Benefits of EEG as biometric

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

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

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

 

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

 

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

 

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

Preventing AI From Hacking Human Brains

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

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

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

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

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

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

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

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

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

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

Brain-Generated-Private-Keys (BGPK): Neural States as a Method for Private Key Generation

Brain-generated private keys offer a source of randomness that's far less likely to be snooped on than computational random number generators.

Private keys are essentially very random passwords used in cryptographic algorithms. They can be generated through a recording sample taken from a brain.  Brain-generated passwords are ideal because an individual’s connectome may show sufficient variation to prevent a hacker from all-out network attack (Kelly et al. [2012]). The far greater number of states in the human brain (2^210,000,000,000) versus typical cryptographic algorithms (2^256) are encouraging for this endeavor. If a (quantum) computer were ever powerful enough to guess a private key generated by an implant-stored algorithm, the network would collapse.

To evaluate the potential of neurons for private key generation, recordings from 384 to 640 neurons were taken from rhesus macaque primates during a center-out task (Li et al. [2009]). Data was sorted into 10ms and 100ms bins. A neuron that had fired within the bin window was given a ‘1’, while those that were quiet were given a ‘0’. For each time window, the neurons were randomly sorted into groups of 32, constructing a 32-bit number based on firing state. Over 14 million 32-bit numbers were generated in this manner. After excluding 0 (the 32-bit number), each of the 14 million numbers were unique.  However, the greater prevalence of the ‘0’ bit-state (quiet neurons) resulted in a non-uniform distribution (Figure 6), invalidating this rudimentary method as a means for private key generation.

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Figure 6: The top panel depicts a histogram for the distribution of the total dataset of 32-bit numbers, sorted into 300 bins. The middle and bottom panels depict histograms for two particular days of recording, sorted into 300 bins. The non-uniform distribution in each case indicates a vulnerability in private key generation.

While only 32-bit numbers are utilized here, fully uniform 32-bit numbers can be concatenated to create larger numbers, and thereafter, significant computational barriers for private key guessing. It is important to use a dataset as uniform as possible, or else attacks can be utilized which make use of the most commonly-generated numbers. Simplifying to the 32-bit-state allows for increased sample size and more efficient data processing.

A second approach utilized a combination of biological and computational means. Using MATLAB’s random number generator, a neuron that had been given a ‘0’ bit-state had a 25% or 35% chance of being altered to a ‘1’. Figure 7 depicts a new level of uniformity of the dataset. While the data is increasingly uniform, the partial reliance on a random number generator is somewhat concerning. Fortunately, this combination is much more secure than simply using a random number generator on its own. With the neurons partially-generated by a persons brain, the advantages to individuality are maintained without full reliance on a random number generator.

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Figure 7: a) Full distribution for neurons that had a 25% chance of changing their bit-state from a ‘0’ to a ‘1’. c) Full distribution for neurons that had a 35% chance of changing their bit-state from a ‘0’ to a ‘1’. It is probable that the “sweet spot” lies somewhere between 25% and 35%.

A third approach was again purely biological. This time, the number of times a neuron had fired in a particular bin was used to influence the bit-states in the neuron’s past bins. In Figure 8a, if a neuron had fired greater than twice in a particular bin, the previous three bins for that neuron had their bit-state set to ‘1’. In Figure 8b, if a neuron fired x times in a particular bin, x previous bins were set to ‘1’. There again appears to be increased uniformity when just comparing these two mechanisms, but not enough for private-key generation. It is possible that a variant of this mechanism could be used, however, the storage and manipulation of past neural recordings offer a potential security vulnerability, as a hacker may be able to edit the stored bit-states to a private key of their choosing. Real-time private-key generation is preferred for optimal security.

graph3

Figure 8: a) Full distribution for active neurons that, after firing at least three times, had their previous three bins changed from a ‘0’ to a ‘1’. b) Full distribution for neurons that had x previous bins set to ‘1’, where x is the number of spikes in a particular bin. In this case, bins were not “changed” from 0 to 1 – if a neuron already had a ‘1’ in a previous bin, the bin was included in the x count. Comparison between a) and b) shows increased uniformity.

graph4

Figure 9: a) Full distribution for outputs in which the results of two random neurons were merged. If either neuron had at least one spike, a ‘1’ was recorded, otherwise a ‘0’ was recorded. b) Full distribution for a similar case in which three neurons were used. There appears to be slightly more uniformity in the 3-neuron case, indicating that grouping neurons could produce increased uniformity.

A final approach merged neurons. If either of two neurons had a spike, a ‘1’ bit-state was recorded. A similar method was used for three neurons. Outside the first peak in Figure 9, there appears to be increased uniformity elsewhere in the dataset. This is promising because merging neurons offers no potential security vulnerabilities. When recording from 1,000 neurons, 10 neurons may be merged to offer a possibility of 2100 bit-states. This still exceeds the 277 offered by most private-key generation algorithms. As the number of recorded neurons increases, one can expect merged neurons to play a greater role in private-key generation.

This paper explored a simple binary model in a small number of neurons. The simple, real-time characteristics of this method is enticing because it allows more time for spike sorting and does not “hide” potential vulnerabilities behind a complex algorithm.  However, utilizing a stored random number generator may be a security vulnerability (Figure 7), as is minor data storage (Figure 8). Further research into merging neurons (Figure 9) should be conducted to determine whether a fully-uniform dataset can be constructed. It is anticipated that as the number of recording neurons increases, so will the ability to generate private-key generation algorithms from neurons. Note that specific motor movements did not consistently elicit particular private keys. This can be explained by the immense amount of noise rampant within the brain.

  1. Conclusion

Encapsulated within each of us is a relatively untapped, multi-billion dollar supercomputer. Our brains are capable of performing particular tasks with a power-efficiency and computing-strength greater than any arrangement of hardware. They are also incredibly talented at recognizing what makes us human, offering potential for a proof-of-cognition protocol to tie digital identities to biological ones. Additionally, an identity-based blockchain offers a safer means of data communication than a centralized server, which can be hacked. Unfortunately, our understanding of the brain lags significantly behind our understanding of machines. Further research into neural engineering may prove that a brain-to-brain network will exponentially increase humanity’s collective intelligence.

 

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

Proof-of-Work Versus Proof-of-Cognition

Can a proof-of-work (POW) protocol substitute or supplement a proof-of-cognition (POC) protocol?

Can a proof-of-work (POW) protocol substitute or supplement a proof-of-cognition (POC) protocol? It is possible, but not ideal. Humans in a POC protocol have equal mining power, instead using human biology to secure human conscience. If mining power were unequal (as is the case with POW), human consciences could be manipulated a debatably far worse outcome than a simple double spend in a currency system. By relying on machines rather than biology, the network can be overpowered by artificial intelligence producing their own mining hardware, or re-routing existing mining power to reap digital currency rewards (BGP hijacking). Furthermore, miners in a POW protocol are motivated by currency rewards for honest mining. Human-to-human transactions would need to be made feeless if humans were to continuously verify each other. Determining which verification webs were human-based would be difficult or impossible from a POW miner’s perspective.

Can POC replace POW? The simplest reason it cannot is that miners in a POC protocol would be able to inject bad blocks into the network, sending themselves currency when they had previously had sent it elsewhere. Since POC trades a valueless data structure, this risk is nonexistent.

Proof-of-work is ideal for currency, while proof-of-cognition is ideal for identity.

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.

Services in a Brain-to-Brain Internet

The below article is intended for a very futuristic use case, involving implants. You can study actual, ready BTB services at our github.

Future plans: Imagine a service called YouLive. Rather than sharing user-uploaded videos (as YouTube does), YouLive would share user-uploaded experiences. In such a system of shared experience, care must be taken to ensure that a user’s downloaded experience does not exceed the length of the recording, and that the downloaded experience is of the quality expected by the receiver. A user’s brain must be fundamentally protected from becoming ‘trapped’ in another (potentially ill-acting) user’s experience.

On a POC blockchain, safety is ensured by publicly-announcing connections between humans and services. The value or script of the transaction would be signify the length of time a connection was assumed to be valid. A human would confirm a connection time after receiving a service’s proposal. Once a connection was terminated, a human would then send a second transaction to signify they had been disconnected. The publicized initiation and termination of a connection is crucial to the safety of the network. If a connection was not terminated after the specified time, users on the network would notice on the blockchain, and could remove the ill-acting service from the network, or simply leave the network themselves. Once again, the decentralized nature of blockchains offer a safer means for these broadcasts than a centralized server, the latter of which can be hacked. Thus, a blockchain is absolutely necessary as a foundation for brain-to-brain security.

In the bitcoin protocol, a small number of ’emergency broadcast keys’ are given to core developers to alert users in the case of network failure. These keys could be used to automatically disconnect users in the event a number of downloaded thoughts or service connections did not receive their termination transactions.

In the case of human-service handshakes, a minuscule digital currency fee would be incurred to allot for the cost of blockchain data storage, as well as to help support the centralized structure hosting the experiences. If the service were decentralized (which it should be), a portion of the digital currency fee could be sent to the user who uploaded the experience, or whatever other feature the service offered. This fee would be necessary as an incurred cost for network spam, and relatedly, would ensure human nodes with a poor level of trust could still have their human-service transactions posted on the blockchain.

The protocol for a brain-to-brain internet outlined here is one that is discontinuous. Downloaded thoughts and service connections would only be utilized for an agreed-upon length of time. Code signing (Kiehtreiber and Brouwer [2006]) would ensure that downloaded thoughts were unaltered, similar to how file downloads on the internet are secured. Continuous, live stimulation of neurons will probably never be a safe mechanism for brain-to-brain communication, though refreshable service connections may allow for continued data sharing.

 

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.

Transcranial Direct/Alternating Current Stimulation in Boosting Memory

Noninvasive brain stimulation techniques are gaining attention due to their safety in modulating brain dynamics. Promising applications include treatment of various central nervous system diseases and improvement in cognitive functions. Transcranial electrical stimulation provides noninvasive brain modulation using direct current, alternating current and random noise stimulation (Paulus, 2011).

A weak direct electrical current is applied through the scalp using two or more electrodes in the  transcranial direct electrical stimulation tDCS technique. This induces brain excitability through cathodal hyperpolarization and anodal depolarization (Paulus, 2011). The induced effects depend on polarity, duration and intensity of electrical stimulation.

Transcranial alternating current stimulation is same as direct current in case of low intensity and electrodes. But it uses sinusoidal current through the scalp for electrical stimulation (Woods 2011). Several studies have been carried out that showed that transcranial direct/alternating current has a positive impact on motor, memory, perception and cognitive functions.

Cognitive processes include perception, memory, learning, and long-term memory formation. Induction of transcranial electrical brain stimulation enhances the cognitive functions. Direct current stimulation occurs through spontaneous cortical activity and alternating current modulate cognition by interfering with the oscillations of cortical networks (Kuo, 2012)

Transcranial stimulation of the brain through weak direct current induction serves a non-invasive and painless technique (Nitsche, 2000). In their study, induction of direct current through the scalp for modulating motor cortex excitation, showed up to 40% of the excitation changes that last for several minutes after end of stimulation. Stimulation was achieved by membrane polarization inducing anodal stimulation and inhibiting cathodal stimulation.

Weak direct current induction leads to cerebral excitability. Fregni et al (2005) evaluated “the effect of anodal stimulation of dorsolateral prefrontal cortex (DLPFC) on working memory”. A letter-based working memory task was performed by fifteen individuals during anodal stimulation of DLPFC. Out of these seven performed the same task but with cathodal stimulation. Results showed the increased performance of individuals with anodal stimulation.

Another study was carried out to investigate the association of slow oscillations on memory during sleep. Induction of transcranial slow oscillations of 0.75 Hz in early sleep increased the retention of declarative memory in healthy subjects and also improved the slow wave sleep and slow spindle activity in the frontal cortex. Stimulation of the brain by 5Hz oscillations during rapid eye movement sleep had no effect on declarative memory (Marshall, 2006). Based on this, another study was carried out to rule out the effect of slow oscillations during waking on brain and memory encoding. It was concluded that the effect of oscillation and memory depend on brain state, as when the awake brain transmitted stimulation by responding to oscillations and facilitated encoding. Transcranial oscillations didn’t improve memory when applied after learning, while it showed enhanced encoding of hippocampus dependent memory when induced during the process of learning (Kirov, 2009)

Transcranial alternating current stimulations have an enhanced effect on human cognitive functions. Antonenko et al (2016) conducted research on young and older healthy individuals. Transcranial alternating current of 6Hz was applied to the brain for 20 minutes during a language learning process. The results were in support of the evidence that alternating current improves human cognition through direct stimulation of task-related brain oscillations.

 

References

Antonenko D, Miriam Faxel, Ulrike Grittner,Michal Lavidor and Agnes Flöel, 2016. “Effects of Transcranial Alternating Current Stimulation on Cognitive Functions in Healthy Young and Older Adults”. Neural Plast: 4274127. Doi: 10.1155/2016/4274127. PMCID: PMC4889859. PMID: 27298740

Fregni, F, Boggio, P.S, Nitsche, M. et al, 2005. “Anodal transcranial direct current stimulation of prefrontal cortex enhances working memory”. Exp Brain Res: 166: 23. https://doi.org/10.1007/s00221-005-2334-6

Kirov R, Carsten Weiss, Hartwig R. Siebner, Jan Born, and Lisa Marshall, 2009. “Slow oscillation electrical brain stimulation during waking promotes EEG theta activity and memory encoding”. PNAS; September 8, 2009. 106 (36) 15460-15465; https://doi.org/10.1073/pnas.0904438106

Kuo M F, Michael A. Nitsche, 2012. “Effects of Transcranial Electrical Stimulation on Cognition”. Clinical EEG and Neuroscience, Volume: 43, issue: 3, page(s): 192-199. https://doi.org/10.1177/1550059412444975

Marshall, L., Helgadóttir, H., Mölle, M., and Born, J. (2006). “Boosting slow oscillations during sleep potentiates memory”. Nature 444(7119):610-3. doi: 10.1038/nature05278

Nitsche MA, Paulus W, 2000. “Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation”. J Physiol. 2000 Sep 15; 527 Pt 3:633-9. PMID: 10990547 PMCID: PMC2270099

Paulus W, “Transcranial electrical stimulation (tES – tDCS; tRNS, tACS) methods”.Neuropsychol Rehabil. 2011 Oct; 21(5):602-17. Doi: 10.1080/09602011.2011.557292. Epub 2011 Aug 5.

Transcranial Stimulation In the Treatment of Depression And Mood Improvement

The largest study conducted so far with respect to the application of transcranial direct and alternating current stimulation in the treatment of depression was published by Brunoni. The author asserted that he made a controlled trial with over 120 patients suffering from depression. This resulted in a factorial study in which the patients subjected randomly to receive active tDCS and serum sertraline/placebo exhibited significant symptoms as compared to the patients that were given active tDCS and also in combination with sertraline.

As a result, further randomized clinical trials that aim to evaluate the clinical efficacy of tDCS in depression are being performed worldwide.

Here are some of the benefits of using transcranial direct and alternating current stimulation in the treatment of depression:

Transcranial alternating and direct current stimulation improves memory

The use of theta waves on the left parietal of a depressed patient helps to increase the working memory as well as factual memory as shown in 12 ADHD children. It is interesting to note that this only works when the theta waves are in the synchronization phase. Similar research conducted on 12 healthy female children reveals that it increases the memory confidence.

Transcranial direct and alternating current stimulation changes the brain waves thereby fighting depression

Three different studies were conducted on humans for about 20 minutes and it was discovered that transcranial direct and alternating current significantly increased the brain power for about 30 minutes in the indicated wavelength range.
Conclusion

Transcranial direct and alternating current stimulation is an appealing treatment for depression as a result of its relative safety and efficacy profiles attached to the fact of its relative inexpensiveness. TDCS have also been found to have tangible anti-depressant effects. It is also considered a promising therapy because of its minimally invasive nature and its benign relative adverse effects.

However, further research is required to examine the utility of transcranial direct and alternating current stimulation as the first treatment to think about in more severe forms of depression. Presently, it seems crucial to consider transcranial direct and alternating current stimulation as a treatment for patients with a mild level of depression without resistance to treatment. It may be just as effective to make use of it in enhancing the first kind of response rates when combined with pharmacotherapy and psychotherapy.

 

References

1Antal, A., Boros, K., Poreisz, C., Chaieb, L., Terney, D., & Paulus, W. (2008). Comparatively weak after-effects of transcranial alternating current stimulation (tACS) on cortical excitability in humans. Brain stimulation, 1(2), 97-105.

http://www.psychiatrictimes.com/neuropsychiatry/current-status-transcranial-direct-current-stimulation-treatment-depression/page/0/1

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4369553/

https://www.sciencedirect.com/science/article/pii/S0014488609001290

https://www.cambridge.org/core/journals/psychological-medicine/article/transcranial-direct-current-stimulation-in-the-treatment-of-major-depression-a-metaanalysis/96254C1048E1706414248C27C4E9BCA5#

Liveness-detectable biometrics, and cybersecurity

How can we use liveness-detectable biometrics to provide computational barriers to creating fake accounts and fake votes?

Network security today is more difficult than it needs to be, largely because it must be based on the assumption that a single user can hold multiple accounts or multiple IP addresses. The one commonality between e-mail spam, DDOS attacks, presidential election meddling, and blockchain fees is that they’re all based on a single person holding multiple accounts. This person may submit multiple forum posts, multiple http requests, or multiple high-fee cryptocurrency transactions for the sole purpose of attacking or clogging the network, ruining it for everyone else. You might know this just as “spam”, or as “fake news”.

If there were a walled garden of the internet where each user was guaranteed to have provably one account, based on their biometricity, just like in the real world. You could also ban users who spam the network with these inexpensive requests, and could hold each person’s posts to a higher degree of accountability (people wouldn’t submit fake news if they couldn’t shed their post’s biometric signature). In blockchain, the real reason blockchain payments aren’t cheaper than say Visa for millions of users is due to this fact; People are clogging the network with low-priority payments and spam payments.

hd1

So, building a biometric internet isn’t really that simple. You can’t just say “here’s my fingerprint, now create my account”, because a fingerprint is really just an image file. An attacker could copy the scan of your fingerprint and pretend to be you very easily, otherwise this would have been done before.

What you need is a signal that exhibits biometricity, unique features that identify you, but is also a liveness challenge, a puzzle that basically asks “are you human?” and “are you actually there, right now, submitting this biometric signal?”.  If what you submit equates to a yes, you can enter the garden. If you’ve ever had to type in numbers on a street sign, or crooked letters in all caps, or anything that’s like “I’m not a robot”, that’s a liveness challenge (Google’s reCAPTCHA). It’s a liveness challenge, but it doesn’t tie you to one account because it doesn’t exhibit biometricity. There’s nothing biological to distinguish your response from that over other users — it guarantees one person per computer, but it doesn’t guarantee you’re going to act honestly once you’re inside, because you’re still anonymous. A liveness challenge that also exhibited biometricity would ensure that whatever biometric signal you were submitting, like a fingerprint, wasn’t copy and pasted from a file, but rather the signal was generated quite recently, from an actual human being behind their computer screen.

A simple example of a challenge that exhibits both liveness detection and human detectability might be to have some sort of public ledger that is a string of random values, like a blockchain. Each block contains a random hash value that you can use to derive 20 random words out of a list of 1000. These words are derived from the hash value, meaning there is a relation between the block’s hash and this new, presentable stimulus. You could then present these 20 random words to a user and tell them, “quick, say these 20 random words out loud!”. A person says the words and their voice, which is unique to them, is recorded and propagated across the network, where it is confirmed by nodes to be a biometric unique to them. It’s a liveness challenge because it’s difficult for a computer program to generate audio that (1) exhibited biometricity and (2) contained words seeded by a very recent block (i.e. the computational power to generate this may expensive if the seeding block was found quite recently). The fact that you were able to say the words so quickly would indicate that the signal wasn’t generated in advance, or before the random value was submitted, which is a sign that the words came from a live human. A person’s voice is a good (but not great) biometric, so it exhibits biometric features as well.

hd2

Everything discussed so far is great, except for the fact that the NSA and big tech companies have been collecting voice data for years and would be really good at generating a voice simulation to say those 20 words within 3 minutes (or three seconds). We learned from the Titanic that nothing is ever going to be 100% secure, so voice data definitely has a role in our network, but as a more future-proof liveness challenge/biometric, imagine now you’re inputting an electrical signal that was generated based on a block hash. The signal passes over a user’s skin near their left ear and comes out their right ear. The signal as it leaves the right ear still contains elements of the original signal, but it has been modulated based on the unique biological properties of your skin. We then digitize the analog signal so that it can be transmitted to a network of computers which analyze it.

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The most efficient blockchain will be one where every single user has provably one account. Decentralized systems are plagued by spam, so if you can unclog the network from these spam attacks, you can make the fees cheaper for everyone. Right now credit card companies take 2% off every transaction, a financial network with 0% fees is obviously going to be preferable; Money will always flow to the space with highest liquidity (unless it’s prone to corruption like EOS). To build this one-person-one-vote network you need a biometric that exhibits liveness, and one method for doing that is described here, using the unique properties of brainwave biometricities.

A long-term validation metric can be used as an appendage to this, as pioneered by OpenMined.org. Basically, to validate that data isn’t faked, a machine learning algorithm (over a time-consuming process) determines which data improves its recognition rates and which do not. If it were possible to fake out a machine learning algorithm with bad data, then ML theorists would already be doing it, and nobody would be paying for data. Thus, the most stable foundation for a network like this ultimately (and ironically) relies on artificial intelligence (in the long-term / for cementing transactions) as well as fluid biometrics and a reputation system (in the short-term / for immediate payouts).

Finally, we provide a financial incentive for identifying fake accounts based on biometric signals by creating a challengers-verifiers market. People are motivated by financial reward to check each biometric signal submitted and verified by block producers. Block producers are expected to submit fake data 1% of the time and distribute new oblio as a reward. See our github for the latest spec on this component.

Keep an eye out for our next post, where we’ll be delving into the fully-blockchain-compatible algorithm that reached higher identification rates on brainwaves than any other study we’ve seen.

How to Scale a Smart Contract Blockchain

The simplest solutions are often the best. Let's create a voting system to vote in good block producers, and kick out the bad ones, where everybody has exactly one vote.

Imagine if you were in a big, empty room with one or two other people. It would be very easy for each of you to communicate by shouting at one another from corner-to-corner. This is how blockchains used to be, back when very few people knew about them.

Imagine now you start adding more people to the room. Suddenly, communication relies on everyone shouting at each other in all different directions, and each person is overwhelmed with information. Nobody has time to understand anything that they’re hearing – the channels are clogged. How can you fix this problem?

 

Before you invest in any type of blockchain, you should understand what the cons are to blockchains themselves. When we talk about blockchains and scaling, it’s easy to misconstrue the facts, because just as there are many types of blockchains, there are many different viable ways to scale a blockchain as well.

For starters, “scaling” is just making a blockchain accessible to a lot more people – orders of magnitude more, in fact. On a decentralized network, everyone should be able to help out. But the more crowded a blockchain becomes, the harder it is for every person contributing to the blockchain to process new information related to supporting it. This, effectively, moves power to those with pre-existing wealth– those with more bandwith, more storage space, etc.

Going with the analogy described earlier, one solution to the ‘crowded room’ problem is to charge money before people are allowed to shout information. This makes sure only really serious people get to speak into the crowd. But, it also makes communicating (over blockchains) very expensive. People with a lot of pre-existing wealth can launch “spam attacks” on the network to drive up the fees on regular users’ transactions. If it’s a proof-of-stake chain or a centralized proof-of-work algorithm, then these rich persons are probably profiting in this attack, because they’re basically raising the fee price by paying huge fees while most of the fees go back to them anyway. As the network becomes clogged, its market price tumbles, and said “bag-holders” profit off their margin-traded short position. If we’re talking about a smart contract blockchain, this becomes a fundamental problem for developers too.

Experienced programmers can imagine the tiny, but not-so-negligible cost of running a website on one external server. It’s only about $5 per month to get a user database up and running on Amazon AWS. With dApps, things get a lot more expensive. Rather than only one user database, you’re effectively paying everyone on the network to calculate your program’s output at the exact same time, in addition to listening for basic communications. More computational power means higher fees. In the crowded room scenario, imagine if someone had to not only listen to transactions, but also type in each one’s instructions on a handheld calculator before they could process the next one.

 

Another solution to the crowded room is the “telephone game”. People at the corners of the room might whisper information towards central spokes near the middle. This is a bit like how Ripple works. It might also be an analogy for payment channels. But Ripple and payment channels don’t seem to apply to smart contracts, the part where people have to type stuff into calculators. This sucks, because smart contracts have proven to be incredibly useful. If we’re not only asking people to share information, but having each person do calculations on it, the “whisper method” really doesn’t play.

On Project Oblio, nodes that are “ordained”  run smart contracts in subgroups and tie their output to their own biometricity and staked wealth. The difference here is that rather than running the same program across many nodes (traditional smart contract chains), only a small group of ordained nodes need to run, verify, and sign a particular program’s output with their unsheddable biometricity. Because a person can’t shed their biometricity, one person can’t spin up more than one node to dupe the network (Project Oblio is one-person-one-vote, anti-Sybil system). Thus, this proof-of-individual system is a highly-secure way to evaluate smart contracts.  More importantly, it reduces fees by  requiring considerably less computational power.

The gist of it is, on wealth or work-based blockchains, a single smart contract needs to be run by every single node before a block is verified. On Project Oblio, a smart contract or “service” only needs to be verified by one or more trusted individuals to reach approval of the network.  This reduces fees, and allows for greater computational prowess than previously described.

As one eerily might have expected after reading Satoshi’s white paper, one-human-one-vote offers a lot more for society than one-CPU-one-vote. Only through one-person-one-vote  can you scale a smart contract blockchain.

The Anti-Fake News Internet

Project Oblio aims to create an area of the internet where there are no fake or mercenary accounts.

While anonymity is a powerful tool in speaking out against oppression, recent events in our time have shown that pure anonymity can have disastrous consequences. ​The majority of fake news articles are written and posted by anonymous persons. Despite this, under no circumstance should anonymity be outlawed – that’s not what Project Oblio intends to do. Rather, it is extremely important to consider the potential benefit of having a corner of the internet where we can be certain everyone has exactly one account. As this protocol would also double as a gateway into a human-only internet, it would behoove us to consider it as a necessary communication channel in the event of hired A.I. language bots polluting the internet’s watering holes.

Through identification, human detection, and authorization, we can build a  internet where every one of a persons’s postings is tied to their unsheddable and unique aura. On this network, we ask for a higher modicum of proof that a person is reporting honestly on a particular news subject, as any dishonest news reporting will be tied to their biometricity for years, if not eternity. At the very least, a computational barrier for producing thousands of fake comments algorithmically could do wonders for digital communication.

While initially Project Oblio would like to leave all sorts of privacy to traditional blockchains (there are many, many blockchains that all claim to have the “best” privacy, but few that offer proof-of-individuality), it may be possible to have a human-only internet that is also anonymous. Although relatively new, two fields to look into are homomorphic encryption and zero-knowledge proofs. We fully anticipate these improvements to be integrated into Project Oblio at some point in the future.

Hopefully, an internet with a biometric or even just a human-detection-spam-filter will better our ability to differentiate between fact and fiction in the ever-changing digital landscape.