Homomorphic Encryption Vs. Multiparty Computation

Bleeding edge encryption techniques allow one to monetize their personal health information without sacrificing privacy.

Encryption:

Encryption is the conversion of information into a code unreadable for unauthorized users by the use of an algorithm. Encryption is important when you don’t want anyone else to have access to your private data, such as brainwave data, selfie video data, or personal health data. There are many ways to compute (do math on) the encrypted data without knowing whose information it is about out of which, two are: Homomorphic Encryption and Multiparty Computation.

Homomorphic encryption is a kind of encryption in which the data is converted into a ciphertext which can be later analysed and worked on as it was still in its original form. The ciphertext is an encrypted version of the input data. It is operated on and then decrypted to obtain the desired output. This encryption allows us to perform complex mathematical operations on encrypted and secured data. It transforms one data set into another without harming the relationship between elements of both sets.

Multiparty computation is used to evaluate the inputs of two or more parties while keeping their inputs hidden from each other. This is done when different parties wish to jointly compute a function to their inputs in such a way that there certain security properties are preserved.

In simpler term, encryption allows us to hide data in a way that appears meaningless to anyone except those who have access to the secret decryption key. 

 

PROTECTION OF DATA THROUGH HOMOMORPHIC ENCRYPTION:

There have been many attempts to secure genomic privacy of biologically researched data using cryptographic methods. Particularly, it has been suggested that the privacy can be protected through homomorphic encryption.

The math on brainwave data recorded, of secret participants, using EEG while watching TV commercials, can be done through homomorphic encryption without decrypting the data.

The companies that get the brainwave data, never want to reveal the identity of their participants, that is why they send the samples in an encrypted form, to the lab, where the computations are done using homomorphic encryption, and the predictions (results) are sent back, to the company, in the encrypted form; where only they can decrypt it back using decryption keys. In this way, the identity of the person is never disclosed. The data is encrypted, also because companies and labs are bound by regulations and participant’s agreements to handle his data confidentially.

PROTECTION OF DATA THROUGH MULTIPARTY COMPUTATION:

Multiparty computation can be implemented using different protocols, such as Secret Sharing, in which the data from each party is divided and computed on separately. Then after combining again, it provides the desired statistical results. Security in multiparty computation means that the players’ inputs remain secured (except for the results that are computed) and the results computed are correctly. The security is supposed to be preserved In the face of any sort of adversary. Intuitively, no party learns about any other party’s inputs.

All in all, computation of encrypted data is an interesting topic that explains how cryptography faces the hardest problem of protecting data in use. This is just an overall review about what these two methods of computation have to offer us. The past few years have seen the most significant advances in making the use of these two technologies on more wider-scale.

 

For more information, see the project at OpenMined.org

 

 

References

BUILDING SAFE ARTIFICIAL INTELLIGENCE. (n.d.). Retrieved from open mined: https://www.openmined.org/

Computing Over Encrypted Data. (2017, may 29). Retrieved from Enigma: https://blog.enigma.co/computing-over-encrypted-data-d36621458447

homomorphic encryption. (n.d.). Retrieved from Search Security: https://searchsecurity.techtarget.com/definition/homomorphic-encryption

Homomorphic Encryption Market Size, Worldwide Analysis, Design Competition Strategies, Company Profile, Development Status, Opportunity Assessment and Industry Expansion Strategies 2027. (2018, june 7). Retrieved from 14 News: http://www.14news.com/story/38373250/homomorphic-encryption-market-size-worldwide-analysis-design-competition-strategies-company-profile-development-status-opportunity-assessment-and

Private predictive analysis on encrypted medical data. (2014, Aug). Retrieved from Science Direct: https://www.sciencedirect.com/science/article/pii/S1532046414000884

Secure multi-party computation made simple. (2006, Feb 1). Retrieved from Science Direct: https://www.sciencedirect.com/science/article/pii/S0166218X05002428

 

 

What is neuromarketing?

Neuromarketing enables one to substantially profit off the multi-billion dollar super-computer between their ears.

Neuromarketing is the science about your customer’s minds. It includes the direct use of brain imaging, scanning or other brain activity measurement technology to predict the subject’s response towards specific products, packaging or any other marketing element. It is the application of neuroscience to marketing.

Every year, billions of dollars are being spent on advertising campaigns. But conventional techniques have failed to predict how a customer feels when he is exposed to an advertisement. Neuromarketing offers cutting edge methods to know how a customer’s brain actually works and what effect does marketing have on the consumers’ population.

Neuromarketing researchers believe that consumers sometimes make subconscious decisions in a split of second. They believe that consumer’s decision can be driven through changing their emotions.

How Neuromarketing Works:

Knowing how an advertisement captures a consumer’s attention is what neuromarketing is all about. Research data is gathered by using certain biometrics that include:

  1. Eye tracking: tracking eye movement to understand which part of the advertisement is most appealing to the viewer.
  2. Facial coding: Testing facial expressions to learn certain responses about a product or an advertisement.
  3. Skin response and electrodermal activity: measures sweat gland secretions and different levels of excitement and arousals.
  4. Electroencephalography (EEG): measures electrical activity in the brain which is linked with increased or reduced focus and/or excitement levels.

Use Of EEG in Neuromarketing:

EEG biosensors makes the neuromarketing research easy. This allows the researchers to record consumer’s response in the right place such as movie theatre, bars etc. The biosensors can be placed on the head that accurately measure the brain activity of the subject. The changes in the electrical activity of the brain determines the emotional response of the person being tested, also whether he is engaged in watching the advertisement or not at all focused.

EEG can also reveal that the consumer was very attentive during the first 30 seconds of the advertisement but lost interest in the last 30 seconds. This feedback, in, turn, could better help in making the last 30 seconds of the advertisement even more effective.

Earning Money by Watching Advertisements and Using EEG:

This has also become a popular way of making money. This simple yet very beneficial tool can be used as a source of earning by using EEG to record your brainwaves while watching TV commercials and advertisements at home. People get paid by different companies and brands for selling their brainwave data. The general price paid per brainwave recording is between $80 and $100 per hour.

Different companies recruit people and pay them for watching their advertisements and recording their brainwaves by using electroencephalogram scans. It records on a second-by-second basis regarding how people respond to the commercial.

Neuromarketing has helped marketers make engaging and effective commercials. This not just benefits the marketers but the customers as well in enhancing their experience with a brand or product long before they consider buying it. This field is gaining unbelievable popularity among marketing and advertising professionals and is growing day by day.

 

 

References

Making Ads That Whisper to the Brain. (2010, nov 13). Retrieved from https://www.nytimes.com/2010/11/14/business/14stream.html: https://www.nytimes.com/2010/11/14/business/14stream.html

Neuromarketing and EEG: Measuring Engagement in Advertising. (2018, june 14). Retrieved from NeuroSky: http://neurosky.com/2016/08/neuromarketing-and-eeg-measuring-engagement-in-advertising/

Neuromarketing: Marketers scan consumers’ brains to test their ads. (2015, nov 5). Retrieved from CBC: http://www.cbc.ca/news/technology/neuromarketing-brainsights-1.3303384

Neuromarketing: The New Science of Consumer Behavior. (2011, jan 14). Retrieved from springer link: https://link.springer.com/article/10.1007/s12115-010-9408-1

What is Neuromarketing? (n.d.). Retrieved from Neuromarketing: https://www.neurosciencemarketing.com/blog/articles/what-is-neuromarketing.htm

 

 

 

What is neuroplasticity?

Is it true that neurostimulation helps your neurons make connections?

Neuroplasticity, also known as brain plasticity, is the ability of the brain to be adapted to any change in its surrounding throughout its life. From the time we are born until the day we die, the cells in our brain keep reorganizing according to our needs of change. Neuroplasticity is constantly at work throughout our lives. The connections within our brain are either becoming stronger or weaker. Younger people’s brains change more easily, because their brains are very plastic. Aged people lose their brain plasticity and become more firm and fixed in their thinking, learning and perceiving. In clinical context, the term neuroplasticity determines how quickly a patient recovers after a brain injury i.e. to regain independence to perform daily life activities (self-care, dressing, personal hygiene etc.)

 

It has been recognized that not all psychiatric and neurological behavioural indicators are solely because of abnormality, but because of alteration in the functionality of the brain regions. In this context, brain region becomes an important target of neuromodulatory interventions such as transcranial direct current stimulation. The advancement in neuroimaging techniques have made ways for us to non-invasively visualize different regions of the brain. tDCS has been used to improve various areas of cognitive functions. Some of them are briefly described ahead.

 

tDCS to improve learning and boost memory:

tDCS has been proven to be potentially beneficial in improving memory and learning in people with atypical brain development. With the help of several researcher’s work, it was proposed that tDCS when used on the right inferior frontal and right parietal cortex improved memory conditions. tDCS has also been reportedly said to improve language performance and word retrieval in people with language impairment.

 

tDCS to enhance motor skills:

In a randomized study, it was observed that tDCS could enhance motor skills in patients with chronic stroke. The transcranial direct current stimulation (tDCS) was positioned over the motor cortex (M1) (through anode) and contralesional forehead (through cathode) challenging fine motor skill task. The results showed significant increase in motor skills relative to any other treatment.

tDCS to treat Chronic Pain:

 

Different experimental research work done on patients with fibromyalgia and phantom limb pain suggested that tDCS had the capacity to upregulate and downregulate the functional connectivity of brain regions that are associated with motor, cognitive and pain processing. Patients with phantom limb pain were given anodal tDCS (applied over motor cortex) for over 5 consecutive days and they reported reduction in their pain.

 

tDCS to enhance athleticism:

A “Cycling Time to Task Failure Test” was conducted among several athletes in which it was revealed that participants who received anodal stimulation biked longer than those who received sham or cathodal stimulations. The researchers suggested that the better performance could be due to higher excitability of motor cortex leading to a decrement in effort and increment in endurance of the athletes.

 

tDCS to treat Alzheimer and other diseases:

It was reported that tDCS used on temporal cortex and left DLPFC enhanced VRM (Visual Recognition Memory) in patients with Alzheimer Disease. In different research work, it was noticed that tDCS also proved great in enhancing overall memory conditions of Alzheimer patients when applied bilaterally over the temporal regions through anodal electrodes on the scalp.

 

 

References

 

A brain network perspective on tDCS induced neuroplasticity: Single versus dual or multiple sites stimulation. (2017, march). Retrieved from science direct: https://www.sciencedirect.com/science/article/pii/S1388245716306897

IMPROVING NEUROPLASTICITY 

AND MOTOR LEARNING BY BRAIN. (2016, feb). Retrieved from https://pdfs.semanticscholar.org/536d/5ef2ae380aab59630ceeda72c54de0751ac8.pdf

Induction of Neuroplasticity by Transcranial Direct Current Stimulation. (2016, dec 1). Retrieved from NCBI: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5219570/

WHAT IS NEUROPLASTICITY? (n.d.). Retrieved from brain works neurotherapy: https://brainworksneurotherapy.com/what-neuroplasticity

 

Alternative Modalities in BMI Biometrics

While most medical applications of the EEG BMI require clean data, recorded artifacts from facial movements and eye-blinks have been shown to be exceedingly accurate in classifying individuals.

Historically, when a subject undergoing an EEG recording session blinks or twitches their face, a sharp spike appears on the recording trace that renders that epoch unusable. While most medical applications of the EEG BMI require clean data, recorded artifacts from facial movements and eye-blinks have been shown to be exceedingly accurate in classifying individuals.

In addition to facial movements, eye-blinks are now being integrated with EEG systems to achieve a higher classification rate than using brainwaves alone. In [34], it was discovered that using a cheap, consumer-version EEG headset (the Neurosky Mindwave) achieved a 99.4% classification rate by discriminating eye-blinks in addition to EEG brainwaves. This represents the highest classification rate for a significant subject size (31 persons) to date using an EEG instrument.  Eyeblinks may have some disadvantages relative to brainwave patterns (such as forced-volition and lack of continuous verification), and are likely to be more prone to mimicry, but may be easier to train. Integrated with the continuous verification protocols offered by EEGs, eyeblinks may provide a high-level of security when requested in discretized intervals.

An exciting new modality analyzes differences in neural connectomes derived from functional magnetic resonance imaging (fMRI) to identify individuals. Unlike EEGs, fMRI relies on imaging deep neuronal structures, often in a clinical setting. The neural connectome is a diverse, complicated web of linkages among neurons inside the brain, which varies both with experience and genetics. In [35], connectomes constructed in the Human Connectome Project identified persons in a group of 126 persons with high accuracy (approximately 93%). It was noted that analyzing just the frontoparietal network was more distinctive across subjects compared to analyzing the whole brain (p < 10-9), and that these ‘neural fingerprints’ did not change when a person was at rest or performing a task. A person’s fMRI data from one session was used to create a connectivity matrix of their neural connections. This connectivity matrix was then compared to all other connectivity matrices from the remaining sessions, and a correlation coefficient for each session was calculated.

Disadvantages of using fMRI as a biometric is that the subject must remain stationary, it is contraindicated in subjects with implanted metallic devices, and it is significantly more expensive than other methods of biometric verification. However, conclusions drawn on fMRI data are likely to hold true on developing mobile technologies, such as DOT (Diffuse Optical Tomography) [36]. DOT has a greater temporal resolution than fMRI, allowing for continuous verification. However, it may be more dependent on detector placement than EEG [37].

 

Citations:

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

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

 

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

 

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

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