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ORIGINAL PAPER
Hacking the brain: brain–computer interfacing technology
and the ethics of neurosecurity
Marcello Ienca
1
•Pim Haselager
2
Springer Science+Business Media Dordrecht 2016
Abstract Brain–computer interfacing technologies are
used as assistive technologies for patients as well as
healthy subjects to control devices solely by brain activity.
Yet the risks associated with the misuse of these tech-
nologies remain largely unexplored. Recent findings have
shown that BCIs are potentially vulnerable to cybercrim-
inality. This opens the prospect of ‘‘neurocrime’’:
extending the range of computer-crime to neural devices.
This paper explores a type of neurocrime that we call
brain-hacking as it aims at the illicit access to and
manipulation of neural information and computation. As
neural computation underlies cognition, behavior and our
self-determination as persons, a careful analysis of the
emerging risks of malicious brain-hacking is paramount,
and ethical safeguards against these risks should be con-
sidered early in design and regulation. This contribution is
aimed at raising awareness of the emerging risk of mali-
cious brain-hacking and takes a first step in developing an
ethical and legal reflection on those risks.
Keywords Brain–computer interfacing Neurosecurity
Privacy Neurocrime Brain-hacking Autonomy
Agency
It is always too early to assess a technology, until suddenly it is too late.
Martin Buxton (Buxton 1987).
Introduction
The term brain-hacking refers to the emerging possibility of
coopting brain–computer interfaces (BCI) and other neural
engineering devices with the purpose of accessing or
manipulating neural information from the brain of users.
This paper offers an overview of the possible sorts of brain-
hacking to which BCIs are or may become subject in the
near future and provides an inventory of the specific ethical
implications of brain-hacking. We will proceed as follows:
first, we will discuss the main features of computer crime.
Second, we will discuss the main features of neurocrime
and brain-hacking. Third, we will offer a brief description
of the BCI cycle. Fourth, we will identify what specific
types of brain-hacking can occur at each phase of the cycle.
Finally, we will delineate the major ethical implications
emerging out of the phenomenon of brain-hacking.
Although the ethical concerns we discuss in relation to
brain-hacking may be found in relation to other technolo-
gies as well, we suggest that their particular combination
with respect to BCI warrants a separate discussion, espe-
cially given the current and to be expected progress in BCI
research and applications. Therefore, our aim is to provide
a systematic treatment of the various ways of brain-hacking
in relation to the different components of BCI. This con-
tribution is aimed at promoting a public debate over the
potential threats to neurosecurity related to the potentially
widespread availability of BCIs among the general public,
and takes a first step in developing a systematic ethical and
legal reflection on brain-hacking. Future research is
required to extend this analysis and to develop a compre-
hensive ethical, legal and regulatory framework.
&Marcello Ienca
marcello.ienca@unibas.ch
Pim Haselager
w.haselager@donders.ru.nl
1
Institute for Biomedical Ethics, University of Basel,
Bernoullistrasse 28, 4056 Basel, Switzerland
2
Donders Institute for Brain, Cognition and Behaviour,
Radboud University Nijmegen, B.01.13 Spinoza Building,
Montessorilaan 3, 6525 HR Nijmegen, The Netherlands
123
Ethics Inf Technol
DOI 10.1007/s10676-016-9398-9
Computer crime
The number and quality of human activities enabled or
mediated by computers is increasing rapidly. Emerging
trends in information and computer technology such as big
data, ubiquitous computing, and the Internet of Things are
accelerating the expansion of computer use in our societies.
Today, computers are used to perform or facilitate an
enormous variety of tasks and activities of daily living
including, but not restricted to, banking, trading, schedul-
ing and organizing events, learning, entertaining, gaming
and communicating. Computer use does not restrict solely
to the social and economic domain. Several activities that
are considered inherent to our psychological and biological
dimension are now supported or facilitated by computing.
Examples include the use of GPS systems in geolocation
and spatial navigation, the use of wearables in monitoring
bodily processes such as calories intake, heart beat rate,
and weight loss, and the use of personal computers in
performing cognitive tasks such as arithmetic calculus,
writing, and memory.
As the uses of computers in human life have increased
both in volume and in richness, the security threats to
computing have also increased significantly. Notoriously,
computer and information technologies can be used by
actors for nefarious purposes such as cracking, fraud,
identity theft, financial theft, and information warfare. The
broad range of criminal activities that result from misusing
computers and networks is referred to as cybercrime.
Halder and Jaishankar (2011) define cybercrime as: ‘‘Of-
fenses that are committed against individuals or groups of
individuals with a criminal motive to intentionally harm the
reputation of the victim or cause physical or mental harm
to the victim directly or indirectly, using modern
telecommunication networks’’ (Halder and Jaishankar
2011). Originally, cybercriminal activities were restricted
to personal computers and related computer networks.
With the dramatic expansion of the digital ecosystem many
new opportunities for malicious exploitation should be
expected. It is predicted that the current number of devices
connected to the Internet will increase from 9 billion in
2011 to 50 billion in 2020, generating a flow of 50 trillion
GBs of data (Evans 2011). Devices such as watches, TVs,
eye-wears, home-appliances, automobiles and medical
devices are increasingly becoming sources of computa-
tional information and will irrigate the digital ecosystem
with an unprecedented quantity of data flows and at an
unprecedented velocity. This will also multiply the quan-
tity of data and the number and type of devices that are
potentially exposed to cybercriminality.
Many of the technologies responsible for this dramatic
expansion of the digital ecosystem fit in the category of
disruptive technologies as they make a lasting change to
the technological landscape. Although disruptive tech-
nologies are designed to positively impact individuals and
society, their technological novelty also opens ‘breaches’
for criminals. These breaches, as Dupont points out, are
often ‘‘the result of a defective legal or regulatory coverage
and provoke rapid increases in offenses’’ (Dupont 2013). In
fact, regulation upgrade occurs at a much slower rate than
technology upgrade and present security regulations are
often incapable to effectively account for the accelerating
changes generated by technology in human activities and
infrastructures.
In this rapidly changing context, the goals of computer
security, namely the protection of the confidentiality,
integrity, and availability of information become more
difficult to achieve (Denning et al. 2009). This increased
difficulty does not arise exclusively from the quantity and
velocity of data. Rather, the quality of information intro-
duced into the data flow is crucial too. The more pervasive
computing technology becomes, the more intricately it is
interwoven into the everyday life. While this has the sig-
nificant benefit of minimizing interaction friction between
humans and machines, hence making computer-use
effortless and more personalized, it also multiplies the
classes of information that become accessible, hence
potentially exposed to cybercriminal risks. Among these
classes of information, biological information is critical.
1
Medical computer technologies such as artificial cardiac
pacemakers as well consumer-grade technologies such as
wearable heart rate monitors are designed with the purpose
of accessing and processing biological information –in this
case, information about the beating of the heart. As the use
of bioengineering devices is rapidly increasing, the amount
of biological information irrigating the digital ecosystem
will increase as a consequence. This raises the issue of
privacy and information security, as biological information
is carrier of private and sensitive data whose access or
manipulation by malicious actors may cause significant
physical (including life-threatening), psychological or
social harm to technology users. An example of this
emerging risk was provided by Halperin et al. (2008) who
experimentally demonstrated that a hacker could wirelessly
compromise the security and privacy of an already com-
mercialized implantable cardiac defibrillator. In their
1
The notion of biological information is used in this paper to
extensively refer to information expressed in the processes charac-
teristic of living organisms at various levels, i.e. at the levels of
molecules, cells, organs, circuits etc. This definition is in accordance
with the statistical definition of information formulated by Claude
Shannon and used in mathematical information theory (Shannon
1949). In Shannon’s sense, ‘‘anything is a source of information if it
has a range of possible states, and one variable carries information
about another to the extent that their states are physically correlated’’.
For a comprehensive understanding of the notion of biological
information see: (Godfrey-Smith and Sterelny 2007).
M. Ienca, P. Haselager
123
experiment, hackers could use homemade and low-cost
equipment to change a patient‘s therapies, disable therapies
altogether, and induce potentially fatal processes such as
ventricular fibrillation (Halperin et al. 2008).
Neurocrime
The problems of technology misuse and security of bio-
logical information are particularly critical in the context of
neurotechnology as this type of technology applies (either
directly or indirectly) to a very important organ in the
human body, the brain. The brain not only contributes
significantly to life-maintaining processes (such as nutri-
tion and respiration) but also to faculties such as con-
sciousness, perception, thinking, judgment, memory and
language and is of great importance to our behavior and our
self-identification as sentient-beings or persons. Therefore,
misusing neural devices for cybercriminal purposes may
not only threaten the physical security of the users but also
influence their behavior and alter their self-identification as
persons. We call the realm of cybercriminal activities
enabled by the misuse of neural devices neurocrime.
It is worth noting that neurocrime does not necessarily
involve direct access to the brain and to brain information.
Rather, neurocriminal activities are most likely to occur, at
present, in a manner that affects the brain only indirectly,
for example by limiting, modifying or disrupting function
in the devices that interface brain computation. This type of
risk is already critical at the current level of deployment of
neural engineering technologies. With neurally controlled
devices (e.g. brain stimulators and brain–computer inter-
faces) being available as medical technologies as well as
commercialized products, present neurocriminals may
abuse of the users by disrupting or terminating function in
their devices without the users’ permission or consent. For
example, neurally controlled robotic limbs used to com-
pensate for the motor deficits of amputated patients are
potentially vulnerable to being mechanically destroyed by
malicious actors, which would deprive the users of their re-
acquired motor abilities. This type of neurocrime affects
the brain only indirectly since the users’ neural computa-
tion is not directly accessed or manipulated in any signif-
icant sense during the attack. Nonetheless, criminal
activities of this type may affect significantly the mental
life of the victims, because these activities can limit and
constrain their behavior, generate emotional responses such
as panic, fear, and psychological distress, and leave trau-
matic memories. In the light of this and in accordance with
the previously reported definition of computer crime, we
define the emerging phenomenon of neurocrime as offenses
against individuals or groups of individuals with a criminal
motive to intentionally cause direct or indirect physical and
mental harm to the victim as well as harm to the victim’s
reputation and property by accessing or manipulating
neural information through the use of neural devices. It is
worth noting that, under some circumstances, the attacker
and the target of the attack may be the same person. For
example, mentally unstable users of prosthetic limbs may
choose to damage their devices in an attempt to perform
self-imposed harm.
From the perspective of neurocrime two types of neural
devices are particularly critical at present: brain stimula-
tors—especially deep brain stimulation (DBS) and tran-
scranial direct-current stimulators (tDCS)
2
—on the one
hand, and brain–computer interfaces (BCIs) on the other
hand. The reason for that stems from three basic facts
common to both types of neural device: (1) they potentially
enable direct access to neural computation, although in
diametrically opposite ways—brain stimulation versus
reading of brain activity; (2) their use is widespread as they
are both available not exclusively as medical technologies
but also as commercialized products for healthy users (3)
they have the potential to generate safety and security
concerns.
3
Being the only type of neural devices whose
hackability has been proven in experimental and real-life
settings, BCIs will be the only neural technology at stake in
this paper. Further research is required to explore the
specific neurocriminal risks associated with DBS, tDCS
and other forms of neurostimulation.
Brain–computer interfacing
In contrast to neurostimulators, brain–computer interfaces
are not used to stimulate the brain but establish a direct
communication pathway that allows BCI-users to control
an external computer device exclusively with brain activ-
ity, bypassing the peripheral nervous and muscle systems
(Vallabhaneni et al. 2005). BCIs originally developed in
2
Deep brain stimulation (DBS) is an invasive neurostimulation
technique which involves the neurosurgical implantation of a medical
device into the brain. This implanted device sends electrical signals
into targeted subcortical areas with the aim of eliciting activity. DBS
is an increasingly used therapy for several neurological conditions
such as Parkinson’s disease, dystonias, essential tremor, and chronic
pain syndromes when patients are not responding to less invasive
approaches (Tronnier and Rasche 2015).
Transcranial direct current stimulation is a neuromodulatory
intervention which uses constant, low electrical current delivered to
the cortical area of interest via small electrodes placed on the skull
with the aim of changing neuronal excitability in that area (Brunoni
et al. 2012). This change of neuronal excitability may influence, and
in certain cases enhance cognitive performance for a brief period of
time on a number of different cognitive tasks.
3
See, for example, the following two magazine reviews: (Conner
2010; Strickland 2014). Although concerns expressed by popular
media may at times be exaggerated, they still may require appropriate
responses by scientists and ethicists, if only to diminish or forestall
unrealistic worries amongst the general public.
Hacking the brain: brain–computer interfacing technology and the ethics of neurosecurity
123
clinical medicine as a therapeutic or assistive technology
for neurological patients. In clinical settings, BCI-appli-
cations are directed at repairing, assisting or augmenting
cognitive or sensory-motor functions in patients experi-
encing cognitive or sensory-motor impairments including
spinal cord injury, stroke, and motor neuron disease such as
amyotrophic lateral sclerosis (ALS) and muscular dystro-
phy (Allison et al. 2007; Vallabhaneni et al. 2005). For
example, BCI-based motor prostheses have successfully
been trialed in animal models and patients to enable direct
brain control on artificial limbs, wheelchairs and other
devices (Fetz 2015). To date, BCI-applications are avail-
able not only within clinical settings but also to the general
public. Several commercial applications of EEG-based BCI
devices have made their way onto the market and are
becoming increasingly popular among healthy individuals
for gaming and supporting everyday activities. For exam-
ple, companies Emotiv (http://emotiv.com/) and Neurosky
(http://www.neurosky.com) have pioneered the commer-
cialization of consumer-grade non-invasive and easy-to-
wear BCIs for gaming, interactive television, or as hands-
free control systems. The electronic telecommunication
industry is providing consumer-grade BCIs that are avail-
able for potential mass adoption. For instance, iPhone
accessories such as Xwaveallow the headset to plug
directly into compliant iPhones and read brainwaves.
Meanwhile, prototypes of next-generation Samsung Galaxy
Tabs and other mobile or wearable devices have being
tested to be controlled by brain activity via EEG-based BCI
(Powell et al. 2013). In addition, neuromarketing compa-
nies such as Nielsen (http://www.nielsen.com/) are using
BCI-applications to better assess customer needs and
preferences.
4
Given the significant potential benefits of
brain control in computing—e.g. immediacy, hands-free
control, portability etc.—Yuan and colleagues predict that
BCIs will gradually replace the keyboard, the touch screen,
the mouse and the voice command device as humans’
preferred ways to interact with computers (Yuan et al.
2010). Finally, a number of military and warfare BCI-ap-
plications are currently in development. The US Defense
Advanced Research Projects Agency (DARPA) is currently
funding a broad spectrum of BCI projects with two major
purposes: (1) restoring neural and/or behavioral function in
warfighters, and (2) enhancing training and performance in
warfighters and intelligence agents (Kotchetkov et al. 2010;
Miranda et al. 2015). For example, the Neurotechnology
for Intelligence Analysts (NIA) has developed BCI systems
utilizing non-invasively recorded EEG to significantly
increase the efficiency and throughput of imagery analysis
(Miranda et al. 2015).
While the potential benefits and predicted distribution of
clinical and non-clinical applications of BCI technology
are significant, the neurosecurity risks associated with the
widespread availability of this technology remain largely
unexplored.
From neurocrime to brain-hacking
Denning et al. (2009) provide prototype-examples of neu-
rocrime. These include the wireless hijacking of a prosthetic
limb, the malicious re-programming of neurostimulation
therapy (e.g. the wireless alteration of the device settings to
generate unsafe brain stimulation) and the eavesdropping of
a brain implant’s signals to reveal private information.
These examples describe very specific neurocriminal phe-
nomena where the attack is not simply directed at disrupting
the neural device but at getting direct access to brain
information. Neurocriminal activities of this type appear
more specific than general neurocrime as (1) can only be
performed on neural devices that establish a direct con-
nection pathway with the brain such as tDCS, neural
implants and BCI, (2) involve the direct access to and
manipulation of neural information, (3) influence directly
neural computation in the users. We call this special type of
neurocrime malicious ‘‘brain-hacking’’ as it exploits the
neural device to get illicit access to and eventually manip-
ulate brain information in a manner that resembles how
computers are hacked in computer crime. As in general
neurocrime, also in brain-hacking the attacker and the target
of the attack may be the same person. For example, a user
may hack his or her own neurostimulation device to self-
prescribe elevated moods or increase activation of reward
centers in his or her brain (Denning et al. 2009).
Li et al. (2015) have provided an inventory of possible
malicious brain-hacking activities based on the type of BCI
application. They distinguish four types of BCI applica-
tions: (1) neuromedical applications, (2) user authentica-
tion, (3) gaming and entertainment, and (4) smartphone-
based application (Li et al. 2015). For each of these appli-
cation families they presented the current attack scenario
and suggested possible countermeasures. Some forms of
brain-hacking have already proven to be actually feasible in
experimental as well as in real-life settings. Rosenfeld et al.
(2006) have shown that brain–computer interfaces can be
coopted to detect concealed autobiographical information
from users with a significantly high accuracy rate (Rosen-
feld 2011). More strikingly, Martinovic et al. (2012) have
successfully used brain–computer interfaces to reveal pri-
vate and sensitive information about the users such as their
pin codes, bank membership, months of birth, debit card
numbers, home location and faces of known persons
(Martinovic et al. 2012). We will discuss these possibilities
in more detail below in the ‘‘Input manipulation’’ section.
4
http://www.nielsen.com/us/en.html (last accessed May 3, 2015).
M. Ienca, P. Haselager
123
A sci-fi future where people can access and manipulate
information in other people’s brains is approaching and
their prodromes are already here. Therefore, unless
appropriate safeguards are considered early in the design of
the neural devices that will be deployed in the next future
(5–20 years), concerns of malicious misuse in the form of
brain-hacking could become paramount for public safety.
The BCI cycle
BCIs can be distinguished into two types: invasive and
non-invasive. Invasive BCIs record brain signaling via
surgical implantation of electrode arrays in or directly
connected to the central nervous system. Non-invasive
BCIs interface brain signaling via neuroimaging tech-
nologies such as electroencephalography (EEG) and elec-
tromyography (EMG) that record brain activity through
electrodes placed on the outside of the skull. As said pre-
viously, both invasive and non-invasive BCIs establish a
direct interaction between the user’s brain and a computer
device. This interaction is usually described as a 4-phase
cycle (van Gerven et al. 2009). See Fig. 1.
The first phase concerns the input, i.e. the generation of
specific brain activity by the user in response to a stimulus.
This brain activity is generated when the BCI-user is in a
certain cognitive state or performs a mental task. For
example, when a BCI user is controlling a wheelchair a
matrix of possible itinerary choices is presented on the
interface that the user is watching. A frequent brain acti-
vation pattern used in BCI are the so-called event-related
potentials (ERPs), i.e. measured brain responses that are
the direct result of a specific sensory, cognitive, or motor
event. Among these ERPs, increasing interest is sur-
rounding the P300 wave, an ERP component usually eli-
cited in the process of decision making (Fazel-Rezai et al.
2012). In our example, when the desired itinerary is pre-
sented (e.g. by highlighting or ‘flashing’ it) at the interface,
the user’s brain signals will contain a P300 signal that can
be picked up by the BCI.
The second phase concerns the measurement and
recording of brain activity. At this stage, patterns of brain
activity in the user’s brain are detected and measured by
the interface during a cognitive process or the performance
of a mental task. For example, when a certain itinerary
option upon which the BCI-user is focusing is flashed (say,
a specific end-location, or an instruction to turn left), the
BCI can detect the P300 wave elicited at that moment. The
measurement can be implemented in several ways
according to the type of BCI in use. The most frequent type
of BCI is based on electroencephalogram (EEG); other
measurement options include magnetoencephalography
(MEG), and functional magnetic resonance imaging
(fMRI).
In order to be usable for the BCI and generate appro-
priate outputs (i.e. those expected by the user), the raw data
measured in the second phase should be decoded into its
main features and classified. This decoding and classifying
process typically occurs in the third phase of the BCI cycle.
In this phase, data are processed in order to ‘clean’ the
brain signals, namely to increase the signal-to-noise ratio
Fig. 1 The BCI cycle Source: Figure adopted, with permission, from J. Farquhar/Braingain
Hacking the brain: brain–computer interfacing technology and the ethics of neurosecurity
123
(i.e. a measure of strength of the desired signal relative to
background noise) and to filter the most relevant aspects of
each signal for further processing. This processing is nec-
essary to extract the relevant features from the signal and
distinguish them from non-relevant features, especially
from the background noise due to the underlying brain
activity that is not directed at the execution of that specific
mental task (in our example, the activity that is not directed
at moving the wheelchair, e.g. processes involved in color
perception).
Once the signals are decoded, they can be translated into
output. The output is usually the performance of the action
initially intended or desired by, or deemed beneficial for,
the user through the control of the applications interfaced
by the BCI (in our example, turning left with the wheel-
chair). Controllable applications include motor devices
(e.g. wheelchairs and robotic limbs), sensor devices as well
as several software and hardware applications (including
apps for smartphones). Once each cycle is completed the
user can perceive the feedback resulting from the previous
cycle (e.g. notices the wheelchair turning left) and the next
cycle can start.
Brain-hacking
Brain-hacking can in principle occur at each of the dif-
ferent phases of the BCI-cycle. In the following, we will
provide an overview of the sorts of brain-hacking to which
BCIs are subject at present or may be subject in the near
future according to the phase of the BCI-cycle at which the
attacks may occur. For each sort of attack it will discuss the
corresponding criminal activities that can be committed
and the type of moral values and norms that are at stake.
Input manipulation
Brain-hacking via input manipulation occurs when the
hacker attacks the BCI user at the moment of providing
input, i.e. at the first phase of the BCI cycle.
5
Input
information can be manipulated by altering the stimuli
presented to the user. For example, brain-hackers may
preselect target stimuli to elicit specific responses in the
user that facilitate the access the user’s neural information.
This type of hack has been proven to be actually feasible by
recent research in computer security and human–computer
interaction. For example, Rosenfeld et al. (2006) have
developed a P300-based protocol to detect concealed
autobiographical information from users with a signifi-
cantly high accuracy rate (Rosenfeld et al. 2006). Van Vliet
et al. (2010) have used the N400 component of ERP to
detect what a BCI user is ‘thinking about’ without using
explicit stimuli (van Vliet et al. 2010).
6
Particularly strik-
ing are the results by Martinovic et al. (2012). In this study,
researchers presented EEG BCI-users with six classes of
stimuli: (1) PIN code digits, (2) photos related to banks, (3)
names of the months, (4) debit card digits, (5) locations,
and (6) faces. For each class, one target stimulus (i.e.
stimulus eliciting sensitive information known to the user)
was inserted in the randomly permuted sequence of non-
target stimuli (Martinovic et al. 2012). For example, in the
bank experiment, the target stimulus was the picture of an
ATM machine from the user’s bank whereas the non-target
stimuli were a series of pictures of ATMs from other banks.
The goal of the study was to detect a P300 signal in
response to private and sensitive information about the
users (their pin codes, bank membership, month of birth,
debit card numbers, home location and faces of known
persons) and extract that information. Since this informa-
tion is usable for monetary transactions, home banking and
log-into private on-line accounts, extracting this informa-
tion may enable hackers to perform offenses against BCI
users. The results show that this sort of input-manipulation
can turn the BCI against users in order to reveal some
private information with a significant chance of success: in
fact, the Shannon entropy of the private information was
decreased on the average by approximately 15–40 %
compared to random guessing attacks
7
(Martinovic et al.
2012, p. 1). Such sorts of malware resemble the function of
computer spyware as they aid in gathering information
about a user, in sending it to another entity, or in asserting
control over a computer or computer-driven device without
the user’s permission or consent. Unlike common spyware,
however, the malware involved in brain-hacking extracts
information directly from brain signaling, hence the name
‘brain-spyware’. The potential set of applications of brain-
spyware in future cybercrime is large and may involve
several criminal activities such as password cracking,
identity theft, phishing and fraud.
5
It is worth noting that there are two potential meanings of input
here: (1) the user provides input to the BCI through brain activity; (2)
the interface provides information (e.g. a screen with commands) to
the user. To disambiguate, in this section we will refer exclusively to
the latter as this type of input is the only one whose hackability was
proven in the experimental setting.
6
The ambiguous term ‘thinking about’ is defined by the authors as
‘being primed on‘. Since the priming effect occurs for many types of
stimuli (e.g. words, sounds, and images) the authors assumed that a
subject can prime himself by being told to think about an object. See
van Vliet et al. (2010, p. 183).
7
In order to quantify the information leak that the BCI attack
provides, the researchers compared the Shannon entropies of guessing
the correct answers for the classifiers against the entropy of the
random guess attack. The entropy difference directly measures the
information leaked by an attack; see Martinovic et al. (2012, p. 11).
M. Ienca, P. Haselager
123
An additional breach for brain-hacking via input-ma-
nipulation is authentication via EEG signal. Li et al. (2015)
have reported an attack model by impersonating the
thoughts of subjects using EEG generative model based on
the historical EEG data from a subject (Li et al. 2015).
It is worth to point out that at the current level of
development of BCI technology three major technical lim-
itations prevent the diffusion of brain-hacking cases outside
the clinical setting: (1) measurement accuracy, (2) pro-
cessing speed, and (3) distribution. As we have seen in the
previous chapter, in the decoding phase of the BCI cycle
data must be processed in order to increase the signal-to-
noise ratio and segregate relevant from non-relevant infor-
mation. For today’s hackers, decoding brain signals with a
level of accuracy and at a speed comparable to cracking
computer codes is still impossible outside the experimental
settings. This is exacerbated, by the limited commercial
distribution of portable BCI-applications. Given the limited
readability of brain signals and the current level of maturity
of the market, for today’s hackers the reward may not be
worth the risk. However, as technology advances and the
BCI-market rapidly expands, brain data will reveal more
and more and their level of readability will rapidly increase.
Measurement manipulation
Brain-hacking can also occur when the hacker attacks the
BCI user during the phase of the measurement in order to
generate—without the user’s permission—outputs that are
different from those expected to be generated by regular
processing. Attacks of this type may differ with regard to
their purpose. Three main criminal purposes are foresee-
able: cracking the BCI’s raw data, disrupting BCI’s func-
tion, and hijacking the BCI. A real-life protoexample of
BCI-cracking is the so-called Cody’s Emokit project,
developed by the hacker Cody Brocious. Brocious cracked
the encryption of a consumer-grade BCI produced by
Emotiv (called EPOC) and built a decryption routine.
Subsequently, he created an open-source library for reading
encrypted data directly from the headset, and posted about
his project on the Emotiv user forum. As Conner explains:
‘‘his library of code hacks to the device just pulls raw data
from the unit; there’s no ability to filter the signals or tell
which sensor corresponds to each data stream’’ (Conner
2010). It is worth to highlight that Brocious’ hack had no
malicious motive. In contrast, it was designed to open
EPOC’s source code and open up the device to development
(hence with the ethical purpose of accelerating secure new
products and research). However, malevolent agents can use
similar strategies to illicitly crack information as a form of
dual-use.
Attacks by BCI disruption may occur when the hacker
aims at manipulating the measuring process in order to
confuse, sabotage or delay the function of the BCI appli-
cation. The function of a BCI can be disrupted, at the level
of measurement, by adding noise to make the measurement
inaccurate. Hijacking may occur when the hacker tries to
monitor and alter the BCI communication channel with the
purpose of diminishing or even replacing the user’s control
of the BCI application. During hijacking the system is
given other commands than those intended or desired by
the user, for the benefit of the hacker. Brain-hackers could
manipulate the measurement by adding noise in order to
diminish or eliminate control of the user over the BCI
application. For example, a frustrated caregiver could
hijack a BCI-enabled speech production device to silence a
cognitively impaired user or a wheelchair to force the user
to follow a certain itinerary. More generally, measurement
manipulation by hijacking may result in several criminal
activities aimed at limiting, harming or taking advantage of
the BCI-users’ behavior.
Decoding and classifying manipulation
Brain-hacking at the level of decoding and classification is
also aimed at generating outputs that are different from
those intended or desired by the user, and expected to be
generated by regular processing. This criminal goal may be
achieved in three ways: (1) by adding noise to simply make
the decoding process unduly difficult; (2) by intervening
with the machine learning component (so strictly speaking,
moving to the feature classification phase) in order to
manipulate the classification of the brain signal; or (3)
overriding the signal sent by the BCI to the output device.
8
Each of these hacking strategies will have peculiar pros and
cons. For example, the noise-adding hack will have the
advantages (from the perspective of malicious actors) of
being more easily performable and less easily
detectable than the other two but it will also make it more
difficult for the hackers to have the BCI application do
what they want. In contrast, the other two hacking strate-
gies will be more difficult to perform and more easily
detectable than the former but they will, in principle,
enable the hackers to have more control over the BCI
system. Similarly as in measurement-manipulation, brain-
hackers can intervene at the level of decoding and classi-
fication with the criminal motive of hijacking the BCI-
application. The peculiarity of attacks at this phase of the
cycle, however, is that the hijacking may not be simply
aimed at diminishing or expunging the control of the user
over the application, but also at replacing control. Brain-
8
It is worth noting that the first strategy (adding noise) is similar to
the one discussed in ‘‘Measurement manipulation’’section with regard
to measurement manipulation. However, at this level, the conse-
quence we discuss may be different as the aim of the intervention here
is to delay or complicate the decoding process.
Hacking the brain: brain–computer interfacing technology and the ethics of neurosecurity
123
hackers may try to monitor and alter or inject messages into
the BCI communication channel with the purpose of
replacing the user’s control of the BCI application. During
hijacking, the system is given other commands than those
intended or desired by the user, for the benefit of the
hacker. Successful hijacking will result in the hacker
having partial or full control over the BCI application and
the BCI user having diminished or no control on the
application. This would expose the user to perils directly or
indirectly induced by the hijacker. For example, a criminal
actor could hijack the BCI-controlled smartphone of a BCI-
user without the user’s permission to extort payments,
erase sensitive information or communicate with third
parties by masquerading his or her identity under the
identity of the user (hence performing offences including
fraud, theft and identity theft). In addition, as we have seen
before, hijacking strategies could also become sources of
threat to the personal safety of third parties, as the hijacked
device could harm third parties either accidentally or as an
explicit command of the hijacker.
Although no confirmed real-life or experimental reports
of hacking via measurement or decoding and classifying
manipulation are available at present, these types of brain-
hacking deserve particular monitoring in the context of
security, surveillance and public health. The reason for that
stems from the fact that their potential nefarious outcomes
are not exclusively restricted to actions involving the
access to sensitive information (e.g. identity theft and
fraud) but extend to more detrimental activities involving
the physical and psychological harm of the users.
Feedback manipulation
Brain-hacking by feedback manipulation would occur
when the hack is aimed at altering the feedback perceived
by the user at the end of each cycle. This type of hack
would aim at manipulating the perception that the user has
of previous actions performed or the self-perception of
previous cognitive states generated through the BCI. The
criminal motive underlying these hacks would be to
induce—without the user’s permission—particular cogni-
tive states or actions in the subsequent cycle of the user for
the advantage of the hacker. For example, brain-hackers
could perform a sort of ‘‘brain-phishing’’ in which the user
is required by the hacker to insert a password or another
type of authentication information before the originally
intended process can continue (e.g. a user could be asked
for a password to actually start the program she has men-
tally commanded). Through the same mechanism, trau-
matic experiences could be induced in the user to his or her
detriment. Criminal activities performable through this
hack may include fraud, phishing, identity theft, and
physical or psychological harm.
These different sorts of hack with their related type of
malware, and potential criminal activities are presented in
Table 1.
Ethical implications
These four sorts of brain-hacking have several ethical and
legal implications. Some of these implications are cross-
categorical, i.e. apply to all forms of brain-hacking,
whereas some others are peculiar to a specific category of
hack. In this section an inventory of the ethical implica-
tions of brain-hacking will be provided. Further research is
required to develop each of these implications into a
detailed ethical and legal analysis to inform future regu-
latory strategies for the prevention of neurocrime.
The dual-use dilemma of brain-hacking
Cross-categorical ethical implications involve the general
problem of dual-use and the obtainment of informed con-
sent. By dual use it is meant the fact that the same bene-
ficial scientific knowledge or technology can be used for
good as well as for nefarious purposes (Pustovit and Wil-
liams 2010). Dual-use is a particularly crucial ethical
concern in computer, telecommunication and information
technology, since computers and networking technologies
are frequently used by actors for cybercriminal purposes.
Therefore, the ethical implications of dual-use, in particular
the dual-use dilemma, also apply to BCI technology and
the phenomenon of brain-hacking.
The peculiar dual-use dilemma of brain-hacking can be
summarized as follows: the same neural device (e.g. the
same BCI) has the potential to be used for good (e.g.
assisting cognitive function in neurological patients) as
well as bad purposes (e.g. identity theft, password cracking
and other forms of brain-hacking). This dilemma is pri-
marily faced not only by researchers and technology
developers, but also by companies because of their
potential product liability and by governments as they are
committed to promoting health and security of their
citizens.
At the current level of diffusion and sophistication of
brain-hacking, the benefits produced by BCI development
for patients and society significantly overwhelm the risks
associated to brain-hacking and other neurocrime.
Although some mild forms of brain-hacking have been
proven feasible in experimental settings or in real-life tests
(as in Cody’s Emokit project), there is no confirmed report
of criminal and/or detrimental activities involving BCI to
date. However, the phenomenon of brain-hacking should
be constantly monitored and appropriate safeguards should
be considered early in the design and deployment of the
M. Ienca, P. Haselager
123
neural devices as the opportunities for criminal offense and
malicious exploitation related to BCI are predicted to sig-
nificantly increase in the near future. These safeguards may
include:
•The development of mechanisms and methods for
anonymizing neural signals. A promising example of
this is the Brain–Computer Interface Anonymizer
(patent US 20140228701 A1), a method to generate
anonymized neural signals by filtering features to
remove privacy-sensitive information (Chizeck and
Bonaci 2014).
•The deployment and integration of security mecha-
nisms to detect uncharacteristic increase of noise in
BCI-processing at the level of measurement as well as
at the level of decoding and classification.
•The deployment of feedback mechanisms for users to
allow them to signal clearly undesired or uninitiated
output of the device. In vulnerable (e.g. physically
disabled or cognitively impaired) users these feedback
mechanisms may be connected to alarms and/or
location services that allow the hacked-users to auto-
matically alert a response center (e.g. their caregivers or
public safety authorities) and receive prompt support.
•The deployment of machine learning self-control
mechanism for detecting severe inconsistencies in the
classification of features. These self-consistency check
mechanisms could detect criminal circumstances where
the brain-hack occurs at the level of decoding and
classification of features.
•The provision of specific training sessions for clinical
BCI-users to train the users’ resistance to brain-
hacking, especially brain-hacking via input-manipula-
tion. These trainings could include the instruction of
specific responses to potentially unsafe stimuli such as
those related to banking and authentication methods
and could be directly provided by the health-care
institution where the user is allocated.
•The inclusion of free neurosecurity demos into the BCI-
package for general users. Future commercially avail-
able BCI-packages may include a small introduction
software package containing a brief serious game demo
with instructions and safety-guidelines related to brain-
hacking.
It is a major role for current neural engineering and
information security organizations to call for awareness
regarding the dual-use risks associated with brain–com-
puter interfacing and design regulatory mechanisms that
could enhance the safety and security of present and
future BCI applications. In addition, it is important to
raise awareness among the general public on the ethical
implications associated with the phenomenon of brain-
hacking and to stimulate the understanding and practical
application of guidelines aimed at protecting and pro-
moting the privacy, autonomy and integrity of the
individual.
Informed consent
Ethical issues with respect to informed consent for BCI-
use interventions especially focus on the ratio between the
high expectations that BCI technology may generate and
the possible vulnerability of potential BCI users (Clausen
Table 1 Synoptic view of malicious brain-hacking
Phase Type of attack Criminal activity Ethical problem Proved feasibility
Input Providing misleading input Password/PIN cracking
Identity theft
Fraud
Phishing
Privacy
Confidentiality
Personal security
H
Measurement Noise addition
Manipulating classification
Disruption
Termination
Hijacking
Psychological distress
Physical harm
Diminished agency
–
Decoding Overriding signal sent to output Disruption
Termination
Hijacking
Psychological distress
Physical harm
–
Output Feedback alteration Disruption
Termination
Hijacking
Diminished agency
Psychological distress
Physical harm
Uncertain personhood
Uncertain moral responsibility
–
Hacking the brain: brain–computer interfacing technology and the ethics of neurosecurity
123
2011). For example, in the case of severe neuromuscular
patients such as LIS patients, high expectations on the
liberating effect of BCI technology may represent a major
ethical challenge, since these expectations could under-
mine patients’ evaluation of risks and benefits, including
the risks associated with the phenomenon of brain-hack-
ing. Vulnerable patients may be more likely to accept a
higher risk of information insecurity, hence become more
exposed to brain-hacking. To prevent this, accurate
monitoring and reporting of the phenomenon of brain-
hacking is recommended not only for scientists and
ethicists but also for technology producers and the media.
Inaccurate or insufficient reporting may result in gener-
ating unrealistic expectations in patients and reducing
their perception of risk. In addition, more rigorous pro-
cedures for informed consent should be implemented to
increase the user’s understanding of the risk–benefit ratio.
It is worth remembering that getting informed consent is
especially challenging when communicating with severely
paralyzed target users such as those suffering LIS.
Impaired communicative capacities of LIS patients
require paying attention also to some characteristics of
information and communication that are not reducible to
verbal communication (e.g. eye blinking). As Clausen
(2011) note, this is especially important for the questions
whether the patient understands the information correctly,
and whether there are any questions left for him/her
(Clausen 2011).
Privacy, confidentiality and security
Particular ethical problems are posed by each single sort of
hacking. Two major ethical problems are associated with
hacking through input-manipulation. The first one is pri-
vacy. The possibility of extracting private and sensitive
information from the brain of users represents a significant
treat to privacy and data protection. Users that are victims
of this sort of brain-hacking typically lose the ability to
seclude confidential or inherently sensitive information
about themselves, thus experience an intrusion of their
private sphere (Bonaci et al. 2014). This ethical problem is
particularly significant because privacy is a priority issue in
a free society, closely linked to civil liberties, democracy
and human rights (Westby 2004). The protection of privacy
and confidential data is a primary commitment in the
United States as well as in the European Union where there
is a collaborative push for modernizing the current data
protection principles, strengthening the data protection
mechanisms, ensuring police and criminal justice cooper-
ation and a proper enforcement of the rules on privacy and
confidentiality (Heisenberg 2005).
The second problem is security. As experimentally
shown by Martinovic et al. (2012), brain-hacking via input
manipulation exposes BCI users to the risk of losing
surveillance over their personal and financial security.
Additionally, the opening of a breach into private and
confidential information implied by input-manipulation
also exposes users to physical and psychological insecurity.
The reason for that stems from the fact that the sort of
information potentially extractable from a user’s mind does
not limit to financial information but may extend to
information about the health condition of the users, their
profession, location, psychological capacities, sexual
preferences, religious beliefs, routine activities etc. For
example, Martinovic et al. (2012) have proved the feasi-
bility of extracting information about the user’s place of
residence and date of birth, two types of information that
are directly involved in personal security. It is expected
that other types of equally complex information can be
extracted in a similar manner (Bonaci et al. 2014). This
type of information is potentially of interest not only to
criminals involved in harmful activities such as blackmail
but also to employers and insurances. For example, health
insurance companies may be interested in extracting
information about the medical records of the user to accept
or reject her enrollment into an insurance plan or to
determine her insurance premiums. Similarly, employers
could extract information about the user’s political views
or sexual preferences and commit political or sexual ori-
entation discrimination.
Physical and psychological safety
Brain-hacking via measurement-manipulation, decoding-
manipulation, and feedback-manipulation pose a problem
for physical and psychological safety. These types of
hacking may result in severe physical and psychological
(e.g. traumatic experiences) harm to users in a way that is
proportionate to the level of benefit of the BCI in
assisting the user’s physical and psychological perfor-
mance. For example, patients using BCIs to control
wheelchairs may suddenly lose their reacquired spatial
mobility and be led back to their original condition of
impairment (prior to the BCI). Similarly, robotic limb
users and patients using vision BCIs may lose respec-
tively their reacquired motor capacity and visual percep-
tion. This sort of attacks require immediate monitoring in
the context of security, surveillance and public health as
they may not need to involve sophisticated malware
development, hence can performed also in absence of
specific cybercriminal skills.
In addition, BCI users that are victim of these types of
attack may experience psychological distress as a result of
their incapacity to perform the actions that they are mentally
inducing. This distress would be particularly significant in
LIS patients who use BCIs as the only available connection
M. Ienca, P. Haselager
123
to the external world.
9
As the primary goal of implementing
neurotechnologies in health-care is promoting the benefit of
the patient, the development of regulatory mechanisms for
protecting physical and psychological safety will be
required. Equally strict and rigorous regulatory mechanisms
should protect healthy people who use consumer-grade BCI
for entertainment, gaming and communication.
Autonomy, agency and personhood
Particularly critical ethical and legal implications are posed
by brain-hacking through decoding and feedback-manipu-
lation. The reason for that stems from the fact that these
types of brain-hacking, as previously mentioned, would not
simply enable malevolent actors to access information but
may cause changes in the user’s decision-making and/or
behavior. This possibility for an external control over the
user’s future behavior seems to substantially conflict with
the moral values of personal autonomy and free agency and
may even interfere with the self-determination of personal
identity. Personal autonomy is generally understood as the
capacity of someone to deliberate or act on the basis of
one’s own desires and plans and not as the product of
manipulative or distorting external forces (Anderson 2013;
Buss 2002). Autonomous individuals are those that are able
to act freely in accordance with a self-chosen plan. By
contrast, potential victims of brain-hacking may see their
deliberation and action being partially limited, controlled
or interfered by malevolent others. From this perspective,
the way brain-hackers influence the users’ decisions and
behavior seems to substantially undermine their individual
autonomy. The threat to autonomy posed by brain-hacking
will be exacerbated in the clinical context as it would affect
an extraordinarily vulnerable class of individuals such as
patients with severe neurological disorders. In medical
ethics, autonomy, conceived at minimum as a ‘‘self-rule
that is free from both controlling interference by others and
from limitations’’ (Varelius 2006), is usually considered a
fundamental requirement for the respect of patients and the
protection of their dignity (Beauchamp and Childress 2001;
Varelius 2006). It is important to stress, however, that
although hacked BCI-users with severe neurological con-
ditions would be exposed to the risk of diminished auton-
omy if compared to non-hacked users with the same
condition, they may, nevertheless, achieve greater overall
autonomy than equally impaired patients who do not have
access to BCI whatsoever. This fact is worth extensive
philosophical reflection, since the counterintuitive situation
that the same technology can both increase and diminish
autonomy requires quite detailed analysis of the benefit-
risk ratios in different scenarios.
The challenge to autonomy posed by these types of
brain-hacking also raises the issue of coercion, i.e. the
exercise of a constraining power on another party (besides
the use of force, violence, and threats thereof) with the
purpose of forcing him or her to act in a non-voluntary
manner (Mill 1869). As such, brain-hacking could raise a
novel, more subliminal (since performed below the vic-
tim’s threshold of consciousness) form of coercion which
adds to extortion, blackmail, torture and other currently
performed forms of coercion.
Strictly related to the notion of autonomy is the notion of
agency, i.e. the capacity of an agent to act, and the capacity
to distinguish between events that are self-initiated versus
simply occurring (i.e. experiencing the difference between
doing something and having something happening to you).
The sense of agency serves to identify the range of one’s
actions (i.e. activities actively performed by agents) from
those events that are passively caused by external forces.
For example, jumping into the water is considered an action
if the jump is performed by the agent without being caused
by external forces (e.g. being forced into the water by
another person or by the wind). Similarly, controlling a
wheelchair via BCI is an action if it is performed inten-
tionally by the agent. In itself, BCI already contains the
possibility to result in considerable uncertainty of the BCI
user about whether or not the user actually did or did not
perform a BCI mediated action, e.g. in case of error
(Haselager 2013). When another agent, e.g. a remote
hacker, gains control over the application and determines
the actions of the user, the agency of the BCI user dimin-
ishes and the uncertainty of ascribing the action to the user
increases significantly. This is ethically problematic for
three major reasons. First, because the detachment of the
intention-action causal link prompted by brain-hacking
may result in psychological distress and for the user. Sec-
ond, because it generates uncertainty about the voluntary
character of the user’s actions. Third and consequently,
because in Western jurisprudence the capacity for volun-
tary control over one’s own actions is consider a requisite
for legal liability. Therefore, diminished or absent volun-
tary control over one’s own actions would result in
diminished or absent legal liability of the user with regard
to those actions. This intimate link between agency and
legal liability is explicitly expressed by the USA Model
Penal Code (MPC), Section 2.01, which states that ‘‘(1) a
person is not guilty of an offense unless his liability is based
on conduct that includes a voluntary act or the omission to
perform an act of which he is physically capable’’. The
MPC also provides a list of examples of non-liable acts
which includes: ‘‘(a) a reflex or convulsion; (b) a bodily
9
Here too, there is a difference between hacking by disruption and
hijacking, as the psychological stress involved in doing something
different from what the user intended may differ from the traumatic
experience of losing control over oneself.
Hacking the brain: brain–computer interfacing technology and the ethics of neurosecurity
123
movement during unconsciousness or sleep; (c) conduct
during hypnosis or resulting from hypnotic suggestion;
(d) a bodily movement that otherwise is not a product of the
effort or determination of the actor, either conscious or
habitual’’ (Wechsler 1968). The performance of an act as a
consequence of brain-hacking via output manipulation
seems to fit in at least three of the four above mentioned
explicative categories, as the act is not a product or deter-
mination of the BCI user but of the hacker. Problems of
uncertain legal liability are expected to arise. Collaborative
research at the intersection between criminal law, cyber-
security, neurotechnology and ethics will be required in the
next future to assess these problems in a manner that
facilitates the judicial circuit and protects BCI users.
Conclusions
This paper took a first step in addressing the issue of brain-
hacking and raising awareness on the ethical and security
implications associated with the malicious use of BCI
technology. An overview of the possible vulnerability
sources of BCIs and their related sorts of brain-hacking was
offered. Additionally, an inventory of the major ethical
implications of brain-hacking via BCI was provided. Fur-
ther interdisciplinary investigation is required to extensively
analyze those implications and to develop a normative and
regulatory framework that allows maximizing the benefits
of BCI technology while minimizing its potential risks.
BCI applications have the potential of significantly
improving life quality in patients (especially in patients
suffering severe neuromuscular disorders) and enabling
enhanced and more personalized user experience in com-
munication, gaming and entertainment for general users.
However, the potential benefits of this technology may be
tempered if security issues and ethical-legal considerations
remain unaddressed. Ideally, this debate should involve the
collaboration of ethicists, neuroscientists, engineers, com-
puter scientists, cybersecurity experts, lawyers and other
significant stakeholders and inform regulators and policy-
makers.
Acknowledgments This project was partly supported by the Eras-
mus Mundus Scholarship (European Commission).
Compliance with ethical standards
Conflict of interest The authors declare that they have no com-
peting interests.
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