, the sheer length of these documents make them impractical to even
be fully browsed [73]. A study shows that only 1 out of 3000 users read a representative EULA document to find a
secret $1000 prize that was embedded in its text [74]. This shows how critical information can be easily missed by
many users if say a BMI application decided to "retain your EEG Data and Experiment Data for scientific or historical
research purposes" while "your user Account will be de-activated and you will lose access to the Service" in response to
a request for erasure of personal information from the vendors server [75].
It is needless to say that having "unwarranted" access to someone’s neural data allows extraction of all sorts of private
or sensitive information. In fact the feasibility of extracting personal information such as credit card pin codes and
choice preferences has been already demonstrated even with EEG signals that do not normally have a high signal to
noise ratio [76]. Given the rapid progress in neuroscience hardware and software technologies and the recent FDA
designations for more invasive high-performance BMI technologies (e.g. Synchron and Neuralink [77]) which provides
access to much richer neural signals, the range of potentials and risks can simultaneously grow to unimaginable levels
compared to current state of affairs. The addition of causal manipulation of the neural processes to the mix has the
potential to close the BMI loop around the windpipe of people’s remaining privacy and agency.
2.3 Imposing Stakeholders
The commoditization of high performance BMIs as a consumer-level product will not only attract isolated small-game
hacking and phishing attempts, but also larger players with more menacing intentions equipped with well-thought plans
and trumping resources. Most notable among these players are the governments and rich high-tech corporations.
As for the governments, keeping law and order often creates an insatiable appetite for gathering intelligence domestically
and internationally and tightening control over ones own constituency. The recent history is replete with leaked or
declassified reports that document such organized data gathering and surveillance schemes which were in many cases
coordinated with large well-known tech companies. Attempts that ranged from mass surveillance, collection of metadata,
embedding backdoors for well-trusted encryptions, unlimited access to smartphones for implementation of "great
firewalls" just to name a few [67,78].
The corporations are also in a maddening race to dominate markets and increase profits. Once again the history shows
that ethical considerations and safeguarding values appear as mere sentimentalities when confronted with brutal business
realities. The worrisome and recurring list of violations of privacy and agency of users by corporations also range from
buying, selling and exploiting users data for various questionable purposes even to the extent of shaping the public
opinion to determine the outcome of democratic elections [79] by capitalizing on addictive social media [80].
It is needless to say that all of the above mentioned infringements will have orders of magnitude larger repercussions
once people’s remaining private space is opened up to abuse by linking the modern marvels of information technology
with ever accumulating wisdoms of neuroscience.
2.4 Mind Reading and Manipulation: The Lure
The attraction of being able to decipher other people’s thoughts and to manipulate them is not new and can be traced
back to antiquity. In the modern times, there are convincing examples of such organized attempts at least back to the
cold war era [81]. While the degree of success of these past attempts may be unknown, the resurgence of new machine
learning technologies along with the rise of neuroscience and biotechnology is poised to give a new thrust to such
ambitions [82, 83] with a potential of turning BMI into a new battle zone for the global and local powerhouses with the
laypeople caught in the crossfire.
The unsettling fact here is the high likelihood of translation of the current IT mindset to the emerging BMI sector. A
mindset with powerful contingencies and conveniences built without regard to end-users ownership and the "right to
know" as discussed previously. An honest deliberation on the extension of the current IT landscape to technologies
involving the brain portraits a dystopic picture of a future with spying daemons delivered as beautifully packaged
updates that instead of webcams, hijack eyes and thoughts or with neuro-marketing Nazis controlling and paying people
by electrical signals delivered directly to the brain reward centers. What is most alarming in these scenarios is that the
very same BMI technologies can be used to detect and snooze any thoughts of opting out of such gimmicks by the
user [84].
3 Toward a Safer Solution
In what follows, we will put forward a set of general principles that could provide a safe route for development and
commercialization of BMI products. The proposed guidelines are not in any ways claimed to be final or all-inclusive but
rather they should be construed as a starting point for further discussion and critique by those concerned with preserving
the public’s rights and values in the high-performance BMI era.
3.1 Open Design and Transparency
A direct neural interface for humans should have an open design in both software and hardware architecture that is freely
available for the scrutiny of the general public including the experts in line with Linus’s law (i.e. “given enough eyeballs,
all bugs are shallow”) [85]. The critical point here is that no single or group of entities should be unconditionally
trusted to uphold values like security, privacy, independence, agency and freedom of users. The open design principle
vaccinates the BMI services against accidental or engineered bugs. Furthermore, the open design enables organic
growth and improvement in functionality through the participation of the large community. The open ecosystem also
sustains a constant flow of creativity from the melting pot of ideas and diversity of contributions. The open design
principle addresses both the ownership and transparency issues, ultimately satisfying security in the best way possible.
Closed ecosystems are repeatedly shown to sacrifice consumer rights with a false promise of security in spite of the fact
that “security through obscurity is no security at all”.
As for the processing hardware the architecture most widely used today (x86, basically, nearly all desktop processors!)
has many hidden undocumented instruction sets (isa) [86]. Such hidden instructions pose a risk if misused in BMI
applications. Indeed many of the currently available platforms have hardware level vulnerabilities that allows attackers
to execute commands with unprivileged access below the level of applications and the operating system allowing them
to get access to user’s private data. (privilege escalation) [57, 87].
The ideal plan here would be to design a set of standard specifications (similar to IEEE 802 for networks) through
an open committee to collect and suggest standard design protocols for BMI solutions and to check commercial
products for compliance. The proposed guidelines should allow for different internal architectures (e.g. hardware based
architectures for artificial neural networks [88]) so long as they are compatible with the open design specifications. Use
of reduced instruction set processors (RISC), which are becoming more popular these days, are certainly a practical
alternative to complex instruction set computers of today. If enterprise hardware are to be used, they should be inspected
to ensure compliance and to be free from hidden isas.
On the software side, the design should also follow an open source, transparent and interpretable structure. The source
code should be reviewable by the public to check for vulnerabilities, suspicious activity or bugs. The building blocks for
a BMI system should be envisioned and plans for creating a transparent system should be devised such that users with
reasonable effort could monitor all outgoing and incoming signals from and to their nervous system. Obviously, such a
comprehensive outlook, requires serious joint effort from both neuroscience and engineering communities to create
an API that would take commands in a user friendly and reviewable format. Signals that are ready to be transmitted
from the brain or the ones arriving from outside should rely on users permission for the final transmission. Similar
to the hardware design, an open committee comprised of experts in relevant fields should be responsible for devising
approved protocols and for ensuring compliance with the open transparent standards in collaboration with the public
and expert communities.
BMI commands range from simple ones such as moving an actuator to more complex ones such as a certain neural
code that is to be transferred in a communication session. Creative solutions that maximize efficiency and convenience
for review prior to transmission may be implemented in this regard. For instance in case of motor commands, using
augmented or virtual realities before actual execution of the interpreted command can easily and quickly show the user
the consequences of an action in the environment. In the case of a neural code, decoding the pattern and showing it in
the modality that best represents that concept can help with the verification process.
3.2 Modularity and Isolation
Another important principle for protecting consumers agency and freedom is the modular design. Indeed many of
the processes that have to be done on the neural signals including denoising, filtering, analogue to digital conversion,
spectral analysis, basic pre-processing, spike sorting, etc have highly optimized and standard hardware designs that can
be implemented using special purpose modules with application specific integrated circuits (ASICs). Consequently,
custom designed hardware can be designed for each processing steps in the form of replaceable vendor-nonspecific
components. Such a modular design allows one to mix and match components according to specific requirements along
with standards and protocols to ensure compatibility between components.
The application layer which needs more complex operations and higher programmability can be implemented with
open instruction set architectures (RISC-V). The classification and decoding can be done using state of the art machine
learning techniques and neural net architectures using open source GPU like instructions that support matrix arithmetic.
The modular design also allows for parallel processing that increases throughput.
Such modular and application specific design also supports information isolation across the various modules. One may
choose to set aside separate isolated memory spaces to control what information should or should not be accessible
by other modules or applications. This is in contrast to the usual default in which applications can access information
registered under a given user globally. While the information isolation can be done in the software level, implementing
isolation at the hardware level by having physically separated memory spaces and rerouting access according to users
preferences to apps and sockets ensures much higher reliability.
The communication between components, regardless of the internal architectures, can be done through proper application
programming interfaces (APIs). Of course, the components themselves should be designed to receive and support
such APIs (it could be in their architecture, whether by being supported natively at the hardware level or via command
translation to the native software language via the interface)
3.3 Local Processing and Offline Deployability
BMI applications can be riskier when their functionality depends on data transmission and computations on the cloud.
Consider the scenario of having a chip in your brain that belongs to a closed ecosystem on which you rely for processing
neural data online to deliver services to you and your customers. While very convenient, the BMI company enacts a
new policy requiring you to increase their cut from the profit you make, otherwise you will be downgraded to their
free version which includes occasional advertisements played right into your head that you cannot skip. Terminating
your service is also made unattractive since your neurally processed information is being stored under a proprietary
format that you cannot legally use or transfer to other ecosystems. Another scenario could involve authoritative policy
makers deciding that you need a limited “cleaner” version of your BMI service by openly banning your access to the
“full” version or even worse sneak in and manipulate the computations on the cloud to their liking. Dependence on
online processing make it easier to impose functionality limitations either accidentally or intentionally by power term
dictations, thus threatening end-user agency and privacy.
In order to protect consumer rights, BMI setups should be deployable offline and should allow for local and peer to
peer configurations, rather than a client-server only format with third-party corporations. It is true that doing things
locally would be more expensive than doing them centralized somewhere remotely. However, given the fact that
there is no stringent hard limits for increasing local processing power, we believe that with reasonable investments in
financial and human resources most processing needs are addressable by appropriate local architectures in an open,
modular and offline depolyable format. Indeed given the sensitivity of the issue and what is at stake, the additional
costs and investments for development of powerful local solutions of even common BMI applications are well justified.
Furthermore, it is expected that mass production and improvements in technology can contribute to lowering the costs
by economy of scale.
3.4 Least Privilege
Since data comunication between BMI modules within and across systems cannot be zero, principle of least privilege
which in about sharing the least amount of information that is required for a particular job is proposed similar to
previous suggestions [55, 56]. Unfortunately, when it comes to exchanging data with other systems, the dominant
paradigm today involves applications freely opening sockets, accessing to data and freely communicating with the
outside world, which provides great flexibility but makes it equally potent for abuse.
There are some solutions with attempts to remove personal information and “anonymize” [56] data. Along we such
schemes we propose that the data sent over the net should be as qualitative as possible and have the least amount of
information for the exact purposes requested by the API and devoid of unnecessary details. Ultimately however, there
seems to be no method to remove all private information since one cannot predict every possible misuse of neural
data as new methods may be discovered for discrimination of features from a seemingly anonymized brain signals (or
new computational power unleashed to nullify today’s most robust anonymization and encryptions, think quantum
computing). Nevertheless, when combined with previous principles, the concept of least privilege when dealing with
neural data, should be considered to further minimize chances of misuse.
4 Closing Remarks
The current rate of progress in the know-how and technologies related to BMI, is thrusting the human society into an
unprecedented era of interconnection between man and machines. We have argued that the current state of affairs and
the IT landscape gives one many reasons to be worried about an apocalyptic future especially with commercialized high
performance BMI. We have argued that the window of opportunity for changing the course of events is rapidly closing
and have proposed a set of principles that if correctly implemented would minimize the risk to people’s agency and
privacy in the coming BMI era. However, as with any other technologies, the suggested principles are not a foolproof
guarantee against the risks and should not be construed as an alternative to the individual’s due diligence in their
interactions with BMI applications. Furthermore, openness and rejection of proprietary and closed ecosystems comes
with certain costs and some degree of uncertainty about the behavior of BMI systems which should probably should be
dealt with as the lesser of the two evils.
The key point here is that given the current state of affairs and the projection of advances in BMI technologies, we may
only be a few years away from the "point of no return". That is unless a wholesome and forward looking alternate
solution is devised and implemented soon, the current tenets in the IT sector will naturally swamps the consumer BMI
enterprise resulting in an ecosystem for which patches and ad-hoc fixes could no longer restore long-lost end-user
rights and ethical values. Note that the pressure caused by mass migration of middle-class to BMI solutions and the
apparent ease and enrichment they bring to daily life will put non-adopters at great disadvantage and weakened and
thus irrelevant in the long run.
Obviously, our proposed guidelines do not preclude a person from using a BMI solution to harm himself or use it in
addictive ways for instance by directly stimulating reward centers in his brain. From an authoritative perspective, the
potential for such misuse, justifies a closed and controlled ecosystem with only limited people or entities having access
to know or modify what is behind the curtain. While this argument may seem sound in theory, in reality the most
frequent and dangerous abuses are often conducted by the same authoritative entities that are entrusted with ensuring
safety, privacy and agency of the people. On the other hand, transparency allows the community to detect violations of
protocols and openness allows them to fight back the abusers (without claims to a silver bullet [89]). Thus the problem
of dealing with misguided decisions of people with regards to BMI are not specific to this technology and should be
countered by agreed upon checks and balances enacted by the general public.
Furthermore, the principle of local deployability will certainly have to deal with the challenge of mass migration toward
cloud computing in terms of price tag and processing power. We would like to argue that given the sensitivity of
the subject matter and what is at stake a more costly yet a safer alternative should be the preferred route of action
similar to the reasoning behind choosing more expensive alternatives for high risk medical treatments. Furthermore, it
is very likely that with enough demand for local deployability, future advances in storage and processing power can
make offline implementation of most common BMI applications feasible. On the other hand, it is conceivable that
given limited resources and promoted by the convenience of current proprietary and closed ecosystems among other
practical reasons, open BMI solutions become a fringe but nevertheless a viable alternative to the established and widely
supported enterprise solutions
We argue that despite some skepticism [90] the upcoming BMI era is a new turning point in human history. The marriage
of brains and machines might as well be the true realization of the long imagined artificial intelligence singularity [91].
Indeed, the emergence of super-intelligence [92] may not be of a pure human or artificial intelligence form per se
but of a hybrid between humans and machines. Unlike previous technological revolutions however this one is about
conquering the innermost citadel of our individuality, our brains and minds. As of to-date there are no strong alternatives
that can guarantee the public agency and privacy in the BMI era. This shall prompt all who care about preserving the
free societies to get actively engaged in proposing safe protocols and creating BMI solutions that respect human rights
and abide by internationally accepted ethical standards.
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Full-text available
Background Implantable brain–computer interfaces (BCIs), functioning as motor neuroprostheses, have the potential to restore voluntary motor impulses to control digital devices and improve functional independence in patients with severe paralysis due to brain, spinal cord, peripheral nerve or muscle dysfunction. However, reports to date have had limited clinical translation. Methods Two participants with amyotrophic lateral sclerosis (ALS) underwent implant in a single-arm, open-label, prospective, early feasibility study. Using a minimally invasive neurointervention procedure, a novel endovascular Stentrode BCI was implanted in the superior sagittal sinus adjacent to primary motor cortex. The participants undertook machine-learning-assisted training to use wirelessly transmitted electrocorticography signal associated with attempted movements to control multiple mouse-click actions, including zoom and left-click. Used in combination with an eye-tracker for cursor navigation, participants achieved Windows 10 operating system control to conduct instrumental activities of daily living (IADL) tasks. Results Unsupervised home use commenced from day 86 onwards for participant 1, and day 71 for participant 2. Participant 1 achieved a typing task average click selection accuracy of 92.63% (100.00%, 87.50%–100.00%) (trial mean (median, Q1–Q3)) at a rate of 13.81 (13.44, 10.96–16.09) correct characters per minute (CCPM) with predictive text disabled. Participant 2 achieved an average click selection accuracy of 93.18% (100.00%, 88.19%–100.00%) at 20.10 (17.73, 12.27–26.50) CCPM. Completion of IADL tasks including text messaging, online shopping and managing finances independently was demonstrated in both participants. Conclusion We describe the first-in-human experience of a minimally invasive, fully implanted, wireless, ambulatory motor neuroprosthesis using an endovascular stent-electrode array to transmit electrocorticography signals from the motor cortex for multiple command control of digital devices in two participants with flaccid upper limb paralysis.
Full-text available
Brain-Computer Interface (BCI) technology is a promising research area in many domains. Brain activity can be interpreted through both invasive and non-invasive monitoring devices, allowing for novel, therapeutic solutions for individuals with disabilities and for other non-medical applications. However, a number of ethical issues have been identified from the use of BCI technology. In this paper, we review the academic discussion of the ethical implications of BCI technology in the last five years. We conclude that some emerging applications of BCI technology-including commercial ventures that seek to meld human intelligence with AI-present new and unique ethical concerns. Further, we seek to understand how academic literature on the topic of BCIs addresses these novel concerns. Similar to prior work, we use a limited sample to identify trends and areas of concern or debate among researchers and ethicists. From our analysis, we identify two key areas of BCI ethics that warrant further research: the physical and psychological effects of BCI technology. Additionally, questions of BCI policy have not yet become a frequent point of discussion in the relevant literature on BCI ethics, and we argue this should be addressed in future work. We provide guiding questions that will help ethicists and policy makers grapple with the most important issues associated with BCI technology.
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Background: Emerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro experiments, more and more attention has been paid to the development of efficient and feasible computational methods to predict the potential associations between miRNA and disease. Methods: In this work, we present a machine learning-based model called MLMDA for predicting the association of miRNAs and diseases. More specifically, we first use the k-mer sparse matrix to extract miRNA sequence information, and combine it with miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity information. Then, more representative features are extracted from them through deep auto-encoder neural network (AE). Finally, the random forest classifier is used to effectively predict potential miRNA-disease associations. Results: The experimental results show that the MLMDA model achieves promising performance under fivefold cross validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. In addition, to further evaluate the prediction performance of MLMDA model, case studies are carried out with three Human complex diseases including Lymphoma, Lung Neoplasm, and Esophageal Neoplasms. As a result, 39, 37 and 36 out of the top 40 predicted miRNAs are confirmed by other miRNA-disease association databases. Conclusions: These prominent experimental results suggest that the MLMDA model could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates. The source code and datasets explored in this work are available at .
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We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as “Senders” whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the “Receiver,” who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects’ decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainNet allows Receivers to learn to trust the Sender who is more reliable, in this case, based solely on the information transmitted directly to their brains. Our results point the way to future brain-to-brain interfaces that enable cooperative problem solving by humans using a “social network” of connected brains.
Full-text available
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Brain-machine interfaces (BMIs) hold promise for the restoration of sensory and motor function and the treatment of neurological disorders, but clinical BMIs have not yet been widely adopted, in part because modest channel counts have limited their potential. In this white paper, we describe Neu-ralink's first steps toward a scalable high-bandwidth BMI system. We have built arrays of small and flexible electrode "threads", with as many as 3,072 electrodes per array distributed across 96 threads. We have also built a neurosurgical robot capable of inserting six threads (192 electrodes) per minute. Each thread can be individually inserted into the brain with micron precision for avoidance of surface vasculature and targeting specific brain regions. The electrode array is packaged into a small implantable device that contains custom chips for low-power on-board amplification and digitiza-tion: the package for 3,072 channels occupies less than (23 × 18.5 × 2) mm 3. A single USB-C cable provides full-bandwidth data streaming from the device, recording from all channels simultaneously. This system has achieved a spiking yield of up to 85.5 % in chronically implanted electrodes. Neu-ralink's approach to BMI has unprecedented packaging density and scalability in a clinically relevant package.
Greater safeguards are needed to address the personal safety, security and privacy risks arising from increasing adoption of neurotechnology in the consumer realm.