Security Applications: Beyond Biometrics

A number of novel study designs have made use of the unique characteristics of BMIs to produce results that are not possible with other methods of verification.

The first example is Project Oblio. Project Oblio has an ambitious goal; to create a human-only area of the internet, beginning with a decentralized form of reCAPTCHA. If such a liveness detectable signal is also a biometric, then Project Oblio can create an anti-Sybil internet, with rate-limited cryptocurrency transactions at the user level, and ensure that everybody (even whales) have exactly one vote within Project Oblio’s government, prediction markets, and any other straw poll you might desire to post.

Another example is the bio-cyber machine gun (BCMG). This EEG-based password-validator works through a spin-off of the oddball paradigm, called the “spelling paradigm”. Letters that may be used in a password are grouped in regions, and a second set of letters are used to label these grouped regions. The region-letters (second set) are then flashed to a person wearing an EEG cap, in a random order. When a user is flashed the region-letter that corresponds to the region containing the desired letter in their password, their brain non-consciously emits a P300 brainwave, due to their underlying surprise or “peaked interest” correlated with the P300 inflection. (The P300 is commonly examined in commercial neuromarketing systems.) On the next go-round, a person is shown only the letters in their selected region, and can then choose the letter that comes next in their password. [24] Repeating this task, passwords can be strung together that have levels of entropy on a cryptographic level, as well as being tied to the biometric identity of a person’s brainwaves.

Another application involves EEGs in smart-home appliances for the disabled. In this set-up, visually-evoked potentials are able to both authenticate homeowners and reject nefarious individuals. An additional classifier based on imagined motor actions allow a disabled person to perform tasks, such as turn on and turn off lights, with moderately-high success (up to 85%). For those who may be quadriplegic, simple tasks such as turning a key in a locked door are impossible. Thus, a BMI set-up such as this provides not just security, but mobility, control, and independence, all with a single headcap. [25]

Brainwaves have also been used to generate a replicable PIN using a single-channel, commercial grade EEG [26]. Subjects underwent the oddball paradigm, viewing random presentations of digits 0-9. When the “password” digit was presented, a measurable brain wave called the P300 was produced, quite similar to the BCMG. Though the PIN was repeatedly classified with 100% accuracy initially, a latter publication by the same group indicated their classifier performance degraded each month of time following the training session (down to 78% for one subject after 3 months) [19].  This is one of the few studies looking at BMI biometric classifier degradation over time.

A protocol mentioned earlier utilizes recognition of EEG artifacts as a “covert warning” feature in the case of threat. The idea behind covert warning is that an authorized subject put at risk is capable of secretly broadcasting an alert that they are under attack, without alerting their attackers that they are calling for help. In this experiment, identified users wearing EEG caps clenched their teeth three times to produce sharp voltage spikes on the EEG trace, allowing for a signal to be detected 100% of the time. During this process, personal-identification rates dropped from 93% to 90%, a small drop-off considering the feature bonus. This is one of the only studies to expand on use cases for continuous verification. Notably, training data for this study was collected during various mental states (before and after caffeine intake, early and late in the day, etc.), which may have lead to its high classification rate. [14]

An increasingly  popular feature of security systems is the use of multi-factor authentication. Multi-factor authentication relies on a number of security measures, such as a fingerprint and a password, to authenticate an individual. Basic multi-factor authentication systems using BMIs have been reported in the literature [27]. However, BMIs are unique in that nearly any repeatable stimuli or task produces a distinguishable brain pattern. Thus, BMIs offer an endless number of “multi-task” authentication opportunities, all with a single headcap.

Quite recently, [28] proposed a multi-task learning system for BMI verification that interweaved information from finger-movement tasks to maximize learning. Subjects were asked to imagine moving either their left or right index finger. As was the case in [XX], the subject’s left side recordings were more distinguishable, but the greatest discrimination was obtained when using both the left and right side data together. These studies show that though some features are more reliable than others, integrating multi-factor authentication can produce even better security system than with one task, without the need for additional hardware.

Tasks that could be integrated into a multi-task authentication system include resting EEG state (including closed and opened eyes, see Table 1), imagined speech [10],  visually-evoked potentials for different objects (see [30], also Table 2), auditory-evoked potentials [29], solving a mathematical task [38], and imagining the rotation of an object or body part [13,38].  As implied, a user may choose which or however many of these stimuli to train their classifier on, adding a further security measure to this protocol.

Table 2: Classification accuracy of visually-evoked potentials using EEG.

eeg

 

 

 

  1.       http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4445586/
  2.       http://www.nature.com/nmeth/journal/v11/n6/full/nmeth.2936.html
  3.       http://www.ajol.info/index.php/amhsr/article/view/112158
  4.       https://www.cert.org/insider-threat/best-practices/
  5.       D&d – Need to find this citation still again
  6.       – Lost this citation too
  7.       http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=812278
  8.       http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=813403
  9.       http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4107575
  10.   https://www.researchgate.net/profile/B_Kumar8/publication/224194495_Subject_identification_from_electroencephalogram_(EEG)_signals_during_imagined_speech/links/54a97f8a0cf2eecc56e6c45f.pdf
  11.  http://www.theregister.co.uk/2014/12/29/german_minister_fingered_as_hackers_steal_her_thumbprint_from_a_photo/
  12.  http://www.sciencedirect.com/science/article/pii/S0921889014002632
  13.   S. Marcel, J. R. Millan. Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4):743–748, April 2007.
  14.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6199830&tag=1
  15.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7126357&tag=1
  16.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7314193
  17.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6994220
  18.  https://kar.kent.ac.uk/49535/1/PIN_ijcb.pdf http://www.inderscienceonline.com/doi/abs/10.1504/IJBM.2014.060960
  19.   http://www.researchgate.net/profile/Patrizio_Campisi/publication/282122165_EEG_Biometrics_for_User_Recognition_using_Visually_Evoked_Potentials/links/56039ec808ae08d4f171779c.pdf
  20.   http://www.sciencedirect.com/science/article/pii/S0925231215004725
  21.   http://link.springer.com/chapter/10.1007/978-3-319-19713-5_13#page-1
  22.  http://download.springer.com/static/pdf/440/chp%253A10.1007%252F978-3-642-27733-7_9145-2.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Freferenceworkentry%2F10.1007%2F978-3-642-27733-7_9145-2&token2=exp=1447987756~acl=%2Fstatic%2Fpdf%2F440%2Fchp%25253A10.1007%25252F978-3-642-27733-7_9145-2.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Freferenceworkentry%252F10.1007%252F978-3-642-27733-7_9145-2*~hmac=be7c889f4ec177bd4096057e765bb2169f3614dfd4323d2e28787a2e3b92393c
  23.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985
  24.  http://www.ncbi.nlm.nih.gov/pubmed/19163618   

http://www.igi-global.com.proxy.libraries.rutgers.edu/gateway/chapter/full-text-pdf/7471

 

  1.   http://www.metaljournal.com.ua/assets/Journal/MMI-6/060-Jianfeng-Hu.pdf
  2.   http://csee.essex.ac.uk/staff/palaniappan/1930378.pdf
  3.   http://ieeexplore.ieee.org.proxy.libraries.rutgers.edu/stamp/stamp.jsp?tp=&arnumber=5634487
  4.   – Need to find citation for this one as well  
  5.   Auditory potentials – https://etd.ohiolink.edu/!etd.send_file?accession=ucin1439300974&disposition=inline
  6.   http://ojs.bibsys.no/index.php/NIK/article/view/243
  7.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7280344
  8.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985
  9.   Facial movements, eeg authentication using artifacts http://link.springer.com/chapter/10.1007/978-3-319-07995-0_34#page-1
  10.   Multi-level approach based on eye-blinking http://www.sciencedirect.com/science/article/pii/S0167865515002433
  11.   http://www.nature.com/neuro/journal/v18/n11/full/nn.4135.html
  12.   DOI is good  http://www.nature.com/nphoton/journal/v8/n6/full/nphoton.2014.107.html
  13.   DOI vs fMRI – http://www.ncbi.nlm.nih.gov/pubmed/23578579
  14.   http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1619442

 

BMI Biometrics: What don’t we know?

Brain-machine interfaces are a biometric that's been untapped by leading governments and tech companies. Can we use anonymization techniques like homomorphic encryption to create computational barriers to fake-accounts?

Today’s EEG’s are commerical, having gelless electrode contacts for convenient recording outside the scalp. They are wirelessly compatible with mobile phones, cost far less than comparable biometric devices, and have established themselves as a novel consumer item. Given the unique advantages to BMIs already discussed (continuous verification, covert warnings, and inclusiveness), as well as their headcap design, EEGs could soon play a role in consumer virtual reality systems, as well as a corporate setting. Since many people could soon rely on the security of BMI biometrics, it is important that the major questions still unanswered in this underdeveloped field are brought to light.

While a large number of BMI studies involving EEGs have proven its ability to identify persons, there is a dearth in the field analyzing its potential for subverting security protocols. To date, only one study has evaluated security systems for storing EEG template data (see next section, or [16]), and only one has evaluated attacks on such a system (see [17]). This is probably because BMIs have yet to reach mainstream adoption, and there are no well-accepted protocols for BMIs in security systems. Once a standardized BMI security protocol is accepted, it will be easier to evaluate the BMI’s robustness in defense against spoofing.

Aside from the security issues, a number of basic, brain-based questions have  yet to be answered in the neuroscientific literature. The first has not been explored since the early, low-resolution studies. Can a brainwave identify differences between identical twins? This question is particularly relevant when looking at task-based biometric systems, which tend to have higher classification rates than resting-state studies (as were initially performed). Additionally, there are a multitude of effective mathematical models (Table 1) which could distinguish themselves on the basis of identifying identical twins.

Another concern on which research is sparse is that brainprints tend to change over a person’s lifetime. Initial research in this area suggests brainprints reach a mature, recognizable pattern shortly after puberty (age 19-20 years[6]), and become involuted with old age. In biometric studies, degradation of classifier performance due to this effect has been variable. Depending on the task used in the experiment, some classifiers have been found to degrade over a period of days [13] or weeks [18,19], while others have lasted up to 6 months [20]. In Marcel and Milan [13], there are indications that higher, long-lasting classification rates can be obtained when training data is collected over a period of days. A solution may be to have a short but infrequent training sessions over a week to establish a person’s identity, then update a person’s recording parameters each time they access sensitive data. Integrated with a password or “resting” EEG parameters, this may allow for effective updates to a person’s security parameters.

Two basic types of experiments exist in the BMI biometric literature: identification, and authentication. While identification experiments are more common, and may be a strong correlation for BMI’s authentication ability, they are far less practical in security settings. For BMIs, person-identification can be thought of as studies that pick out a person from a large group based on brainwave data recorded during a commontask. These experiments often involve recording resting-state brain features with no real active engagement by the user, and often have a lower classification rate. Alternatively, authentication protocols rely on a single task or series of tasks to identify a user. An authentication protocol should rely on a person using a series of “imaginations” known only to them (such as a password, mental image, or mental rotation of an object) to produce a distinct set of brainwaves. This person seems to ‘authorize’ a person based on this thought, regardless of how many members of the population make an attempt. Additionally, should a person be verbally instructed on how to make another’s “password thought”, the system should still reject this nefarious poser.

Though various types of “password thoughts” have proven effective throughout the literature (see next section), the next step to determine whether verifiable thoughts can be mimicked by nefarious individuals. One paradigm could involve a person viewing “live” brain recordings of both themselves and another, and attempting to alter their brain recordings to match that of the first person. The question of whether authenticating thoughts can be mimicked is particularly relevant in the case of identical twins. As a whole, the field of BMI biometrics must focus on developing studies that have “person-authentication” in mind, rather than person-identification.

Unfortunately, like biometrics as a whole, BMI authentication protocols may be at risk of a singular attack focused on obtaining or altering template parameters. To counter this, many biometric systems focus on key-binding architectures, which combine biometric templates with binary keys.  With properly chosen parameters, this protected setup approaches recognition rates close to those that are unprotected, and provides a quantifiable security-level of about 40 bits for this task.[14]

One long-held concern regarding the use of EEGs is the amount of time needed to train the classifier. In [31], it was noted that an increase in training time generally results in greater classifier performance. In this study, LVM classification had the highest classification rate (greater than 95%) after about 29 sessions, with each session consisting of 1-minute of recording (though interestingly, for a smaller number of training sessions, support vector mechanics was more effective). Although training sessions may be lengthy compared to other biometrics, [32] found that individual characteristics can be elucidated with an 88% classification rate based on only 0.2 second bins.

Lastly, a final concern of using BMIs as a biometric is privacy compliance. A brain recording may unveil personal health information of the recorded subject, such as a history of stroke or mental illness, epilepsy, or even alcoholism [21]. A severe trauma to the head or acute development of a neurodegenerative disease (such as a stroke) may lead to an unidentifiable brain pattern. Since BMI recordings are genetically-linked, they may one day be correlated with racial or other physical characteristics, permitting brainprints to identify an unknown individual.

Almost a decade ago, large-scale studies examining the nuances of a BMI-biometric protocol would have been too expensive to pursue. Today, there are numerous small-scale studies that approach BMI biometrics purely based on the potential of the underlying brainwave signal, and without regard to advances in encryption and decentralized machine learning. A number of replicable studies by those who recognize the potential for a one-person-one-vote internet and user-rate-limited, feeless transactions are needed before BMIs can be recommended for use in high-risk security settings.

Table 1: Person-identification in EEG biometrics and their classification. Taken from [22]

eeg1.png

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985 [23]

.. others

Citations (posts continued in other pages)

 

  1.       http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4445586/
  2.       http://www.nature.com/nmeth/journal/v11/n6/full/nmeth.2936.html
  3.       http://www.ajol.info/index.php/amhsr/article/view/112158
  4.       https://www.cert.org/insider-threat/best-practices/
  5.       D&d – Need to find this citation still again
  6.       – Lost this citation too
  7.       http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=812278
  8.       http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=813403
  9.       http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4107575
  10.   https://www.researchgate.net/profile/B_Kumar8/publication/224194495_Subject_identification_from_electroencephalogram_(EEG)_signals_during_imagined_speech/links/54a97f8a0cf2eecc56e6c45f.pdf
  11.  http://www.theregister.co.uk/2014/12/29/german_minister_fingered_as_hackers_steal_her_thumbprint_from_a_photo/
  12.  http://www.sciencedirect.com/science/article/pii/S0921889014002632
  13.   S. Marcel, J. R. Millan. Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4):743–748, April 2007.
  14.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6199830&tag=1
  15.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7126357&tag=1
  16.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7314193
  17.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6994220
  18.  https://kar.kent.ac.uk/49535/1/PIN_ijcb.pdf http://www.inderscienceonline.com/doi/abs/10.1504/IJBM.2014.060960
  19.   http://www.researchgate.net/profile/Patrizio_Campisi/publication/282122165_EEG_Biometrics_for_User_Recognition_using_Visually_Evoked_Potentials/links/56039ec808ae08d4f171779c.pdf
  20.   http://www.sciencedirect.com/science/article/pii/S0925231215004725
  21.   http://link.springer.com/chapter/10.1007/978-3-319-19713-5_13#page-1
  22.  http://download.springer.com/static/pdf/440/chp%253A10.1007%252F978-3-642-27733-7_9145-2.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Freferenceworkentry%2F10.1007%2F978-3-642-27733-7_9145-2&token2=exp=1447987756~acl=%2Fstatic%2Fpdf%2F440%2Fchp%25253A10.1007%25252F978-3-642-27733-7_9145-2.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Freferenceworkentry%252F10.1007%252F978-3-642-27733-7_9145-2*~hmac=be7c889f4ec177bd4096057e765bb2169f3614dfd4323d2e28787a2e3b92393c
  23.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985
  24.  http://www.ncbi.nlm.nih.gov/pubmed/19163618   

http://www.igi-global.com.proxy.libraries.rutgers.edu/gateway/chapter/full-text-pdf/7471

 

  1.   http://www.metaljournal.com.ua/assets/Journal/MMI-6/060-Jianfeng-Hu.pdf
  2.   http://csee.essex.ac.uk/staff/palaniappan/1930378.pdf
  3.   http://ieeexplore.ieee.org.proxy.libraries.rutgers.edu/stamp/stamp.jsp?tp=&arnumber=5634487
  4.   – Need to find citation for this one as well  
  5.   Auditory potentials – https://etd.ohiolink.edu/!etd.send_file?accession=ucin1439300974&disposition=inline
  6.   http://ojs.bibsys.no/index.php/NIK/article/view/243
  7.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7280344
  8.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985
  9.   Facial movements, eeg authentication using artifacts http://link.springer.com/chapter/10.1007/978-3-319-07995-0_34#page-1
  10.   Multi-level approach based on eye-blinking http://www.sciencedirect.com/science/article/pii/S0167865515002433
  11.   http://www.nature.com/neuro/journal/v18/n11/full/nn.4135.html
  12.   DOI is good  http://www.nature.com/nphoton/journal/v8/n6/full/nphoton.2014.107.html
  13.   DOI vs fMRI – http://www.ncbi.nlm.nih.gov/pubmed/23578579
  14.   http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1619442

 

BMI Biometrics: What do we know?

Just like fingerprints, brainprints do have inheritable characteristics. However, unlike many other inputs to biometric analyses (fingerprints, irises, face-photos), brainprints have tremendous immunity to spoofing.

A brain-machine interface (BMI) is an instrument that records changes in electrical potential associated with thought, motor movement, or blood flow within a person’s skull [1,2,3]. As every person’s brain develops differently, these electrical potentials can be analyzed to distinctly identify persons with high accuracy. This is particularly relevant to the field of biometrics, in which biological indicators, such as fingerprints, faces, DNA, or brainwaves, can be used to discriminate between individuals. Today, biometrics are used for authentication when passwords may not be optimal, such as when someone may be “shoulder-surfing”, or if insider-threat level is high [4].  In BMI biometrics, the feature most often (but not always) studied is the “brainprint” — an underlying set of distinct features emitted from a person’s skull during a mental task. Given the remarkable applications of a one-person-one-vote layer to the internet, brain-based biometrics’ distinct advantages have high-potential applications.

It may come as a surprise that the earliest relevant work in BMI biometrics had the intention of finding genetic similarities, not identifiable differences. As early as 1936, researchers demonstrated that brainwave recordings were more similar among identical twins than among two random persons [5]. Three decades later, it was concluded that crude brainwave features have an autosomal-dominant method of inheritance [6]. It wasn’t until 1999 that the first person-identification study using brainwaves was published.  In (Poulos, 1999a), alpha-band rhythms of brainwave recordings identified individuals in a group with 72-80% accuracy [7]. It took only a few months for Poulos to apply a new method to the same dataset, improving his classification results rate to 95% through convex polygon intersections [8].

In 2007, the first large-scale BMI-biometric study was conducted by Palniappian et. al. By analyzing subjects’ brainwaves as they memorized images from a black-and-white picture set, a classification rate of 98.12% in 102 subjects was obtained [9]. In 2010, Brigham et. al applied support vector mechanics on brainwave recordings during subjects’ imagined speech, resulting in a 98.96% classification accuracy in 120 subjects, alerting the security community that brainprints were here to stay [10].

Just like fingerprints, brainprints do have inheritable characteristics. However,unlike many other inputs to biometric analyses (fingerprints, irises, faces), brainprints have tremendous immunity to spoofing. An attacker cannot synthetically generate BMI recordings to assume the identity of another individual as they can with a fingerprint [11], iris, gait [12], or facial recognition system. This is due to the tremendous complexity of the underlying biochemical processes of the brain and lack of public data available for training spoofers. When obtained in a human-detectable setting, brainprints may also be immune to stealing even from spoofing algorithms, unlike DNA. This may be performed in the short-term using vybuds as part of a “time-dependent input, biology-dependent output” communal hash, and in the long-term using OpenMined‘s mantra of “if good data were easy to fake, machine learning theorists would fake data instead of collecting it”.

In addition to BMI’s built-in “liveness” detector, BMI biometric systems are unique in that they require a user’s willingness to cooperate. Because brain patterns are heavily influenced by mood and stress-level [13,14], identification is not valid when forced by an outside party. This is a major benefit for user-safety. While this volition requirement is true for the vast majority of constructed paradigms for BMI biometrics to date, in some special cases (such as proving that a particular person committed a crime), a volition-insensitive BMI biometric may be desired. Initial research into developing protocols where identification is consistent regardless of a person’s stress-level has been explored[15].

Another advantage unique to BMI biometrics is their mobility. Recordings can be made unobtrusively, allowing for continuous verification throughout a secure task. This prevents one-time login attacks and person substitution. With continuous verification, a log-in cannot be transferred to others to allow them access to sensitive data. Given that digital security systems are most often thwarted by insiders [5], this advantage is particularly enticing for those working with corporate enterprise. Likewise, new research is taking this distinct feature of BMIs to the next level. Continuous verification allows for users to subtly initiate emergency broadcasts, without alerting a threatening individual who may be watching (see later section –  “Security Applications: Beyond Biometrics”). These “covert warnings” make use of distal artifacts (such as clenching one’s teeth) to secretly call for help without alerting one’s attackers that it has been requested [14], and are only possible through a continuous verification protocol.

A final dose of idiosyncrasy offered by BMIs is their inclusiveness. Those with severe injuries such as burned or missing fingers, aniridia (absence of the iris), or severe paralysis (such late-stage ALS or “locked-in” syndrome) are not excluded from using a BMI biometric. Due to their inclusiveness, BMIs are considered ‘universal’.

A number of different modalities have been used to record brainwaves, but to date, only two have been used in person-identification. The first, and most relevant, is the electroencephalography (EEG). The second, discussed later, is functional magnetic resonance imaging (fMRI).

 

 

  1.       http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4445586/
  2.       http://www.nature.com/nmeth/journal/v11/n6/full/nmeth.2936.html
  3.       http://www.ajol.info/index.php/amhsr/article/view/112158
  4.       https://www.cert.org/insider-threat/best-practices/
  5.       D&d – Need to find this citation still again
  6.       – Lost this citation too
  7.       http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=812278
  8.       http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=813403
  9.       http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4107575
  10.   https://www.researchgate.net/profile/B_Kumar8/publication/224194495_Subject_identification_from_electroencephalogram_(EEG)_signals_during_imagined_speech/links/54a97f8a0cf2eecc56e6c45f.pdf
  11.  http://www.theregister.co.uk/2014/12/29/german_minister_fingered_as_hackers_steal_her_thumbprint_from_a_photo/
  12.  http://www.sciencedirect.com/science/article/pii/S0921889014002632
  13.   S. Marcel, J. R. Millan. Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4):743–748, April 2007.
  14.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6199830&tag=1
  15.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7126357&tag=1
  16.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7314193
  17.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6994220
  18.  https://kar.kent.ac.uk/49535/1/PIN_ijcb.pdf http://www.inderscienceonline.com/doi/abs/10.1504/IJBM.2014.060960
  19.   http://www.researchgate.net/profile/Patrizio_Campisi/publication/282122165_EEG_Biometrics_for_User_Recognition_using_Visually_Evoked_Potentials/links/56039ec808ae08d4f171779c.pdf
  20.   http://www.sciencedirect.com/science/article/pii/S0925231215004725
  21.   http://link.springer.com/chapter/10.1007/978-3-319-19713-5_13#page-1
  22.  http://download.springer.com/static/pdf/440/chp%253A10.1007%252F978-3-642-27733-7_9145-2.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Freferenceworkentry%2F10.1007%2F978-3-642-27733-7_9145-2&token2=exp=1447987756~acl=%2Fstatic%2Fpdf%2F440%2Fchp%25253A10.1007%25252F978-3-642-27733-7_9145-2.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Freferenceworkentry%252F10.1007%252F978-3-642-27733-7_9145-2*~hmac=be7c889f4ec177bd4096057e765bb2169f3614dfd4323d2e28787a2e3b92393c
  23.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985
  24.  http://www.ncbi.nlm.nih.gov/pubmed/19163618   

http://www.igi-global.com.proxy.libraries.rutgers.edu/gateway/chapter/full-text-pdf/7471

 

  1.   http://www.metaljournal.com.ua/assets/Journal/MMI-6/060-Jianfeng-Hu.pdf
  2.   http://csee.essex.ac.uk/staff/palaniappan/1930378.pdf
  3.   http://ieeexplore.ieee.org.proxy.libraries.rutgers.edu/stamp/stamp.jsp?tp=&arnumber=5634487
  4.   – Need to find citation for this one as well  
  5.   Auditory potentials – https://etd.ohiolink.edu/!etd.send_file?accession=ucin1439300974&disposition=inline
  6.   http://ojs.bibsys.no/index.php/NIK/article/view/243
  7.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7280344
  8.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985
  9.   Facial movements, eeg authentication using artifacts http://link.springer.com/chapter/10.1007/978-3-319-07995-0_34#page-1
  10.   Multi-level approach based on eye-blinking http://www.sciencedirect.com/science/article/pii/S0167865515002433
  11.   http://www.nature.com/neuro/journal/v18/n11/full/nn.4135.html
  12.   DOI is good  http://www.nature.com/nphoton/journal/v8/n6/full/nphoton.2014.107.html
  13.   DOI vs fMRI – http://www.ncbi.nlm.nih.gov/pubmed/23578579
  14.   http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1619442

 

Motivation

Leading experts in the field of artificial intelligence estimate that by 2040, a technological singularity will cause an unpredictable “intelligence explosion”, after which the capabilities of machines will supersede that of humans (Armstrong [2012]; Carvalko [2012]; Eden [2013]). On the other end of the spectrum, leading neuroscientists contend that no machine will ever be able to compete with the non-linear, non-Turing prowess of the human brain (Nicolelis [2015]). Through biological barriers to computation, we can harness the power of our brains to isolate ourselves from unforeseen advances in machine-based artificial intelligence. Consequently, new methods of information transfer between humans may ignite an unpredictable intelligence explosion which rivals (or exceeds) that of machines.

For centuries, individuals have proclaimed their own existence with the phrase cogito ergo sum (“I think, therefore I am.”) (Descartes, 1685). Communicating thoughts between each other could, for the first time, prove the existence of individuals outside ourselves. Furthermore, an accused (or racially-persecuted) person could prove their innocence by sharing recorded neural patterns during the time of the arrest, a sports fan could experience the adrenaline and mechanical motions of their favorite athlete, a layperson could taste a restaurant’s best meal from across the globe. A blind child could receive visual input from its mother, mental states (hunger, happiness, excitement) could be quantified and tracked, infrared-light detectors could expand our senses (Thomson et al. [2013]). Permanent external storage of thoughts and memories could greatly enhance information recall, and information could be translated and analyzed in ways not yet imagined. A brain-to-brain network would not be limited to humans. The neural intelligence and sensory input of other animals could also be harnessed (Pais-Vieira [2013]; Trimper et al. [2014]).

A secure mechanism to tie a human lifeform to a digital identity can push our governments onto the internet, enabling world passports, transparent elections, and a true, global democracy. In such an identity network, contracts and digital payments can be initiated by thought, files and assets can be forwarded elsewhere upon death, sensitive information can be shared only after a specific neural impulse. Note that DNA offers a mechanism for a biological identity, but not a digital one. DNA can be shed, and thereafter, copied. A better form of identity would be one that is unhackable, digital-friendly, and disposable. Such a form of identity could become the basis for bio-digital signatures, filling in the gap between the virtual and natural.

A proof-of-cognition blockchain as an underlying identity-network for a brain-to-brain internet would provide sufficient autonomy for each of its users. If cryptographic keys were generated and stored on an offline, physically-inaccessible, neurally-trained implant, hacking a person’s identity would be impossible. Decentralization of the network would guarantee that all users had equal power, and that a single ill-acting party could not cause sweeping changes across the network. In the event an ill-acting party did enter the network, the public nature of a blockchain would alert its users, ensuring honest nodes could exit or reject the dishonest node before harm were spread. So long as the majority of nodes remained honest, a proof-of-cognition blockchain can maintain the safety of an individual’s conscious in a brain-to-brain network.

This paper proposes a pseudo-anonymous digital-biological network as a foundation for later brain-to-brain innovations. A rudimentary understanding of hashing, blockchains (Dai [1998]; Back [2002]; Nakamoto [2008]) and modern brain-machine interfaces (Lebedev and Nicolelis [2006]; Lebedev [2014]; Hildt [2015]) is recommended.

Reference: proof-of-cognition-implants , published May 2015. Disclaimer: Project Oblio’s mechanism does not rely on brain implants, but the mechanisms of action are the same. An early version of the paper provably exists in bitcoin address 13eeMVU5fXNfZdoBk5z4fEAbgSH9MawQ6H.

Neuroscience Research Market

How valuable are anonymized and unaltered brainwaves?

According to various modern researchers and the news reports released by Grand View Research, Inc, the global neuroscience is projected to reach the value of USD 30.8 billion by 2020. During the forecast period, the pace of growing developments in the field of neuroinformatics and sudden rise in patented research initiatives supported and funded by governments are factors projected to be responsible for the drive in the market share.

In 2016, the estimated value of the global neuroscience research was at USD 28.42 billion and it is quite expected to grow at a CAGR of 3.1% over the period forecast. The factors responsible for influencing the price thereby propelling the market growth include brain mapping research and investigation projects, neuroscience-based initiatives by government bodies, and the advancement of the technological tools and algorithms that are considered for implementation in the neuroscience space.

Over the forecast period, the introduction of novel technologies serving the purpose of mapping neuronal circuits situated in the brain functions is expected to boost the growth of this market. Furthermore, as a result of the global geriatric population, there is a significant increase in the demand for neuroscience-based research as this segment of the population is more prone towards earning the high risk of central nervous system disorders such as Parkinson’s disease and Alzheimer’s thereby making the market growth highly propelled.

 

REFERENCES

The science behind outdoor advertising

https://www.grandviewresearch.com/industry-analysis/neuroscience-market

https://www.prnewswire.com/news-releases/neuroscience-market-size-to-reach-308-billion-by-2020-grand-view-research-inc-532637711.html

https://www.forbes.com/sites/hbsworkingknowledge/2013/02/01/neuromarketing-tapping-into-the-pleasure-center-of-consumers/#77ab9dec2745

The science behind outdoor advertising

 

tDCS in the Treatment and Prevention of Alzheimer’s Disease

Can non-invasive neurostimulation prevent age-related cognitive decline, including Alzheimer's Disease?

WHAT IS ALZHEIMER’S DISEASE?

Alzheimer’s is a disease characterized by progressive neurodegenerative disorder and is also accountable for dementia (cognitive/memory decline) in old people. According to statistics from 2010, people who had dementia due to Alzheimer’s Disease (AD), at the age of about 60, was 4.02%, and it has been projected to increase drastically by 2030. Treating AD is very costly, with overall spending estimated to be around $422 billion in 2009. Presently, basic pharmacological treatments available for AD patients are N-methyl-D-aspartate receptor partial antagonist and cholinesterase inhibitors. But since these treatments have extremely bad side effects and limited response there is a dire need of alternative treatments for AD.

 

Transcranial Direct/Alternating Current Stimulation In Treating Alzheimer

Since treatments presently available for AD are not really appreciably effective, scientists have recently figured out that brain stimulation is another considerable alternative as a clinical treatment of Alzheimer’s. Non-invasive transcranial direct current stimulation of the brain has been proved to be one of those methods in treating and preventing Alzheimer’s Disease. It also has considerably better results than other treatment options. To date, tDCS has shown beneficial effects in treating several other diseases as well.

 

  1. tDCS Improves Mini-Mental State Examination Score in AD Patients:

A number of experiments have reportedly observed improved cognitive functions of patients with AD. In one of the experiments carried out in Egypt, Khedr and colleagues randomly divided 34 participants, suffering from AD, in 3 groups. Participants of cathodal and anodal groups, both, underwent daily tDCS continuously for 10 days with a low current intensity of 2 mA given to them for about 25 mins/day. A significant improvement was observed on the MMSE score after the experiment, which lasted for about 10 days.

  1. tDCS improves Visual Recognition in AD patients:

Similarly, Boggio and colleagues have been reported to demonstrate that tDCS used on the temporal cortex and left DLPFC enhanced VRM (Visual Recognition Memory) of patients with Alzheimer’s Disease. Ten AD patients were enrolled by the researchers for this experiment, who received two real stimulations and one sham stimulation (stimulation that uses placebo effects). Real stimulation was incited on left DLPFC with a low current intensity of 2 mA for a time period of 30 seconds/session, whereas, the sham stimulation was only carried out for the first 30 seconds. Neurophysiological tests were generated during tDCS stimulation in which it was found that VRM tasks were significantly improved when tDCS was given over left DLPFC and temporal cortex. After which the tDCS was applied bilaterally over the temporal regions through anodal electrodes on the scalp. This was done for 5 days in a week and the current intensity was kept at 2mA for 30 mins a day. The results were observed after the 5-day treatment and it was found that VRM was actually enhanced to great extent, lasting for almost 1 month.

  1. tDCS improves Memory Conditions in AD patients:

Likewise, another experiment was carried out by Cotelli and colleagues. A therapy of anodal tDCS with computerized memory training was developed by them. 36 patients with AD were randomly assigned into three groups. The first group was made to go through anodal tDCS along with computerized memory training, while the second group was given placebo tDCS with computerized memory training. The third group was given motor training and anodal tDCS. The tDCS stimulation was generated for 5 days a week, 25 mins/day with a current intensity of 2 mA. The results showed remarkable improvement.

Most of these clinical experiments were small, so conditions and outcomes of stimuli were different, due to which the results also differ from study to study.

 

CONCLUSION

Studies are continuously investigating techniques of brain stimulation as a therapeutic treatment of AD. Although, some researches and experiments have given the best of the results, there is much more that still needs to be discovered. However, stimulations that enhance memory and cognitive memory are very effective and promising. Other than this, stimulations targeted at different regions of the brain combined with treatments like cognitive training are reported to produce more positive and good results. Although the field of tDCS is still immature, because it is safe, tolerable, and economic for patients with AD, the studies show that the use of tDCS have grown in decades.

 

References

Brain Stimulation in Alzheimer’s Disease. (2018, May 22). Retrieved from Frontiers in Psychiatry: https://www.frontiersin.org/articles/10.3389/fpsyt.2018.00201/full

Transcranial Direct Current Stimulation. (2017, march 31). Retrieved from Brain & NeuroRehabilitation: https://synapse.koreamed.org/Synapse/Data/PDFData/0176BN/bn-10-e4.pdf

Transcranial direct current stimulation for depression in Alzheimer’s disease. (2017, June 19). Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5477338/

Transcranial direct current stimulation improves recognition memory in Alzheimer disease. (2008, Aug 12). Retrieved from Neurology: http://n.neurology.org/content/71/7/493.short

Treatments | Alzheimer’s, ADHD, Autism, Brain Injury Treatment. (n.d.). Retrieved from The Neuro Cognitive Institute: http://neuroci.com/treatments/

Using transcranial direct current stimulation to treat symptoms in mild cognitive impairment and Alzheimer’s disease. (2017, oct 18). Retrieved from Future Medicine: https://www.futuremedicine.com/doi/full/10.2217/nmt-2017-0021

Using transcranial direct current stimulation to treat symptoms in mild cognitive impairment and Alzheimer’s disease. (2017, oct 18). Retrieved from NAtional Centre for Biotechnology Centre: https://www.ncbi.nlm.nih.gov/pubmed/29043928

 

 

Brain-Machine Interfaces In Biometric Verification

Brain-machine interfaces (BMIs) show continued promise for applications in neuroprosthetics, neurogaming, and brain-to-brain communication. One area of BMIs often overlooked is in biometric verification. Biometric verification relies on identifying persons based on their biological traits such as fingerprints, irises, faces, etc. Using either EEG (electroencephalography) or fMRI (functional magnetic resonance imaging), BMIs have shown to be effective in distinguishing the identity of subjects with a near-perfect classification rate.

To date, there have been a number of BMI biometric studies that make use of identifiable brain patterns to classify individuals. BMI biometric protocols offer idiosyncratic advantages to comparable biometrics, including a volitional requirement, continuous verification, covert warnings, universality, and multi-task authentication. However, a number of basic security questions remain unanswered in this young field. Can authorizing thoughts be mimicked? Are brain patterns of identical twins distinguishable? Is there a way to ensure brainwaves remain identifiable throughout a person’s lifetime? Before adoption of any mainstream BMI-biometric innovations, it may be wise to ensure the security of BMI biometrics is as strong as the initial research suggests.

1. BMI Biometrics: What do we know?

 

2. BMI Biometrics: What don’t we know?

 

3. Security Applications: Beyond Biometrics

4. Alternative Modalities in BMI Biometrics

 

  1.       http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4445586/
  2.       http://www.nature.com/nmeth/journal/v11/n6/full/nmeth.2936.html
  3.       http://www.ajol.info/index.php/amhsr/article/view/112158
  4.       https://www.cert.org/insider-threat/best-practices/
  5.       D&d – Need to find this citation still again
  6.       – Lost this citation too
  7.       http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=812278
  8.       http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=813403
  9.       http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4107575
  10.   https://www.researchgate.net/profile/B_Kumar8/publication/224194495_Subject_identification_from_electroencephalogram_(EEG)_signals_during_imagined_speech/links/54a97f8a0cf2eecc56e6c45f.pdf
  11.  http://www.theregister.co.uk/2014/12/29/german_minister_fingered_as_hackers_steal_her_thumbprint_from_a_photo/
  12.  http://www.sciencedirect.com/science/article/pii/S0921889014002632
  13.   S. Marcel, J. R. Millan. Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4):743–748, April 2007.
  14.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6199830&tag=1
  15.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7126357&tag=1
  16.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7314193
  17.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6994220
  18.  https://kar.kent.ac.uk/49535/1/PIN_ijcb.pdf http://www.inderscienceonline.com/doi/abs/10.1504/IJBM.2014.060960
  19.   http://www.researchgate.net/profile/Patrizio_Campisi/publication/282122165_EEG_Biometrics_for_User_Recognition_using_Visually_Evoked_Potentials/links/56039ec808ae08d4f171779c.pdf
  20.   http://www.sciencedirect.com/science/article/pii/S0925231215004725
  21.   http://link.springer.com/chapter/10.1007/978-3-319-19713-5_13#page-1
  22.  http://download.springer.com/static/pdf/440/chp%253A10.1007%252F978-3-642-27733-7_9145-2.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Freferenceworkentry%2F10.1007%2F978-3-642-27733-7_9145-2&token2=exp=1447987756~acl=%2Fstatic%2Fpdf%2F440%2Fchp%25253A10.1007%25252F978-3-642-27733-7_9145-2.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Freferenceworkentry%252F10.1007%252F978-3-642-27733-7_9145-2*~hmac=be7c889f4ec177bd4096057e765bb2169f3614dfd4323d2e28787a2e3b92393c
  23.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985
  24.  http://www.ncbi.nlm.nih.gov/pubmed/19163618   

http://www.igi-global.com.proxy.libraries.rutgers.edu/gateway/chapter/full-text-pdf/7471

 

  1.   http://www.metaljournal.com.ua/assets/Journal/MMI-6/060-Jianfeng-Hu.pdf
  2.   http://csee.essex.ac.uk/staff/palaniappan/1930378.pdf
  3.   http://ieeexplore.ieee.org.proxy.libraries.rutgers.edu/stamp/stamp.jsp?tp=&arnumber=5634487
  4.   – Need to find citation for this one as well  
  5.   Auditory potentials – https://etd.ohiolink.edu/!etd.send_file?accession=ucin1439300974&disposition=inline
  6.   http://ojs.bibsys.no/index.php/NIK/article/view/243
  7.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7280344
  8.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985
    1.   Facial movements, eeg authentication using artifacts http://link.springer.com/chapter/10.1007/978-3-319-07995-0_34#page-1
    2.   Multi-level approach based on eye-blinking http://www.sciencedirect.com/science/article/pii/S0167865515002433
    3.   http://www.nature.com/neuro/journal/v18/n11/full/nn.4135.html
    4.   DOI is good  http://www.nature.com/nphoton/journal/v8/n6/full/nphoton.2014.107.html
    5.   DOI vs fMRI – http://www.ncbi.nlm.nih.gov/pubmed/23578579

    38.    http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1619442

Reverse-Engineering the Brain

The brain is poorly understood, but adequately complex.

In the brain, sharp voltage spikes are propagated from neuron-to-neuron to communicate information (Hodgkin and Huxley [1952]). These voltage spikes are called action potentials. Action potentials can be elicited by a biological stimulus (such as eyes detecting light), or from the random fluctuation of ions crossing a neuron’s biological membrane (Diba et al. [2004]; Kole et al. [2006]; Faisal et al. [2008]). The noisiness of action potentials has led movement-based brain-machine interfaces to make use of only a small fraction of neurons that are well-correlated with a particular movement (Chapin and Nicolelis [1999]; Laubach et al. [1999]). The remaining neurons are a source of entropy. How the brain makes use of such a high level of noise is poorly understood (Dorval and White [2005]; Faisal et al. [2008]). A complete understanding would likely require detailed modeling on a molecular scale.

Today’s best recording implants can wirelessly record from hundreds to thousands of neurons (Schwarz et al. [2014]). For a brain-to-brain network to function optimally, stimulation and recording of neurons across multiple cortical layers must be engineered. Hundreds to millions of implantable, free-floating sensor nodes show promise for high-density, biocompatible brain recording (Seo et al. [2013]). Figure 1 depicts one recent pioneering innovation, aptly termed ‘neural dust’.

neuraldust

Figure 1: Ultrasound waves are used to interrogate implanted neural dust. Changes in dust composition are correlated with changes in electrical potential. Neural dust offers an effective means of extracting a large amount of information from multiple cortical layers, but may have insufficient resolution for full-scale brain recording. Theoretically, neural dust particles of a different piezoelectric composition could cause neural stimulation following electromagnetic interrogation, allowing for parallel recording and stimulation of the brain. Taken from Seo et al. [2013].

Action potentials can also be elicited through foreign electrical stimulation ([Hodgkin & Huxley 1952]). Clinically, artificial neural stimulation has proven to be an effective treatment for depression, bipolar disorder, schizophrenia, (McNamara et al. [2001]) and more (STX-Med [2014]).

In a binary model of a neuron, a neuron in the midst of an action potential is considered a ‘1’, while a neuron at rest is equivalent to a ‘0’. With greater than tens of billions of neurons in the brain, there are at least 210,000,000,000 possible states of the brain at any given moment. Given the brain is not truly binary, but non-Turing, modeling the human brain on a metabolic, molecular, and electrical scale remains a challenging computational problem (Yoosef et al. [2014]). Based on Intel’s BlueGreen experiments (Yoosef et al. [2014]), Moore’s Law indicates that it will take approximately 60 years until a computer may be capable of fully modeling a simplified, generic rat brain. Considering the significant variations from person-to-person in the upper cortex (Kelly et al. [2012]), it may never be possible to successfully model the brain of a living being.

In 2013, the world’s first brain-to-brain interface was constructed between two rats in the laboratories of Miguel Nicolelis (Pais-Vieira [2013]; Nicolelis [2015]). Sensory information was translated to motor action between one rat located in Durham, North Carolina, to another rat in Natal, Brazil. Critics of this experiment claim that information transfer rates between the two rats were not close to the information transfer rates of computers, or even modern brain-machine interfaces. This should not discourage research into brain-to-brain interfaces, but rather, signify a dangerous lack of inquiry into brain-to-brain interfaces relative to computer circuitry.

In the same year that the first brain-to-brain interface was constructed, U.S. President Barack Obama founded the BRAIN Initiative, a funding effort intended to boost the U.S. to the forefront of brain innovation (NIH [2014]). Unfortunately, our lack of understanding in neuroscience is correlated with a stark lack of funding compared to computational research. Even with the BRAIN Initiative in mind, when considering investment from both governments and private institutions, yearly U.S. funding into brain research is less than 10% of the hundreds of billions poured into computer science research (SFN [2011], Kennedy [2012], NIH [2014]). In order to reverse-engineer the brain at a rate similar to advances in computer science, these numbers must be flipped and exceeded. Cooperation is required from industry leaders, governments, and philanthropists to fund neurobiological and brain-machine interface research, particularly because the additional regulations and experimental time necessary for biological research will always exceed that of hardware and software research. So long as we continue to innovate computer circuitry and neglect biological integration, the computational abilities of machines will ultimately surpass humanity’s collective intelligence.

 

Reference: proof-of-cognition-implants , published May 2015. Disclaimer: Project Oblio’s mechanism does not rely on brain implants, but the mechanisms of action are the same. An early version of the paper provably exists in bitcoin address 13eeMVU5fXNfZdoBk5z4fEAbgSH9MawQ6H.

Benefits of EEG as biometric

What are the benefits of using EEG as the underpinning for a proof-of-individual network?

The use of biometrics, a pattern recognition system, for identification and verification of individuals, is now a widespread technology. With the shortcomings of classical biometrics like retinal scans and fingerprints among others, it has become necessary to come up with more innovative biometrics that are more secure. Bioelectric signals are an emerging candidate in this field. These are measurable electrical signals of low amplitude. Of interests among these are the electrocardiography and electroencephalography (Campisi, La Rocca & Scarano, 2012). This paper looks into the possible application of EEGs as a biometric and the benefits of its usage.

An EEG, or electroencephalogram, is a tracing of the electrical activity generated by the brain. Scientific research has shown that these wave patterns, produced by the electrical activity of neurons, are unique to individuals and can be used as a biometric (Knight, 2007). These waveforms are recorded through electrodes placed on the scalp. This confers the usage of EEG as a biometric its first advantage. The process of recording the electrical activity of the brain is non-invasive and thus a very safe procedure. Despite the cumbersome nature of the process, its non-invasive nature makes it easily applicable in person identification. In addition to the non-invasiveness of the procedure, the data collection process is largely simple. According to Palaniappan and Mandic (2007), once electrodes are placed correctly on the scalp, the waveforms are traced and the analysis is automated. The equipment is also portable making the process more appealing to use as a biometric.

 

Perhaps the greatest advantage of EEGs is its very confidential nature. Unlike biometrics like fingerprints and voice traits, an individual’s EEG corresponds to the individual’s mental tasks as dictated by neuronal connections and is very difficult to mimics. The EEG has been shown to be virtually impossible to copy. According to Paranjape et al., (2011) because it depends on the inner mental tasks unique to every individual, it cannot be reproduced by others. This makes the use of EEG effective in person identification. This security is increased by the fact that EEGs can be modified depending on the state of the subject (human detection). The resting EEG waveform is different from that obtained when the subject is mentally engaged and the pattern is different when eyes are open or closed and when open (Taguiam, 2017). This makes copying such information impossible. If this were not enough, the security is further boosted by the change in patterns when under stress. So, unlike fingerprints, retinal scans and voice patterns, an individual cannot be forced to reproduce their mental passphrase (Campisi, La Rocca & Scarano, 2012).

 

Finally, the use of bioelectrical signals for person identification can help in diagnosing certain abnormalities that may be indicated by an EEG during the person identification process. This confers an additional advantage to the field of medicine. Certain medical conditions including sleep disorders, convulsive disorders and lately tumors of the CNS may be diagnosed using EEGs (Paranjape et al., 2011). Use of EEGs might just increase the detection of such disorders based on incidental EEG findings.
In conclusion, while still under development, the use of EEG in biometric identification has myriads of potential benefits. High on this list is the very secure nature of EEGs as a biometric. Additionally, the collection of subject waveforms is non-invasive and very safe. The EEG machines are portable and their use is likely to increase early diagnosis of brain pathologies like convulsive disorders, sleep disorders, and even brain tumors.

 

References
Campisi, P., La Rocca, D., & Scarano, G. (2012). EEG for automatic person recognition. Computer, 45(7), 87-89.
Knight, W. (16th January, 2007). Brain Activity provides novel biometric key. New Scientist. Retrieved from https://www.newscientist.com/article/dn10963-brain-activity-provides-novel-biometric-key/
Palaniappan, R., & Mandic, D. P. (2007). EEG based biometric framework for automatic identity verification. The Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, 49(2), 243-250.
Paranjape, R. B., Mahovsky, J., Benedicenti, L., & Koles, Z. (2011). The electroencephalogram as a biometric. In Electrical and Computer Engineering, 2001. Canadian Conference on (Vol. 2, pp. 1363-1366). IEEE.
Taguiam, R. A. (Jan 24th 2017). Brainwaves Cab Be Passwords, Scientists Explain How EEG Authentication Works. Nature World News. Retrieved from https://www.natureworldnews.com/articles/35169/20170124/brainwaves-passwords-scientists-explain-eeg-authentication-works.htm

Preventing AI From Hacking Human Brains

Can we integrate brainwaves with a blockchain to prevent hacking by artificial intelligence?
Submitted to Nature on May 28th, 2015 

In the last issue of Nature (28th May 2015) a piece called ‘Robotics: Ethics of artificial intelligence’ raised awareness for the latest advances in intelligent machines and some of the possible consequences for society. Several reputed scientists commented on these advances and highlighted a series of solutions that are undoubtedly of major interest for any reader. Here we highlight recent advances in neuroscience that significantly blur the traditional boundaries between AI, computer science and neuroscience, but that will soon have major consequences for the society.

While AI traditionally works towards the goal of developing more advanced forms of computing, neuroscience research has been making significant advances in combining the activity of multiple brains to compute solutions for problems. For example, we have previously proposed that multiple interconnected brains may allow for new forms of computation (Nicolelis 2011, Cicurel and Nicolelis 2015) that cannot be achieved by Turing machines (Siegelman 1995). Following this initial insight, we and others demonstrated that living brains of rats (Pais-Vieira et al., 2013, Deadwiler et al 2013), monkeys (Ifft et al., 2014), and humans (Rao et al., 2014) can be interconnected to allow solving multiple different problems. These advances are quickly leading towards the more intricate reality of complex computation and multi-brain communication using Brainets (Ifft et al2014).

Brainets are defined as groups of interacting brains that cooperate towards a common goal (Nicolelis 2011). The recent developments observed in non-invasive brain stimulation and recording techniques, combined with the swift development of brain-to-brain interfaces, demonstrate that a world wide brain internet is no longer a far fetched idea. A fundamental problem for a society using a brain based world wide web would then be to prevent AI from hacking human brains.

One of us has recently proposed that the use of blockchains a future world wide brainet could prevent attacks from non-living entities (Mauro, 2015), and more broadly, from the Singularity (Kurzweill in Neuman 1958). Blockchains are networks were the history of each individual node can be traced and, based on its record, the weight of a specific node can be updated. An unweighted blockchain system is used to secure bitcoin transactions (Nakamoto, 2008), which prevents double spending of money. For brainet blockchains, the brain’s ability to both encode and decode information would ensure network security. First, the individuality and complexity of each brain activity would be used to encrypt information. Then, brain–to-brain communication combined with other individual markers (e.g. visual and tactile recognition) would ensure that only living, trustable nodes (i.e. brains) would be allowed to remain on the brainet. Attacks by AI would be chronicled on the blockchain, but neurological barriers to computation would prevent total AI takeover.

On a smaller network, brainet blockchains can be used to prevent attacks by lethal autonomous weapon systems (LAWS). The main fear regarding LAWS is that they will turn on their operators (Future of Life, 2015). For example, a LAWS designed to “eliminate all terrorists” may find that it can perform its job most effectively by eliminating those who have the authority to shut it down– namely, its operators. Brainet blockchains can automatically re-distribute authority when nodes are eliminated. The anonymity offered by advanced blockchain innovations would protect nodes before authority is re-distributed (Maxwell, 2013).

In conclusion, recent neuroscience advances are demonstrating first, that interconnected brains can perform multiple computational tasks, allowing for the appearance of a world wide brainet; and second, that such brainet could use blockchains to prevent attacks from non biological entities.

References
1 – Robotics: Ethics of artificial intelligence, Nature 2015, 28th May
2-Nicolelis 2011 Beyond boundaries
3-Siegelman 1995 Science
4-Pais-Vieira et al., 2013 BBI paper
5-Deadwiler et al., 2014?
6-Ifft et al 2014 sfn Abstract with monkey brainet (Arjuns paper?)
7-Rao et al., 2014
8-Mauro K, 2015 Grand Scholars Challenge
9-Kurzweill in Neuman 1958 Singularity
10-Nakamoto 2008

11-https://www.whitehouse.gov/blog/2014/10/09/brain-initiative-and-grand-challenge-
scholars

12-Maxwell, 2013 – https://bitcointalk.org/index.php?topic=279249
13-Future of life- : http://futureoflife.org/static/data/documents/research_priorities.pdf