Using tDCS/tACS to Treat Post-Traumatic Stress Disorder (PTSD) in Veterans

What is PTSD?

PTSD is a mental condition of anxiety that is triggered by a terrifying event, either by experiencing it or by witnessing it. Traumatic events that trigger PTSD may include accidents, natural-human caused disasters, personal assaults etc. Several effective treatments, such as antidepressants as serotonin-specific reuptake inhibitors (SSRIs) and Cognitive Behavioural Therapy have been identified as treatments for PTSD, however even with these a number of patients continue to experience  symptoms. This means that treatment for chronic PTSD is still inadequate. The neuroscience has revealed that patients suffering from PTSD have altered functioning within several brain regions. Researchers say that a non-invasive electrical brain stimulation is a very effective treatment for Post Traumatic Stress Disorder (PTSD) by seemingly correcting the dysfunctional brain parts. It is believed that this, in turn, results in relief of PTSD symptoms.

The non-invasive transcranial Direct Current Stimulation tDCS has been experimented to treat many mental conditions like schizophrenia, depression, obsessive compulsive disorder, stroke and many more. When we talk about PTSD, one of the main problems with this disorder is the inability to escape fearful thoughts. Such as flashbacks of a friend being killed in a car accident. Such flashbacks can aggravate PTSD Symptoms which may also include anger, insomnia, nightmares and irritability.

tDCS decreases Emotional Arousal and Fear in PTSD patients:

Several studies were carried out that involved the use of tDCS to treat PTSD and their results were carefully observed.

During tDCS, a low-intensity current enters through one electrode and leaves through the other. The neurons, under the electrode where the current enters the body, become more likely to send signals, and under the electrode area where the current exits the skin, neurons are less likely to send signals.

A study involved 28 people suffering from PTSD. The researchers took help of an event marked by conditioned reflex, in which the patients predicted an unpleasant event after seeing a neutral stimulus. Coloured lights were marked as neutral stimulus and the unpleasant event attached to it was a harmless but highly annoying current to the fingers. The researchers put the electrode, through which current enters the skin, over a region which plays an important role in extinction learning and memory called ventromedial prefrontal cortex. The purpose was to make sure that those neurons fire off more likely to see if this improved extinction learning or the ability to predict an annoying event. At first, the 28 patients were made to see a colored light in a room, and, simultaneously, were given electric shock. Later, the patients were shown the coloured light without applying the electric shock. The later event is what we call extinction learning; the process where one learns that certain situations no longer anticipate an annoying event.

Fourteen patients received 10 minutes of tDCS just when they were experiencing extinction learning. The other 14 were given tDCS just after they underwent extinction learning, a time period known as extinction consolidation, when the information is being fed into the memory. After 24 hours, all of these patients were tested if they remembered the electric shock or not.

The results showed that the 14 veterans who received stimulation during the time of extinction consolidation showed slightly less perspiration on their hands (which was a sign of less fear/emotional arousal) than those who experienced the tDCS during extinction learning. An increase in hand sweat showed how well the patients had learnt and remembered that seeing colored light will result into a very unpleasant shock to their fingers.

It could be taken as giving the brain a little boost when people learnt that the colored lights no longer predict an electric shock and store that learning into memory, so people can better remember that they don’t need to fear the lights any longer.

For tDCS to be more effective, it is very important to control what the brain is doing during tDCS. That is why people were stimulated when they were doing an experimental task of extinction learning or consolidation of learning.

 

References

Brain stimulation technique shows promise in reducing fear in Veterans with PTSD. (2017, december 

9). Retrieved from US Department Of Veteran Affairs: https://www.research.va.gov/currents/1117-Brain-stimulation-technique-shows-promise-in-reducing-fear-in-PTSD.cfm

Current Status of Transcranial Direct Current Stimulation in Posttraumatic Stress and Other Anxiety Disorders. (2016, april 2). Retrieved from NEURAL ENGINEERING GROUP: https://www.neuralengr.org/uncategorized/new-paper-prospects-of-tdcs-for-ptsd/

Non-Invasive Br

ain Stimulation for Post-Traumatic Stress Disorder . (n.d.). Retrieved from Grantome: http://grantome.com/grant/NIH/R21-MH102539-02

Post-traumatic Stress Disorder Treatment Using Transcranial Direct Current Stimulation (tDCS) Enhancement of Trauma-focused Therapy. (n.d.). Retrieved from Smart Patients : https://www.smartpatients.com/trials/NCT02900053

tDCS improves behavioral and neurophysiological symptoms in pilot group with post-traumatic stress disorder (PTSD) and with poor working memory. (2014, feb 28). Retrieved from Taylor and Francis Group: https://www.tandfonline.com/doi/abs/10.1080/13554794.2014.890727

 

 

 

Treating chronic pain with non-invasive neurostimulation

Can non-invasive neurostimulation help treat chronic pain?

Chronic Pain:

Chronic pain is that pain which lasts beyond the time of one’s expected healing. Many patients e

xperience continuous pain despite having conventio

nal treatments like injections, medical and physical therapy, surgery etc. Non-invasive brain stimulation is gradually becoming a popular tool as an alternative treatment of chronic pain syndromes. tDCS has been explored in a variety of pain population with various chronic pain syndromes such as multiple sclerosis, central pain due to spinal cord injury, fibromyalgia, headaches, neuropathic and post-operative pain etc. It may non-invasively modulate cortical areas related to sensation and pain representations.

Recent evidences suggest that tDCS interacts with several neurotransmitters in the brain, such as serotonin, acetylcholine, dopamine. It also brings about changes in brain-derived neutrophic factors that deal with process of pain. It alters the 

way the nervous system send messages, for example pain messages t

hat the nervous system sends when nerve cells are damaged. Furthermore, it is also said that tDCS can upregulate and downregulate the functional connectivity of brain regions that are associated with motor, cognitive and pain processing.

Effects Of TDCS On Chronic Pain In Spinal Cord Injured Patients:

Sixteen spinal cord injured patients were randomly allocated to active or sham treatm

ent groups. tDCS was administrated by placing the anode over the dominant M1 and cathode over the contralateral supra orbit scalp area. Patients received either sham or active treatment for 5 consecutive days and 20 minutes daily.

In result, no adverse effects of the treatment were seen, while treatment seemed to have reduced the pain scores on VAS.

Effects Of TDCS On Chronic Pain In Fibromyalgia Patients:

48 female patients with (45 females having) fibromyalgia were randomly investigate

d with the results of 2 mA anodal tDCS given for 5 consecutive days, 20 minutes each day. Changes in pain, stress, daily functioning and psychiatric symptoms were observed. A small but significant improvement was seen under the active tDCS treatment. Fibromyalgia related daily functioning was improved. The stimulation was also well tolerated by the patients. And no adverse effects were observed.

This study suggests that tDCS has the potential to induce pain relief in patients suffering from fibromyalgia, without any adverse effects.

Effects Of TDCS On Chronic Pain In Phantom Limb Pain Patients:

Eight patients with unilateral lower and upper limb pain were enrolled and were given anodal tDCS (applied over motor cortex) for over 5 consecutive days, 15 minutes each day. tDCS induced a sustained decrease in phantom limb pain. Moreover, the patients reported a relief in pain each day along with a better condition to move their phantom limb.

The results showed that a 5-day treatment of motor cortex stimulation with tDCS can induce stable relief from Phantom limb pain.

tDCS is a unique and fine treatment to treat chronic pain. The intensity of current used in tDCS is so low that it cannot be felt while it is applied to the skull. The studie

s have shown that tDCS affects variety of brain area in a positive way. tDCS polarizes the brain cells under the electrodes and then alters the way the brain sends and receives messages. It is believed that this polarization can reverse the abnormal brain excitability responsible for pain.

References

A New Treatment for Chronic Pain. (n.d.). Retrieved from Headache and pain. a centre of palm beach: http://www.palmbeachpain.com/new-pain-treatments/41-a-new-treatment-for-chronic-pain.html

Effects of Transcranial Direct Current Stimulation (tDCS) on Chronic Pain in Spinal Cord Injured Patients. (2017, march 22). Retrieved from Spine Research: https://spine.imedpub.com/effectsof-transcranial-direct-current-stimulation-tdcs-on-chronic-pain-in-spinal-cord-injured-patients.php?aid=14974

Evidence-based review of transcranial direct current stimulation (tDCS) for chronic pain syndromes. (2017, march-april). Retrieved from brain stimulation: https://w

ww.brainstimjrnl.com/article/S1935-861X(17)30196-1/fulltext

Immediate and Sustained Effects of 5-Day Transcranial Direct Current Stimulation of the Motor Cortex in Phantom Limb Pain. (2015, april 18). Retrieved from NCBI: https://www.ncbi.nlm.nih.gov/pubmed/25863170

Stimulating the brain without surgery in the management of chronic pain in adults. (2018, april

 13). Retrieved from cochrane: http://www.cochrane.org/CD008208/SYMPT_stimulating-brain-without-surgery-management-chronic-pain-adults

Transcranial direct current stimulation as a treatment for patients with fibromyalgia: a randomized controlled trial. (2015, JAN). Retrieved from NCBI: https://www.ncbi.nlm.nih.gov/pubmed/25599302

 

 

tDCS/tACS to treat migraines

Are you suffering from migraines? Try something new.

What are migraines?

Migraines are severe, recurring headaches. Typically these headaches affect only one half of the head. They are pulsating in nature and can last from about two to 72 hours. Associated symptoms with migraines may also include sensitivity to light, sound and smell, nausea and vomiting etc. Normal recommended treatments include pain medication such as paracetamol and ibuprofen. Approximately 15% around the world suffer from migraines and apparently, no effective solution has been found.

Effective Treatment of Migraine; tDCS:

Recently, mu

ch importance has been given to transcranial direct current stimulation that alters the mechanism underlying the cortical excitability which is said to have become dysfunctional during migraine. tDCS have been reported to be safe and effective tool in dealing with the cortical excitability, activation and plasticity In migraine.

Experiments on Migraine Using tDCS:

Thirteen patients, with chronic migraine, were randomized to get active and sham tDCS of 2 mA for 20 minutes over 4 weeks. These patients received over 10

 session of stimulation during this time period. The data for results was collected before, during and after the treatment. A significant improvement was seen in the follow up period in the active tDCS groups only. Co

mputational model studies showed that the current flew into different regions of the cortical and subcortical that are highly associated with the migraine pain. The current was also generated in thalamus, cingulate cortex, insula and brainstem regions.

Differ

ent studies have shown that patients with chronic migraine pain have a positive response when tDCS is directed towards the anodal motor cortex. These effects may be related to electrical currents induced in pain-related to cortical and subcortical regions.

Another study, including 13 patients with chronic migraine, used tDCS as a preventive migraine therapy. After 10 sessions, the patients reported a 37% decrement in their pain intensity. But the symptoms kicked in after four weeks of treatment. The assistant professor of the study said that it was important that repetitive sessions were arranged to revert ingrained changes in the brain related to migraine.

Other studies have reported that stimulation of the motor cortex decreases the chronic pain. However, this study provided the first known mechanistic proof th

at tDCS over the motor cortex might work as a successful precautionary remedy in complicated and complex, chronic migraine cases, where attacks are more constant and flexible to traditional treatments.

This powerful method of brain stimulation and modulation has determined compelling results in different kinds of chronic pain, and has proved to be more eff

ective regarding enhancing the pain tolerance than other forms of transcranial stimulation. tDCS has promising effects for the medication and treatment of chronic pain disorders, including other of its amazing features such as small portable size, economic cost, and capability to provide a more stable placebo condition.

 

Refer

ences

Brain stimulation in migraine. (n.d.). Retrieved from NCBI: https://www.ncbi.nlm.nih.gov/pubmed/24112926

Migraine patients find pain relief in electrical brain stimulation. (2012, april 19). Retrieved from mechigan news: https://news.umich.edu/migraine-patients-find-pain-relief-in-electrical-brain-stimulation/

tDCS for Migraine Headache. (n.d.). Retrieved from The Brain Stimulation Clinic: http://www.transcranialbrainstimulation.com/Migraine

tDCS-Induced Analgesia and Electrical Fields in Pain-Related Neural Networks in Chronic Migraine. (2012, april 18). Retrieved from NCBI: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4166674/#R7

Transcranial Direct Current Stimulation (tDCS) of the visual cortex: a proof-of-concept study based on interictal electrophysiological abnormalities in migraine. (2013, march 11). Retrieved from Springer link: https://link.springer.com/article/10.1186/1129-2377-14-23

 

tDCS/tACS to treat insomnia

Suffering from insomnia? Try a treatment that's shown some efficacy, but needs more research from contributors like you.

What causes insomnia?

A person suffering from insomnia has trouble in falling and staying asleep. Insomnia might occur due to physical or psychological stress, or may be a side effect of a pharmacological medication. It is considered that cortical activity, when pathologically altered, leads to insomnia. This regulation of cortical activation follows circadian rhythms that allow the transition between sleep and wakefulness.

Fortunately, studies have shown that neuromodulation through non-invasive brain stimulation in the form of transcranial direct current stimulation can alter cortical excitability and can be used to probe effects on different parameters of sleep. It has been shown that tDCS has the ability to cause modifications in EEG parameters of a person’s sleep and wake such as synchronization.

How does tDCS work?

In tDCS, the current that flow in the brain is triggered through a positive and a negative electrode (anode and cathode, respectively). The basic mechanisms of tDCS include polarization of neuronal membranes under the electrodes placed on the skull. Anodal and cathodal tDCS show antagonistic effects on cortical excitability. Anodal stimulation increases cortical excitability, whereas, on the other hand, cathodal tDCS decreases cortical excitability. Hence, the placing of the anode over a particular target cortical area of the brain is capable of modifying the excitability of this area by rising depolarization of cortical neuronal cells.

Experiments on Insomnia Using tDCS:

In an experiment carried out by Lukas Frase and colleagues, the effects of two different tDCS parameters and a sham stimulation on the sleep cycle of 19 healthy participants was compared.

Bi-frontal anodal stimulation, seemed to increase the arousal, and consequently, decreased the total sleep time in comparison to the other two interventions. Bi-frontal cathodal stimulation, expected to decrease arousal, did not increase the total sleep time, may be because there is a ‘ceiling’ or limit after which the good sleepers do not sleep any more. EEG analysis finally proved that the anodal stimulation increased the arousal, while cathodal stimulation did the other way and decreased the arousal.

It was, at last, concluded that by using anodal tDCS total sleep time can be decreased. The researchers hope this knowledge can contribute to future treatments for disturbed arousal and sleep.

Another research study comprising of 26 neuropsychiatric patients ( with stroke, dysphagia, pain, hereditary spastic paraparesis, Parkinson’s disease, aphasia, depression) were made to go through tDCS treatment. tDCS montage for each pathology was different. The current intensity of the stimulation was kept at 2mA and was delivered for 5 consecutive days, 20 min per day. The sleep quality at baseline (T0) and after the tDCS treatment (T1) was assessed.

Despite of the fact that the sample size was small and different tDCS montages were used, data from the observational study showed that anodal tDCS for five consecutive days enhanced the quality of sleep and improved its efficiency.

tDCS could be a non-invasive and valuable new tool for managing sleep disorders. Researchers that studied the total sleep time and other sleep disturbances propose that tDCS may be potentially beneficial to modulate cortical activity linked with insomnia and to adjust sleep adequacy.

References

Modulation of Total Sleep Time by Transcranial Direct Current Stimulation (tDCS). (2016, may 4). Retrieved from Neuropsychopharmacology: https://www.nature.com/articles/npp201665

TDCS Can Change Sleep Duration. (2016, october 7). Retrieved from bipolar news: http://bipolarnews.org/?p=3884

The Modulatory Effect of Sleep on tDCS. (2017, sept). Retrieved from http://epubs.surrey.ac.uk/845444/1/FINAL%20THESIS_James%20Ebajemito.pdf

Transcranial Direct Current. (n.d.). Retrieved from SCIENCE MEDICAL CENTRE: https://www.jscimedcentral.com/SleepMedicine/sleepmedicine-3-1060.pdf

Transcranial direct current stimulation improves sleep in patients with post-polio syndrome. (2013, aug 26). Retrieved from Science Daily: https://www.sciencedaily.com/releases/2013/08/130826143724.htm

 

 

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

 

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