February 2023 - cypherpunks@lists.ogf.org

Cryptocurrency: Lunarpunks, Privacy, and the New Encryption Guerillas... CryptoAnarchy, Agorism, Black Markets, DarkFi
by professor rat 27 Feb '23

27 Feb '23

Keep polishing those Rachel-Rose O'Leary, Coindesk turds, G And AMIR - if you're listening - be sure to go fuck yourself.

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Cryptocurrency: El Salvador's Bukele Clears The Streets To Build The First Cryptopia
by grarpamp 27 Feb '23

27 Feb '23

As forums say... making all drugs legal, then rounding up the remaining street thugs, worldwide, eventually. El Salvador Sends Thousands Of Tattooed Gang Members To Mega-Prison Government authorities in El Salvador are circulating some surreal images in order to prove to the nation and the world that they are cleaning up crime in a country that's currently under a state of emergency due to rampant murders and violent crime. President Nayib Bukele has recently declared a "war on crime" and his message to gangs came in the form of publicizing 'shocking' video footage showing some 2,000 suspected gang members being huddled into a newly opened large prison using military tactics. Tens of thousands more suspected criminals have reportedly been rounded up. Watch below: https://twitter.com/RichardHanania/status/1629513999833497600 Video from the president of El Salvador. Seems like “lock up all the guys with gang tattoos all over their bodies and throw away the key” was pretty low-hanging fruit? It’s interesting how many countries can’t even manage that. The criminals make it easy pic.twitter.com/xUk2Cj1l3U — Richard Hanania (@RichardHanania) February 25, 2023 The footage has been met with some controversy, given there are allegations that innocent men may have been caught in the broad anti-gang dragnet. Additionally, the police appear to be utilizing military prison tactics not seen since post 9/11 Gitmo images. According to international reports: President Bukele tweeted that the first 2,000 people were transferred "at dawn, in a single operation" to the Center for the Confinement of Terrorism, which he says is the largest jail in the Americas. "This will be their new house, where they will live for decades, all mixed, unable to do any further harm to the population." Via Reuters Once widely dubbed the "homicide capital of the world" - authorities in recent years thought they had it under control, but murders spiked over the past year. Via Reuters This caused President Nayib Bukele to declared a state of emergency eight months ago, and police went 'gloves off' in attempting to clear the streets of gangs.

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Cryptocurrency: Lunarpunks, Privacy, and the New Encryption Guerillas... CryptoAnarchy, Agorism, Black Markets, DarkFi
by grarpamp 27 Feb '23

27 Feb '23

Lunarpunks, Privacy, and the New Encryption Guerillas https://www.coindesk.com/layer2/2022/06/08/lunarpunks-privacy-and-the-new-e… By Rachel-Rose O'Leary, Coindesk This article is part of Road to Consensus. Rachel-Rose O'Leary will speak at Layer 2's "Big Ideas" stage at Consensus. We Have Entered the Age of Anonymous Crypto https://www.coindesk.com/markets/2021/01/25/we-have-entered-the-age-of-anon… Lunarpunk, Black Markets, and Agorism in the 21st Century https://theblockchainsocialist.com/lunarpunk-black-markets-and-agorism-in-t… Welcome to Bitcoin Country, Silk Road, and the Lost Threads Of Agorism https://www.coindesk.com/markets/2017/09/23/welcome-to-bitcoin-country-silk… Crypto Anarchists Are Building Tools to Resist the State in Eastern Europe https://www.coindesk.com/markets/2018/12/01/crypto-anarchists-are-building-… https://www.agorist.xyz/ By Rachel-Rose O'Leary, Coindesk, Jun 8, 2022 https://twitter.com/zkarmor https://twitter.com/polarpunklabs A growing group of cryptography experts are using tools to carve "dark" spaces out of the surveilled web. This article is a preview of Rachel-Rose O'Leary's talk on the 'Big Ideas' stage at Consensus. In 1916, a few hundred revolutionaries declared Ireland to be an autonomous nation and occupied strategic locations around Dublin. In the days that followed, the British Army encircled the uprising and suffocated it. One by one, the leaders of the uprising were lined up and shot. A young fighter named Michael Collins evaded death by chance. He vowed never to enter into direct clashes with the British Empire again, and began a war that would change the shape of resistance forever. Units were split into small groups that operated in secrecy. Fighters lacked weapons, but the people and rugged landscape protected them. The new warfare favored hit-and-run tactics and disrupting enemy intelligence. It was the dawn of modern guerrilla tactics – and it won Ireland its independence. These guerilla tactics are no longer feasible today. Modern surveillance technologies and automated weaponry have turned the world that we inhabit into a desert with no protective cover. Resistance fighters are easy targets. Since the 1990s, a movement of privacy advocates and coders called cypherpunks have been fighting back the encroachment of surveillance. In some sense, they draw inspiration from the guerilla fighters before them. Guerilla warfare is fundamentally asymmetric: It is the tactic of a smaller, disadvantaged people against a vastly superior enemy. They fight high-tech with low-tech, complexity with simplicity, fire with water. Coders and guerillas alike define the front lines and change them constantly. For cyphers, it's with ever-advancing encryption and for fighters like the Irish, it's the ability to melt back into the community before the enemy can even give chase. As governments build all-encompassing surveillance machines, cypherpunks use simple encryption tools to render them futile. Cypherpunks argue that without privacy, personal liberty is impossible. Cryptography is a defensive tool to live free from coercion and force. The new guerillas Lunarpunk is descended from cypherpunk but takes its logic a step further. It’s a guerrilla movement committed to establishing a digital forest in cypherspace using tools like encryption that its fighters can recede into. The current internet is a desert rather than a forest due to surveillance. Lunarpunks defend and define new territories – dark, fertile zones that have been claimed back using private, anonymous decentralized autonomous organizations (DAO) and peer-to-peer (P2P) organizational tooling. Another word for this would be an agora, or non-state system. In an interesting twist of history, a science-fiction subculture called solarpunk was one of the principal inspirations for Ether Both lunarpunk and solarpunk are utopian. Unlike solarpunk, lunarpunk is armed. It runs on DarkFi. Currently in the devnet phase, DarkFi (the word is a combination of "dark" and "DeFi") is a layer 1 blockchain that supports these private and anonymous applications. Lunarpunks, so far a small movement of hackers, are already creating tools using DarkFi that allow communities to coordinate in the dark. The DarkFi community has been working on an initial design for a private and anonymous DAO. Right now, DarkFi coders are testing a P2P Internet Relay Chat (IRC) client and task manager to ensure DAOs on DarkFi do not become dependent on centralized and proprietary software. Although crypto aims for decentralization, so much of the industry’s activity happens over for-profit tools like the messaging app Discord and digital notebook Notion. Until now, blockchain applications have been built on a desert landscape. Killer apps like automated market makers (AMM) compute the price of assets in token pools and require the app to know everything that happens in real time. The dominant engineering paradigm requires total surveillance. To engineer anonymous applications, we must generate new concepts. It is necessary to evolve what the DarkFi community calls "anonymous engineering" – a new kind of engineering based on hidden information. For example, zero-knowledge cryptography unlocks a new set of techniques – you can make encrypted commitments to data and trustlessly prove whether or not something has happened. You can compose hidden data structures that can hold references to one another. You can combine these techniques with other primitives, such as homomorphic encryption and multiparty computation, to design fully anonymous and featured applications. Inverting power Lunarpunks perceive lightness as terror, and are fighting against surveillance capitalism. In DarkFi, darkness is structured as an inversion of contemporary power dynamics and a way to empower communities. Darkness is the legacy of surveillance turned upside down. The inversion of hierarchies has been central to many crypto-anarchist movements. Think of the parallel, inverted world that crypto-anarchists call the Parallel Polis, or the tactic of counter-economics, a black market economy that exists parallel to, but distinct from, the statist economy. You can trace this political symbol to the philosopher Friedrich Nietzsche, who wrote about what he called active and reactive forces. Forces are energies that drive human behavior. For him, active forces were positive – and are seen when people affirm and assert their power. Further, active forces lead to differentiation – a multiplicity of cults, factions and communities expressing power in different ways. Reactive forces suppress power and deny difference. In the lunarpunk's language, forests are active and deserts are reactive. Resistance is active; oppression is reactive. According to Nietzsche, active forces should dominate reactive forces. He called this “hierarchy.” But he argues that in reality, perhaps contrary to what you might expect, hierarchies are often inverted. Reactive forces, though lethargic and without any original ideas, are often the most powerful. State power persists by repressing resistance. The desert dominates. Lunarpunk does not negate the current order: It inverts the false hierarchy that places reactive forces on high and suppresses active forces. Lunarpunk proclaims the victory of affirmation against negation, the victory of the active against the reactive, and the victory of the forests against the desert. Like flowers bursting from concrete, a new design space is emerging from dead-end surveillance optimization. It is effortless and spontaneous, like a miracle of healing breaking out across a scarred and broken landscape. Encryption is asymmetric: It favors the smaller player over the monopoly. Cypherpunk hero Julian Assange said that "the universe smiles on encryption" because it is easier to encrypt information than to decrypt it. Lunarpunks are wielding this mystical quality of the universe in open conflict with surveillance. Thanks to Armor and Paul-Dylan Ennis.

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Cryptocurrency: The Four Great Fatal Flaws And Frauds Of Bitcoin, Africa Discovers
by grarpamp 26 Feb '23

26 Feb '23

The Four Great Fatal Flaws And Frauds Of Bitcoin - Pathetically Unservicable Transaction Rate - Massive Growth Of Storage Required To Store TX - Absolute Total Lack Of Privacy - Lightning Network Bitcoin best resolve the first three, and soon, lest it be abandoned by all but the elites who can afford to transact upon it after the next cycle peak. Even they will eventually leave it for lack of privacy. Africa will soon discover these truths... that BTC shills have defrauded them out of the possibility of a True Crypto Future... https://twitter.com/saylor/status/1624175117612814337 Any "fixes" that Bitcoiners apply to Bitcoin to solve these three things can no longer legitimately be called "Bitcoin" under their caretaking... the hypocrisy of their refusal to even attempt fix them earlier exposes that fact.

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Browsers: LibreWolf Project Sanitizing FireFox, Seeks Devs
by grarpamp 26 Feb '23

26 Feb '23

https://librewolf.net/ https://gitlab.com/librewolf-community https://www.reddit.com/r/LibreWolf/ https://lemmy.ml/c/librewolf https://gitter.im/librewolf-community https://matrix.to/#/#librewolf:matrix.org What is LibreWolf? This project is a custom and independent version of Firefox, with the primary goals of privacy, security and user freedom. LibreWolf is designed to increase protection against tracking and fingerprinting techniques, while also including a few security improvements. This is achieved through our privacy and security oriented settings and patches. LibreWolf also aims to remove all the telemetry, data collection and annoyances, as well as disabling anti-freedom features like DRM. https://support.mozilla.org/en-US/products/firefox/protect-your-privacy https://wiki.mozilla.org/Security/Fingerprinting https://arkenfox.github.io/TZP/index.html https://privacytests.org/ https://browserleaks.com/ https://www.deviceinfo.me/ https://www.ssllabs.com/ssltest/viewMyClient.html https://chromium.github.io/octane/ LibreWolf includes... Privacy Delete cookies and website data on close. Include only privacy respecting search engines like DuckDuckGo and Searx. Include uBlockOrigin with custom default filter lists, and Tracking Protection in strict mode, to block trackers and ads. Strip tracking elements from URLs, both natively and through uBO. Enable dFPI, also known as Total Cookie Protection. Enable RFP which is part of the Tor Uplift project. RFP is considered the best in class anti-fingerprinting solution, and its goal is to make users look the same and cover as many metrics as possible, in an effort to block fingerprinting techniques. Always display user language as en-US to websites, in order to protect the language used in the browser and in the OS. Disable WebGL, as it is a strong fingerprinting vector. Prevent access to the location services of the OS, and use Mozilla's location API instead of Google's API. Limit ICE candidates generation to a single interface when sharing video or audio during a videoconference. Force DNS and WebRTC inside the proxy, when one is being used. Trim cross-origin referrers, so that they don't include the full URI. Disable search and form history. Disable form autofill. Disable link prefetching and speculative connections. Disable disk cache and clear temporary files on close. Use CRL as the default certificate revocation mechanism, as it is faster and privacy oriented. For security and usability reasons, the browser might fall back to OCSP in some instances: when that happens, OCSP will be stapled to preserve privacy. Security Stay up to date with upstream Firefox releases, in order to timely apply security patches. Enable HTTPS-only mode. Enable stricter negotiation rules for TLS/SSL. Always force user interaction when deciding the download location of a file. Disable scripting in the built in pdf reader. Protect against IDN homograph attack. Implement optional extension firewall, which can be enabled manually. Revert user-triggered TLS downgrades at the end of each session. Set OCSP to hard-fail in case a certain CA cannot be reached. Annoyances Prevent window resizing from scripts. Disable autoplay of media. Disable search suggestions and ads in the urlbar. Remove all the distracting and sponsored content from the home page. Remove the Pocket extension at compile time. Remove Mozilla VPN ads. Disable Firefox Sync, unless explicitly enabled by the user. Disable extension recommendations. Others Completely open source and community driven. Easy and Docker-based build process, so that everyone can build from source in few steps and without local dependencies. LibreWolf specific UI that exposes the most important privacy and security settings, to allow you to easily control them. Completely disable telemetry, including crash report, normandy, studies and personalized recommendations. No data collection of any kind. In fact, as stated in our privacy policy, we wouldn't even have the infrastructure to do that, making it impossible from a technical standpoint. Disable Google Safe Browsing, over censorship concerns, and in an effort to prevent Google from controlling another aspect of the internet. This would also make a developer key a requirement to build from source, which is something we are not comfortable with. Disable DRM, as it is a limitation to user freedom. Avoid making unnecessary changes that increase the fingerprint without giving any privacy gain. Only allow outgoing connections that are not privacy invading. Disable built-in password manager and suggest more robust options.

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From Big Dog to Big House - the oxmoronic nature of national-anarchy
by professor rat 26 Feb '23

26 Feb '23

The confronting pictures that capture El Salvador's crackdown on gangsters 11 hours ago The Guardian More El Salvador moves suspected gang members to 40,000-capacity ‘megaprison’ Yesterday Reuters More The 'mega prison' in El Salvador's gang crackdown

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“We are looking to kill Trump."
by professor rat 26 Feb '23

26 Feb '23

Take a number and get in line Amirali Hajizadeh, the head of the Revolutionary Guards Aerospace Force, during a TV interview, said that Iran has plans to kill former United States President Donald Trump https://www.news18.com/news/world/irans-cruise-missile-may-raise-us-concern… APster style deadpool on the perp https://stiffs.com/celebrity/donaldtrump Cypherpunk revolution into KILLING US PRESIDENTS SINCE 1999

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[ot] painful research joke: mind control of rat over wifi
by Undescribed Horrific Abuse, One Victim & Survivor of Many 26 Feb '23

26 Feb '23

The painful joke is because members of this list have likely experienced a more conventional kind of mind control (that involving abuse and messaging and possibly drugs and other things including sometimes implants) for “ratting” of powerful criminals. The messaging can at times include stories from the abuser that an implant has been placed in their brain, and/or that the abuser controls them with their mind alone and the victim is helpless, and/or that the victim is a “worthless rat”. Computer viruses and spy devices are called implants now, and some people have found biological implants as well I understand. In this normal recent research paper, wireless devices are attached to the heads of a human and a biological rat such that the rat is forced to do what the human directs. I imagine you could also wire it the other way around, so that a human has to do what a rat wants. Studies may be in progress. This paper came up in my machine learning feed. The twitter account usually tweets popular mainstream data science papers. Not sure why they tweeted this one. I expect that others would agree that, given experience of being mind controlled, experiments like this are horrifying and highly unethical, even though the subject is a tiny rat. Having your brain forced to ignore everything that it is there to do for you, in order to attend to moment-to-moment commands coming from invisible stimulation is world-ending hellish torture that makes painful and confused lifelong activism, uncommunicable complex trauma, and tragic loss of beautiful independent spirits and life potential. Victims need studies like these to somehow represent respect and inclusion of the control subject. Still, I strongly celebrate that research like this is producing mainstream discourse and knowledge, and find it quite necessary. It is one of the paths along which therapies for detection and recovery can develop. I apologize that this is not actual mkultra recovery research, which likely also exists. This paper, the sharing of which unfortunately supports harmful messaging, is still much easier for me to think of and look at than more relevant ones. The paste below is poor quality partly because I am not sure of actual utility of the paper. https://twitter.com/hardmaru/status/1628963052560482305 https://www.nature.com/articles/s41598-018-36885-0 Human Mind Control of Rat Cyborg’s Continuous Locomotion with Wireless Brain-to-Brain Interface shaomin Zhang1,3,4, Sheng Yuan1,3,4, Lipeng Huang2, Xiaoxiang Zheng1,3,4, Zhaohui Wu2, Kedi Xu1,3,4 & Gang pan 2 Brain-machine interfaces (BMIs) provide a promising information channel between the biological brain and external devices and are applied in building brain-to-device control. Prior studies have explored the feasibility of establishing a brain-brain interface (BBI) across various brains via the combination of BMIs. However, using BBI to realize the efficient multidegree control of a living creature, such as a rat, to complete a navigation task in a complex environment has yet to be shown. In this study, we developed a BBI from the human brain to a rat implanted with microelectrodes (i.e., rat cyborg), which integrated electroencephalogram-based motor imagery and brain stimulation to realize human mind control of the rat’s continuous locomotion. Control instructions were transferred from continuous motor imagery decoding results with the proposed control models and were wirelessly sent to the rat cyborg through brain micro-electrical stimulation. The results showed that rat cyborgs could be smoothly and successfully navigated by the human mind to complete a navigation task in a complex maze. Our experiments indicated that the cooperation through transmitting multidimensional information between two brains by computer-assisted BBI is promising. Direct communication between brains has long been a dream for people, especially for those with difficulty in verbal or physical language. Brain-machine interfaces (BMIs) provide a promising information channel between the brain and external devices. As a potential human mind reading technology, many previous BMI studies have successfully decoded brain activity to control either virtual objects1–3 or real devices4,5. On the other hand, BMIs can also be established in an inverse direction of information flow, where computer-generated information can be used to modulate the function of a specific brain region6–8 or import tactile information back to the brain9–11. The combination of different types of BMI systems can thus help to realize direct information exchange between two brains to form a new brain-brain interface (BBI). However, very few previous studies have explored BBIs across different brains12. Miguel Pais-Vieira et al. established a BBI to realize the real-time transfer of behaviorally meaningful sensorimotor information between the brains of two rats13. While an encoder rat performed a senso- rimotor task, samples of its cortical activity were transmitted to matching cortical areas of a “decoder” rat using intracortical micro-electrical stimulation (ICMS) on its somatosensory cortex. Guided solely by the information provided by the encoder rat’s brain, the decoder rat learned to make similar behavioral selections. BBIs between humans have also been preliminary explored. One example of a BBI between humans detected motor intention with EEG signals recorded from one volunteer and transmitted this information over the internet to the motor cortex region of another volunteer by transcranial magnetic stimulation, which resulted in the direct information transmission from one human brain to another using noninvasive means14. In addition to information transfer between two brains of the same type of organism, the BBI enables information to be transferred from a human brain to another organism’s brain. Yoo et al. used steady-state visual evoked potential (SSVEP)-based BMI to Figure 1. Experiment setup. (a) Overview of the BBI system. In the brain control sessions, EEG signal was acquired and sent to the host computer where the motor intent was decoded. The decoding results were then transferred into control instructions and sent to the stimulator on the back of the rat cyborg with preset parameters. The rat cyborg would then respond to the instructions and finish the task. For the eight-arm maze, the width of each arm was 12 cm and the height of the edge was 5 cm. The rat cyborg was located in the end of either arm at the beginning of each run. And preset turning directions were informed vocally by another participant when a new trial started. (b) Flowchart of the proposed brain-to-brain interface. extract human intention and sent it to an anesthetized rat using transcranial focused ultrasound stimulation on its brain, thereby controlling the tail movement of the anesthetized rat by the human brain15. In a very recent work, a BBI was developed to implement motion control of a cyborg cockroach by combining a human’s SSVEP BMI and electrical nerve stimulation on the cockroach’s antennas16. The cyborg cockroach could then be navigated by the human brain to complete walking along an S-shaped track. Although the feasibility of BBIs has been preliminarily proven, it is still a big challenge to build an efficient BBI for the multidegree control for the continuous locomotion of a mammal in a complex environment. In the current study, we present a wireless brain-to-brain interface, through which a human can mind control a live rat’s contin- uous locomotion. Different from the control of lifeless devices, it is critical to have highly demanding instantane- ity in the control of a living creature in real time due to its agility and self-consciousness. For this purpose, the BBI system requires timely reactions and a high level of accuracy in terms of information decoding and importing, as well as real-time visual feedback of the rat’s movement. The SSVEP-based BMI, as used for brain intention decod- ing in previous BBI works that have depended on visual stimulation, may distract the manipulator from reacting promptly to real-time visual feedback. As an alternative solution, motor imagery-based BMI has the advantages of rapid response and a low level of distraction from the visual feedback. Therefore, the BBI system established in the current study integrates control instructions decoded by noninvasive motor imagery with neural feedback, and the instructions are sent back to the rat’s brain by ICMS in real time. We also proposed and compared two different control models for our BBI system, the thresholding model (TREM) and the gradient model (GRAM), to provide a more natural and easier process for the manipulator during steering control. With this interface, our manipulators were able to mind control a rat cyborg to smoothly complete maze navigation tasks. Results Set up of BBI system and task design. The BBI system in the current study consisted of two parts: a noninvasive EEG-based BMI and a rat cyborg system17 (Fig. 1(a)). The EEG-based BMI decoded the motor intent of left and right arm movement, which corresponded to the generation of instruction Left and Right turning, respectively. In the current study, the average EEG signal control accuracy of all 6 manipulators was 77.86 ± 12.4% over all the experiments conducted. The eye blink signals in the EEG were used to elicit the instruction Forward/ Reward, which was detected by the amplitude of EEG signal in the frontopolar channel. The rat cyborgs were prepared based on previous works17–20 and were well-trained before experiments were conducted in this study (see Methods for more details). Two parts of the system were connected through an integration platform, sending decoded instructions from motor intent to the rat cyborgs, and providing visual information feedback in real time. An overview of the BBI system is presented in Fig. 1. The control effect of the rat cyborgs was evaluated by a turning task on an eight-arm maze. A complete run of the turning task contained a total of 16 turning trials, with eight left turnings and eight right turnings. To avoid the influence of the memory and training experience of the rats, the turning direction sequence was randomly Figure 2. (a) Performance of manual control stage. The mean CPT of each rat cyborg for manual control across all sessions. (Note: For display, only positive standard deviations are presented as error bars). (b) Different areas assigned in the investigation for the optimal area. The simplified plus-maze was modified from the original eight-arm maze by blocking four crossing arms. (c) The averaged success rate (mean ± SD) of each area for the rat cyborgs to receive instructions with manual control. generated by computer before each task run. The targeted turning direction of each trial was informed vocally by other experimenters at the start of each trial during the turning control experiments. For each run, the rats were placed at the end of one of the eight arms as a starting point. The rat was then driven towards the center of the maze and guided to turn into one of the adjacent arms. A trial was regarded as successful when the rat performed a correct turning and reached the end of the target arm. A new trial would then start when the rat reached the end of one arm and turned its head back towards the center of the maze. If the rat failed to complete one turning trial, the same turning direction trail was repeated until the rat succeeded. The total time from the start to the end of completing 16 correct trials was recorded as the completion time (CPT) of each run. The turning accuracy (TA) was then calculated as the ratio of the number of correct turns to the total number of turns performed. The entire experiment contained three stages, one manual control stage and two brain control stages, with each stage containing 5 sessions and being performed on five consecutive days. Each session consisted of 3 inde- pendent runs, with an interval break time between each run of at least eight minutes. The entire procedure was video recorded, and the mouse clicking sequences during manual control stage were recorded for further analysis. In the second and third stages, two different control models (GRAM and TREM, see details in the Methods) were applied. To further test the applicability of brain control, the rat cyborgs were controlled to complete a navigation task in a more complicated maze. Manual control of rat cyborg. During the manual control stage, the rat cyborgs were controlled by experi- enced operators. We found that the turning accuracy of a well-trained rat cyborg could achieve an exceptionally high rate of nearly 100%. As displayed in Fig. 2(a), the average CPT of all rat cyborgs at the first session of manual control was 190.03 ± 75.41 s and decreased to 132.56 ± 12.39 s at the fifth session. Most of the rats showed an obvi- ous learning curve through the manual control stage. The CPT of each rat cyborg became very close at the end of the manual control stage, indicating that they were becoming familiar with the task environment and the control Figure 3. (a) Average CPT across all rat cyborgs for the three consecutive stages. (b) Average turning accuracy across all rat cyborgs for the three consecutive stages. Error bars indicate the standard deviation. *Indicates p < 0.05. instructions delivered into their brains. There was no significant difference (paired T-test, p > 0.05) between the average CPT of the last two sessions of the manual control stage for each rat cyborg, which indicated that the rat cyborgs were in a steady state. During the manual control sessions, we noticed that the successful turning behavior of a rat cyborg was highly dependent on the timing of the turning instructions (Fig. 2(b)). To optimize the instruction timing, an additional experiment was conducted. In this experiment, the rats were placed at the end of the plus-maze, which was modified from the original eight-arm maze, to wait for instructions to turn left or right. By delivering turning instructions while the rats’ bodies were located in different sections along the straight arm, the instruction timing could be evaluated by the turning success of the rats. Figure 2(c) shows the overall performance of the turning success rate at five equally divided sections of the maze. According to the success rate of this plus-maze test, the best location for the rat cyborg to receive turning instructions was the area near the intersection (areas C and D in Fig. 2(b)). When considering brain control conditions, motor imagery should be initiated slightly before the optimal point for manual control because the decoding process and instruction generation take a short period of time. Thus, in our study, the manipulators were asked to start motor imagery when the rats arrived at areas D and E. BBI evaluation. After stage 1 of manual control, two further brain control stages were performed by several brain control manipulators. In the two brain control stages, the manipulators controlled the rat cyborgs with a BBI (Fig. 1(a)) based on one of the two proposed control models. During the first brain control stage (stage 2), the gradient model (GRAM) was applied, and in the second brain control stage, the thresholding model (TREM) was applied. The two control models were based on different threshold calculating strategies. The thresholds were used to differentiate the decoding results attributed to real intention or noise (see Methods for a detailed expla- nation of thresholds). The results of the two control models are shown in Fig. 3. The overall CPT value remained stable in both brain control stages, with no significant difference between the two sessions inside each stage (Fig. 3(a), paired T-test for the average CPT, p > 0.05). However, a comparison between the two brain control stages showed that a longer time was taken to complete the same maze tasks with the TREM-based BBI system. The average CPT of all rat cyborgs across the GRAM-based stage 2 was shorter than the TREM-based stage 3 Figure 4. (a) Average number of turning instructions for all the rat cyborgs across all the sessions and a comparison of the group-level number of turning instructions between different stages. (b) Average number of Forward instructions for all the rat cyborgs across all sessions and a comparison of the group-level number of Forward instruction between different stages. ***indicates p < 0.01, *indicates p < 0.05, paired T-test. (243.41 ± 12.73 s vs. 275.05 ± 14.47 s, paired T-test, p < 0.05), demonstrating that the GRAM model was better than the TREM model for the proposed BBI system. As shown in Fig. 3(b), the average turning accuracy of all rat cyborgs dropped approximately 15% at the first session of brain control stage 2 compared to that in the manual control stage. The turning accuracy then grad- ually increased back to 98.08 ± 2.31% at the last session in stage 2, indicating that the rat cyborgs could quickly be accustomed to the transition of different control styles. The drop of the fourth session was most likely due to the poor performance (81.67 ± 5.44%) of one rat cyborg. When the brain control model changed from GRAM at stage 2 to TREM at stage 3, the turning accuracy slightly dropped to 90.35 ± 5.03% in the first session of stage 3 and then generally increased across the remainder of the last stage. The group level of turning accuracy on average for stage 2 and 3 was 91.75 ± 3.85% and 93.32 ± 1.73%, respectively (stage 2 vs. stage 3, paired T-test, p > 0.05). Overall, the turning accuracy of stage 2 and stage 3 demonstrated stable behavior results of brain control on rat cyborgs at the group level. We further analyzed the sending number of different instructions among the three stages. Figure 4(a) shows the average number of Left and Right turning instructions to complete an experimental run across sessions of all the rat cyborgs tested. Theoretically, the minimum number of turning instructions given in a 100% accuracy run is 16, which can hardly be reached even by experienced manual control. Compared with the GRAM-based and the TREM-based brain control stages, the group-level number of turning instructions were 60.15 ± 7.33 and 87.98 ± 56.30 (stage 2 vs. stage 3, paired T-test, p < 0.01), respectively. Thus, more turning instructions were needed to steer the rat cyborg with TREM-based brain control. Since the number of turning instructions was largely affected by the accuracy of the instructions, the extra instructions in TREM were most likely used to compensate the effect of wrong turning behavior. As we mentioned above, instructions given with a proper timing contributed to fewer mistakes; therefore, the lower number of turning instructions in the GRAM-based brain control stage demonstrated that there was less error turning correction in GRAM-based stage 2 than in TREM-based stage 3. Figure 5. The delay between the start of decoding result output and the instruction generation refers to the thresholds for GRAM and TREM. ***indicates p < 0.01, T-test. As shown in Fig. 4(b), the group level average of Forward instructions across the sessions of GRAM-based and TREM-based brain control was 228.14 ± 44.44 and 286.70 ± 13.57, respectively. The statistical analysis indi- cated that the sending number of Forward instructions had no significant difference (stage 2 vs. stage 3, paired T-test, p = 0.09) between the two brain control stages. This may be due to the large fluctuation in the first two sessions of stage 2, which might have been caused by the transition from manual control to brain control. On one hand, the brain-control manipulators needed to gain experiences in controlling rats. On the other hand, the rat cyborgs also needed time to get adapted to new controlling strategy, especially the different stimulation timing and frequency from manual control. When only the later three sessions of stage 2 and stage 3 were compared, the sending Forward instruction did show a significant difference (later three sessions, stage 2 vs. stage 3, paired T-test, p = 0.03). This result demonstrated that the TREM-based brain control model requires more Forward instructions for the rat cyborgs to complete the same turning tasks. The reason for more Forward instructions with the TREM-based brain control model was the rat cyborgs had a worse performance with the TREM model and required more turning and forward instructions to correct the wrong behavior. To explain the different performances of GRAM- and TREM- based brain control strategies, we also calcu- lated the short delays occurred between decoding result output from EEG device and instructions generated by two different control models. Our results showed a nearly 70% reduction of instruction generation delay with GRAM (155.01 ± 3.10 ms) compared to TREM (494.70 ± 47.22 ms) (Shown in Fig. 5). Turning instructions were thus generated and sent much quicker after the motor imagery with the GRAM model, which ensured less wrong turning behavior of the rat cyborgs and better turning performance. The BBI system was further tested in a maze of higher complexity to test its applicability and stability. The rats were asked to complete a series of preset navigation tasks such as climbing and descending steps, turning left or right, and going through a tunnel in a three-dimensional maze under control of the BBI system. When the rat went into a wrong direction or turned into an unexpected route, the manipulator needed to guide the rat back to the correct route (Fig. 6, see more details in Supplementary Video 1). 5 minutes completion time for each run was limited as the criterion for evaluating success rate. A successful run was defined as the rat cyborgs finish all of preset navigation tasks following the route within the limited time. All rats participated in turning tasks were tested with the optimized GRAM-based brain control model in the maze task. The rats all performed well with high success rate in 10 consecutive tests (Table 1). Discussion Our study demonstrated the feasibility of cultivating an information pathway between a human brain and a rat brain. With our BBI system, a rat cyborg could accurately complete turning and forward behavior under the control of a human mind, and could perform navigation tasks in a complicated maze. Our work extended and explored the further possibility of functional information transmission from brain to brain. Unlike mechanical robots, the rat cyborgs have self-consciousness and flexible motor ability, which means the rat cyborgs will have unexpected movements depending on their own will during the control period. The BBI system should thus be designed with high instantaneity and real-time feedback for a better control effect. Previous brain-to-brain sys- tems have mainly been based on the SSVEP paradigm15,16. In the SSVEP paradigm, the manipulators must switch their attention between the feedback screen and the flickers. However, rat cyborgs move quickly and require a minimum frequency of Forward instructions above 3 Hz. It is thus difficult for the human manipulator to send a high frequency of Forward instructions and simultaneously watch the locomotion of the rat cyborgs in the feed- back screen. Compared with previous works15,16, we used motor imagery and eye blink as manipulative protocols and provided real-time visual feedback of the rat cyborg, which is comparably more viable and avoids the visual fatigue of the manipulators. In addition, during the rat cyborgs brain control experiments, the overall perfor- mance was influenced by several major factors: Figure 6. The rat cyborg was navigated by human brain control in a more complex maze (see more details in Supplementary Video 1). The three-dimensional maze was more complicated, consisting of a start point and an end point, slops and stairs for climbing and descending, a raised platform with a height of half a meter, pillars to be avoided and a tunnel to be passed through. The rat cyborgs were asked to complete the navigation task along the preset route (red arrowed) within 5 minutes. Rat cyborg Success Total Success rate A01 8 A02 9 A03 8 A04 9 A05 10 A06 10 Average 9 10 80% 10 90% 10 80% 10 90% 10 100% 10 100% 10 90% Table 1. Success rate of brain control in the complex maze. (1) The accuracy of instructions. The decoding correctness of motor imagery and the appropriate timing of control instructions influence the control performance the most. Furthermore, the instruction should be sent with high instantaneity, especially when an unexpected mistake occurs. In our brain control sessions, the correctness mainly depended on the threshold value and the timeliness of triggering instruction determined by the control models. The better performance (less CPT and number of turning and forward instructions) for GRAM-based BBI is most likely due to less delay between the start of the decoding results and the release of instructions. Comparatively, the longer delay occurred in TREM may probably contrib- ute to a longer CPT, which in turn resulted in greater amount of instructions needed to complete the task. Besides, the longer delay in the TREM model also leads to obstruction of motor imagery. The manipulators reported that the delay of instruction release during TREM brain control could not readily be perceived. Although the manipulators tried to begin imagery in advance, it was difficult to decide the concrete timing and difficult to operate when instructions were needed to be released over a short period. In contrast, with the short response duration in GRAM, the manipulators were able to start motor imagery at the optimal instruction-receiving time, and switching between Left and Right instructions was much easier. (2) Adaption of the manipulators to brain control task. The mental status of a manipulator can be influenced by disturbance, such as environmental noise, and fatigue caused by long-duration imagery. The ability to overcome these could be improved after several practice sessions. The noninvasive EEG-based BMI used in this study translates the sensorimotor rhythms detected in the bilateral motor areas to the control signal for the rat cyborg. This is not intuitive to the manipulators at the beginning of the experiment, but becomes more intuitive as the experiment goes on. The manipulators gradually learn what instruction should be sent and when their imagery should begin according to the movements and locations of the rat cyborg, thereby cultivating a tacit understanding between the human and the rat cyborg. The stable level of perfor- mance seen in the latter stage 2 and stage 3 indicates this mutual adaption. (3) The inherent adaptive ability of rat cyborgs. Rat cyborgs possess an inherent adaptive ability to their environment and the control method. The overall decrease of average CPT in the manual control stage indicates the adaption of rat cyborgs to the control instructions. The variation trend of each line indicates the various adaption abilities among rat cyborgs. Intriguingly, the final CPT of each rat cyborg reached a similar level. It is likely that all of the rat cyborgs adapted to the same control pattern of the operator. In addition, the rat cyborgs can also adapt to the changes of instruction release due to their excellent learning ability. The results showed that the performance was adversely affected by changes in the control mode (stage 1, session 5 vs. stage 2, session 1 and stage 2, session 5 vs. stage 3, session 1 in Fig. 3(a,b)) but subse- quently stabilized. The decrease in the turning accuracy from stage 1 to stage 2 was much sharper than the change from stage 2 to stage 3. This may be because the control pattern is more distinct between manual control and brain control. While between different brain control stages, the manipulator’s control pattern was not likely to dramatically alter. (4) In conclusion, our findings suggest that computer-assisted BBI that transmits information between two entities is intriguingly possible. The control model proposed here could transfer the decoding results of motor imagery-based EEG-BMI to other external devices with remarkable instantaneity. In the future, er- ror-related potentials (ErrPs)21 could be used to detect false generated instructions, thereby eliminating the wrong instructions before sending them to the rat cyborgs. Furthermore, information flow will be made bidirectional and communicative between two human individuals. Methods Participants and ethics statement. Six rats were engaged in this study. All methods were carried out in accordance with the National Research Council’s Guide for the Care and Use of Laboratory Animals. All exper- imental protocols were approved by the Ethics Committee of Zhejiang University, China. Informed consent was obtained from all manipulators. Rat cyborg preparation. The rat cyborg system had long been developed in our previous research work. Briefly, bipolar stimulating electrodes were made from pairs of insulated nichrome wires (65 μm in diameter), with a 0.5 mm vertical tip separation. Microelectrodes were implanted into the rat’s brain for the control of their locomotion. Two pairs of electrodes were implanted in the bilateral medial forebrain bundle (MFB)22 for virtual reward stimulation and instruction of forward moving. The other two pairs of electrodes were implanted sym- metrically in both sides of the whisker barrel fields of somatosensory cortices (SIBF)23 for turning cue stimula- tion. The rats were allowed to recover from the surgery for one week before the experiments. Once recovered, the rat cyborgs were first trained to correlate the stimulations with the corresponding locomotion behaviors17. The parameters of the electrical stimulation that were sent into the rat’s brain were based on our previous works24, which can activate appropriate behavior but avoid seizures even after a long duration of stimulation. During the training and control sessions, electrical stimulations were delivered through a wireless microstimulator mounted on the rat’s back. Control instructions were given by operators with a computer program wirelessly connected to the microstimulator through Bluetooth. Decoding in the BBI. A commercial EEG device, Emotiv EPOC (Emotiv Inc., USA)25 was used in this study for EEG data recording. EEG data were acquired with a 14-channel neuroheadset, with all electrode impedances kept below 10 kΩ. During the brain control experiments, the EEG signals were sampled at the rate of 256 Hz. The recorded data were then wirelessly transmitted to a host computer through Bluetooth and further processed with the help of Emotiv SDK. Through trained imagination, the manipulators learned to modulate their sensorimotor rhythm amplitude in the upper mu (10–14 Hz) frequency band26,27. The power spectrum of left and right compo- sition was then obtained as the intensity of motor imagery by common spatial pattern (CSP)28, i.e., xL(t) and xR(t), respectively. Details of the common spatial pattern filter are described as follows: Let XR and XL denote the preprocessed EEG during right- or left-hand movements with dimensions N × T, where N is the number of channels and T is the number of samples per channel. The common spatial pattern filter is acquired as follows: (1) Calculate the normalized channel covariance of XR and XL as: cov(XL) trace(XLXL) (1) cov(XR) T CL = CR = trace(XRXR) (2) (2) Average the CL and CR on all of the left- and right-hand movement EEG trials; the composite spatial covari- T Scientific RepoRts | 00 (4) Perform whitening transform on CL and CR, and the transformed spatial covariance matrixes are: SL = PCLPT (5) SR = P CRPT (6) where, (5) Perform eigenvalue decomposition on the transformed spatial covariance matrix, where: S =UΣUT (8) L LLL S = U Σ UT (9) R RRR (Note that ΣL + ΣR must be an identity matrix); (6) The eigenvectors corresponding to the largest eigenvalue in ΣL and ΣR are chosen to calculate the common spatial pattern filters for right- and left-hand movements, which can be written as: SF = U (i|argmax Σ (i))P LL iL (10) SF = U (j|argmax Σ (j))P RR jR (11) (7) Let x(t) be the preprocessed EEG signal recorded in movement imaginary application, the intensity of left- and right-hand movement imagery can be given as: xL(t) = SFLx(t) (12) xR(t) = SFRx(t) (13) (8) Finally, calculate the power spectral density of xL(t) and xR(t), and aggregate the band power within the overlapping window length of k. (14) (15) where P(x(t)) indicates the power spectral density of x(t). The intensity of motor intent was then mapped to a value ranged from 0 to 1, and the normalized B(t) was used as the input of the control model. Set up of BBI system. As the BBI system consisted of a noninvasive EEG-based BMI and a rat cyborg system, a controlling program written in Visual C++ was applied to acquire EEG raw data from Emotiv SDK, generate instructions with the control models and trigger the release of instructions to the rat cyborg. The loco- motion and location of the rat cyborg in the entire experimental scene was video captured by a top-viewed cam- era and visually delivered back to the manipulators on an LCD screen in real time. The decoding results of motor imaginary were relayed using a flashing instruction feedback panel that was integrated in the bottom of the LCD by an OpenCV (Open Source Computer Vision Library, http://opencv.org)-based self-written program. The EEG decoding results and motor control instructions were recorded with a J2EE (Java 2 Platform Enterprise Edition)-based program for further analysis. Control models for BBI. The inputs of the control model included the decoding results of Left or Right motor imagery and eye blink detection. The collected EEG signals were projected by a common spatial pattern (CSP) spatial filter. Next, the power spectrum of left and right composition was obtained as the intensity of motor imagery, i.e., xL(t) and xR(t), respectively. Eye blink, xF(t), was detected when the EEG signal (E(t)) amplitude of channels near the eyes exceeds a threshold →θEOG.  The output of the control model was a control signal for the microelectrical stimulations. xL(t), YR(t) and YF(t) represent the Left, Right and Forward instructions, respectively. For the safety of the rat cyborgs, instructions should be sent under the following rule: If two instructions were presented continuously, the latter instruction would only be sent when the time interval was larger than a prede- fined threshold ΔT. Adjacent instructions were defined as tuples <C1, C2>, C1, C2 ∈{Left, Right, Forward}. Five P = Σ−1/2UT (7) 0 t B (t) = P(x (t)) L∑L t−k t B (t) = P(x (t)) R∑R t−k →→ 1, E(t) ≥ θ xF(t) =  EOG 0, Otherwise (16) → Figure 7. Samples of decoding results and their corresponding gradients of motor imagery in a preliminary experiment. The blue curve is the result of right imagery (Right) and the orange curve is the result of left imagery (Left). During the right turning period shown in the figure, only right imagery occurred, while in the left turning period, both left and right results appeared. The right decoding results were deemed to be caused by noise. In addition, the left decoding results appearing in the blank period (no imagination) are regarded as noise as well. The gradL (yellow) and gradR (light blue) curves represent the left and right gradient of corresponding decoding results, respectively. θL and θR are the optimal thresholds for left and right motor imagery in TREM. For GRAM, the optimal thresholds are θL and θR. out of nine types of tuples were restricted, namely, ΔT<F,F>, ΔT<L,L>, ΔT<R,R>, ΔT<L,F> and ΔT<R,F>. These five were determined by the number distribution of the interval for each tuple based on the manual control sequence record. To guarantee the proper reaction, the level of excitement and the safety of the rat cyborgs, the intervals of ΔT<F,F>, ΔT<L,L>, ΔT<R,R>, ΔT<L,F> and ΔT<R,F> for brain control were set to be 200 ms, 500 ms, 500 ms, 350 ms and 350 ms, respectively, according to our previous work17. The minimum time interval was not restricted for F-L, F-R, R-L and L-R because the manipulator needed to send the first turning command as quickly as possible. We defined n = 0, 1, ... as the n-th generation of instruction, and tL(n), tR(n) and tF(n) were the times that an instruction occurred. Initially, tL(n), tR(n) and tF(n) were equal to 0 (n = 0). The generation of Forward was the same for the two models, as described below: (17)    t (n)−t (n−1)≥ΔT ,   F F <F,F>   t (n) − t (n − 1) ≥ ΔT , FL <L,F> 1, t∈tF Y(t)=   t (n)−t (n−1)≥ΔT  F         F a n d R x F ( t F ) = 1 <R,F>   0, otherwise  Two models (Fig. 1(b)) for generating Left and Right instructions were proposed. One was called the thresh- olding model (TREM), in which the instructions were generated when the decoding results exceeded a threshold (θ). The other model was the gradient model (GRAM), in which the instructions were generated when the gradi- ent value between two decoding results transcended a threshold (θ′). The thresholds were used to differentiate the decoding results attributed to real intention or noise. Figure 7 demonstrates typical decoding results of a left and right imagery and their corresponding gradients. Thresholding Control Model. For TREM, controlling impulses were generated when the intensity of left or right exceeded a threshold θ. A turning instruction was generated if xL(t) > θL or xR(t) > θR. Therefore, the function of TREM is described as follows:  1, t∈{t|t(n)−t(n−1)≥ΔT and x(t)≥θ} LLL <L,L>LLL YL(t) =  0, otherwise  (18)  1, t∈{t|t(n)−t(n−1)≥ΔT and x(t)≥θ} RRR <R,R>RRR YR(t) =  0, otherwise  (19) Gradient Control Model. Although the threshold in TREM could differentiate the decoding results attributed to real intention or floating background noise, the delay between the start of the decoding results and the generation of instruction was too long. We proposed an improved model, GRAM, that outperformed in both differentiation and instantaneity. For GRAM, instructions were generated when the gradient value between two decoding win- dows transcended a threshold θ′. The gradient value was calculated as follows: Gradx(t) = x(t) − x(t − 1) (20) A turning instruction was generated if Grad x(t) > θ′. Accordingly, the function of GRAM is described as follows:  1, t∈{t|t(n)−t(n−1)≥ΔT  LL L <L,L> and Gradx(t)≥θ′} LL L and Gradx(t)≥θ′} RR R YL(t) =  0, otherwise (21) (22)   1, t∈{t|t(n)−t(n−1)≥ΔT  RR R <R,R> YR(t) =  0, otherwise  The thresholds θ and θ′ were decided prior to the implementation of brain control. To ascertain the optimal threshold, a preliminary experiment was conducted. The manipulators were asked to complete three rounds of eight motor imagery tasks. Intents were decoded in real time, and the decoding results were recorded. The best threshold was determined with a receiver operating characteristic (ROC) curve. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References 1. Royer, A. S., Doud, A. J., Rose, M. L. & He, B. EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies. Ieee T Neur Sys Reh 18, 581 (2010). 2. Xia, B. et al. A combination strategy based brain–computer interface for two-dimensional movement control. J Neural Eng 12, 46021 (2015). 3. Wolpaw, J. R. & McFarland, D. J. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. P Natl Acad Sci Usa 101, 17849 (2004). 4. Carlson, T. & Millan, J. D. R. Brain-controlled wheelchairs: a robotic architecture. Ieee Robot Autom Mag 20, 65 (2013). 5. Meng, J. et al. Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks. Sci Rep-Uk 6, 38565 (2016). 6. Hoy, K. E. & Fitzgerald, P. B. Brain stimulation in psychiatry and its effects on cognition. Nat Rev Neurol 6, 267 (2010). 7. Chen, R. et al. Depression of motor cortex excitability by low‐frequency transcranial magnetic stimulation. Neurology 48, 1398 (1997). 8. Nguyen, J. et al. Repetitive transcranial magnetic stimulation combined with cognitive training for the treatment of Alzheimer’s disease. Neurophysiologie Clinique/Clinical Neurophysiology 47, 47 (2017). 9. O Doherty, J. E. et al. Active tactile exploration using a brain–machine–brain interface. Nature 479, 228 (2011). 10. Flesher, S. N. et al. Intracortical microstimulation of human somatosensory cortex. Sci Transl Med 8, 141r (2016). 11. Romo, R., Hernández, A., Zainos, A. & Salinas, E. Somatosensory discrimination based on cortical microstimulation. Nature 392, 387 (1998). 12. Min, B., Marzelli, M. J. & Yoo, S. Neuroimaging-based approaches in the brain-computer interface. Trends Biotechnol 28, 552 (2010). 13. Pais-Vieira,M.,Lebedev,M.,Kunicki,C.,Wang,J.&Nicolelis,M.A.Abrain-to-braininterfaceforreal-timesharingofsensorimotor information. Sci Rep-Uk 3, 1319 (2013). 14. Rao, R. P. et al. A direct brain-to-brain interface in humans. Plos One 9, e111332 (2014). 15. Yoo, S., Kim, H., Filandrianos, E., Taghados, S. J. & Park, S. Non-invasive brain-to-brain interface (BBI): establishing functional links between two brains. Plos One 8, e60410 (2013). 16. Li, G. & Zhang, D. Brain-computer interface controlled cyborg: establishing a functional information transfer pathway from human brain to cockroach brain. Plos One 11, e150667 (2016). 17. Feng, Z. et al. A remote control training system for rat navigation in complicated environment. J Zhejiang Univ-Sc A 8, 323 (2007). 18. Talwar, S. K. et al. Behavioural neuroscience: Rat navigation guided by remote control. Nature 417, 37 (2002). 19. Wang, Y. et al. Visual cue-guided rat cyborg for automatic navigation [research frontier]. IEEE Comput Intell M 10, 42 (2015). 20. Yu, Y. et al. Intelligence-augmented rat cyborgs in maze solving. Plos One 11, e147754 (2016). 21. Chavarriaga, R., Sobolewski, A. & Millán, J. D. R. Errare machinale est: the use of error-related potentials in brain-machine interfaces. Front Neurosci-Switz 8, 208 (2014). 22. Hermer-Vazquez, L. et al. Rapid learning and flexible memory in “habit” tasks in rats trained with brain stimulation reward. Physiol Behav 84, 753 (2005). 23. Paxinos, G. & Watson, C. The rat brain in stereotaxic coordinates. (Elsevier, Academic Press, Amsterdam, 2014). 24. Xu, K., Zhang, J., Zhou, H., Lee, J. C. T. & Zheng, X. A novel turning behavior control method for rat-robot through the stimulation of ventral posteromedial thalamic nucleus. Behav Brain Res 298, 150 (2016). 25. Martinez-Leon, J., Cano-Izquierdo, J. & Ibarrola, J. Are low cost Brain Computer Interface headsets ready for motor imagery applications? Expert Syst Appl 49, 136 (2016). 26. Pfurtscheller, G., Neuper, C., Flotzinger, D. & Pregenzer, M. EEG-based discrimination between imagination of right and left hand movement. Electroencephalography and clinical Neurophysiology 103, 642 (1997). 27. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G. & Vaughan, T. M. Brain–computer interfaces for communication and control. Clin Neurophysiol 113, 767 (2002). 28. Kumar, S., Mamun, K. & Sharma, A. CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI. Comput Biol Med 91, 231 (2017).

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Cryptocurrency: Markets 101
by grarpamp 26 Feb '23

26 Feb '23

How To Crypto 51: What is an order book, what are maker/taker fees, what are order types and what is slippage? How To Crypto 51: What is an order book, what are maker/taker fees, what are order types and what is slippage? A guide by MalletSwinging TLDR There is no TLDR for this post. I try to include them when possible but there is no way for me to summarize this information. This is a long, thorough and informative post so be prepared to spend some time reading it if you decide to continue. Intro Welcome to another post in my series, How To Crypto by MalletSwinging. I decided to write these posts because I see a lot of new Redditors on this sub every single day. I know how confusing and overwhelming this space can be and I want to make it easier for our greener members to navigate crypto without feeling too embarrassed to ask questions. In this post I am getting back to crypto basics. On my last post I received a comment from a reader who asked ‘what is a stop loss?’ It got me thinking that maybe instead of continuing with more complicated trading posts I should make a few that can act as a foundation for more advanced concepts I’ll introduce in later articles. Today we are going to talk about order types available on a centralized exchange, or CEX. A CEX is an exchange like Coinbase, Binance, Crypto.com, Kucoin or one of the many other smaller players in this space. We’ve seen centralized exchanges like FTX and Voyager go belly up recently and users of those exchanges have lost some or all of their crypto. This is why picking a reputable exchange to use as a fiat onramp is an important choice. Trading is also possible on a decentralized exchange (DEX) like Sushiswap, Uniswap, Bancor and others. Many of the concepts in this document pertain to DEX trades as well but for the purposes of this article we will be looking at CEX orders and order books only. I’m not here to debate CEX vs DEX or the (de)merits of each CEX; there are plenty of posts in this sub that discuss those. This is a purely informative post and contains no advice beyond what to expect when placing buy or sell orders. To learn any of these concepts we must first understand what an order book is. What is an order book? An order book is a list maintained by each centralized exchange that contains every current and active buy or sell order on a specific trading pair. What’s a trading pair you ask? Great question, Mallet! A trading pair contains exactly two assets. At least one of these on a CEX will always be a cryptocurrency. The other can be another cryptocurrency but it can also be a unit of fiat currency like USD or EUR. Many times the second asset will be a stablecoin like USDT, USDC or (gasp) BUSD. A trading pair allows you to trade one of the two assets for the other. If a trading pair is BTC/USD it means that I can trade BTC for USD or I can trade USD for BTC. These are the only two assets available to trade in this particular trading pair. If I have USDT and I want to trade it for BTC I need to look for BTC/USDT which is a completely separate pair from BTC/USD. Each trading pair has its own order book. An order book on a CEX for BTC/USD has every single current and active order that has been placed on that exchange to trade BTC for USD or USD for BTC. They do not cross over from one exchange to another, meaning the order book on Coinbase will have different order amounts and prices compared to the order book for the same pair on Binance. Let’s take a look at how this is represented visually on Binance.us: https://preview.redd.it/180sjlmnbdka1.png?width=577&format=png&auto=webp&v=… There is a lot of information to unpack here! First, the green numbers on the left represent buy orders. This means these are orders from users who have USD and want to buy BTC. The red numbers on the left represent sell orders. These are orders from users who have BTC and want to sell it for USD. These are real orders placed by accounts on Binance.us. Some people may have placed these orders manually but the majority of them are placed by bots or automated trading strategies. For practical purposes we don’t really care who placed these orders; we only care that they exist. Breakdown of what we are seeing in this order book: The green and red bars on the right show relative order book sizes of the crypto asset at the dollar price listed on the left. Since we are coming at this scientifically we will use the actual numbers instead of the bars. Bids The top green number ($24,880.40) represents the highest USD price someone is willing to pay for BTC. This line is known as a bid. Each green number represents a bid price. Each line represents real USD that someone has locked and is willing to pay to buy BTC. The white number directly to the right of the highest bid price (0.130726) represents how much BTC they are willing to buy at that price. If you look to the second white number right of the first white number you will see the total USD price ($3,252.52) that you would receive if you sold them that 0.130726 BTC at their bid price. These two numbers don’t necessarily mean that one person wants to buy all that BTC at that bid price; this can be one or more parties that cumulatively want to buy that amount of BTC at that bid price. As we go down the list of bids we see subsequently lower green numbers on each line. These represent the next highest bids currently in the order book. Note that they will always be in descending numeric order by price with the highest bid price at the top of the list. There are more bids (aka buy orders) below the lowest one on this list but the list is heavily truncated due to space constraints. Different exchanges will show different amounts of both buy and sell orders depending on how their site is designed. Most have the option to view only buy orders or only sell orders. Last sale price The large red number right above that ($24,880.40) represents the last sale price of any amount of BTC on Binance.us. Note that in this case it is the same as the highest bid price. This is not always the case but it is here. This likely means that a buy order at $24,880.40 was only partially filled and what we are seeing is the remainder of that buy order still on the order book. The red arrow down arrow to the right of the last sale price represents the direction the last sale price moved from the sale price before that. All this tells us is that over the last two trades the sale price of BTC went down by at least $0.01. If the arrow were green and pointed up it would tell us that over the last two trades the sale price of BTC went up by at least $0.01. If you were to look on TradingView or any other charting platform this sale price is what constitutes the current price of an asset. Asks Next, let’s look at the smallest red number directly above the last sale price. This line is known as an ask. The red number ($24,882.06) represents the lowest ask price which is the lowest price someone is willing to sell BTC, priced in USD. Each red number represents an ask price. Each line represents real BTC that someone has locked and is willing to sell for USD. The white number directly to the right of that (0.156284) represents the amount of BTC someone has listed for sale at the lowest ask price. This is real BTC that someone has locked for sale at the price listed on the ask line. *** Note to more advanced readers: There are enough arguments about whether CEXs actually have the crypto assets in question in this sub; please don’t bombard the comments section with this as it will just confuse new users. Let’s assume for the sake of this lesson that they do. The second white number directly to the right of the first white number is the total dollar amount it would cost to buy the entire 0.156284 of BTC at the lowest ask price of $24,882.06. As we go up the list of asks we see subsequently higher red numbers on each line. These represent the next lowest asks currently in the order book. Note that they will always be in ascending numeric order (from the bottom up) by price with the lowest bid price at the bottom of the red list. There are more asks (aka sell orders) above the highest one on this list but the list is heavily truncated due to space constraints. Different exchanges will show different amounts of both buy and sell orders depending on how their site is designed. Maker and taker fees If you look at any exchange’s fee structure you will see two different commission pricings; a maker fee and a taker fee. Since we are trading BTC/USD on Binance.us (or Binance.com) there are technically no fees for orders however most other trading pairs are charged a percentage fee on the total value of each trade that is successfully executed. As far as I know all non-Binance exchanges charge fees on all trades regardless of trading pair. Some do charge tiered fees and some give discounts for trading volume or for paying in their exchange token but that’s not really important to understand here. Here is the base Binance.us fee structure for pairs other than BTC/USD(T) or ETH/USD(T): https://preview.redd.it/noqhsgnubdka1.png?width=350&format=png&auto=webp&v=… Maker orders If you place an order on an order book that is not immediately filled but does remain on the exchange’s order book it is called a maker order. In other words, maker orders occur when you create liquidity on an order book by putting one of the two assets in a trading pair up to exchange for the other at a trade price that will not immediately be filled. Limit orders can be maker or taker orders. If you place a maker order and it is filled you are charged the maker percentage (in this case 0.3%) on the entire trade value by the exchange. Taker orders An order that removes liquidity from an order book immediately is considered a taker order. Market orders are always taker orders because they will always be filled immediately and will therefore take liquidity off an order book. Limit orders that are priced in such a way that they are filled immediately and remove liquidity from an order book will be considered taker orders. If you place a taker order you are charged the taker order percentage-based fee (in this case a whopping 0.45%) on the entire trade value by the exchange. It is possible for part of a limit order to be filled as a taker and the rest to be filled as a maker. It is also possible for part to be placed as a taker and for the rest to not get filled at all depending on how you place your order and what happens in order book by other parties after your order is placed. Order types Now that we understand the basics of how an order book works and how maker/taker fees are determined let’s take a look at order types. In crypto there are two main order types, limit and market. I will go over Stop-limit orders (aka conditional orders) in my next post about stop losses in the interest of keeping things simple here. All orders require an asset pair so for the sake of simplicity we will again use BTC/USD for our examples and assume we are looking at the same order book I used above. Market orders A market order is an order that allows you to fill in a dollar amount (in the case of a fiat or stablecoin-based pair like BTC/USD) and will purchase that much crypto at the best price available from the exchange’s order book. This option gives you the least amount of control over how much you pay for an asset. Market orders, especially larger ones and/or on low liquidity pairs, are subject to something called slippage which I will go over in a moment. Market orders are always taker orders. Let’s say I have $5,000 and assume the order book I used in the example above is currently in place. I want to buy $5,000 worth of BTC and I don’t care what price I pay for it as long as it’s close to the last sale price. I fill out my market order form like this: https://preview.redd.it/ldvcmlvxbdka1.png?width=974&format=png&auto=webp&v=… I can choose to place this as a total USD amount or a total BTC amount in the field market Total with the down arrow next to it. Here I’ve chosen total USD amount for the sake of being consistent. Here is the same order book for the sake of not have to scroll up or down: https://preview.redd.it/de4plawybdka1.png?width=577&format=png&auto=webp&v=… The moment press Buy I will purchase the exchange will buy $5,000 of BTC at the best available price. Assuming the order book was exactly as the image above at the moment I pressed Buy here I will buy: 0.156284 BTC at $24,882.06 for $3,888.670.007498 BTC at $24,882.73 for $186.570.037163 BTC at $24,883.30 for $924.76 This is a good segue into the next section, slippage. Slippage As you can see in the example above, the last sale price of BTC was $24,880.40 but our average buy price was around $2 higher than that. This is known as slippage. Slippage occurs when there is not enough liquidity on an order book to cover an entire buy or sell at the last sale price. In the example above there was a relatively low amount of slippage (less than 0.01%.) When trading high liquidity, high volume pairs in small amounts there is usually relatively low slippage like this. When trading relatively low liquidity, low volume pairs like JASMY/USDT there will usually be considerably more slippage because the order book is often less competitive and therefore more spread out. Note that if I had placed a sell market order for 0.1 BTC I would have experienced no slippage in this case as there is enough liquidity on the order book to cover my entire sell order at the last sale price. Slippage is something I personally usually try to avoid as I like to know exactly how much I’ll pay for something before I buy it. If you don’t like the sound of slippage either, welcome to the world of limit orders. For funsies, here is what a chart looks like when someone places a BIG market sell order on an order book and incurs a ton of slippage. You can see the long red wick indicating that a market sell order cleared out an order book. This drove the last sale price down before other others swept in and filled the order book back up, bringing the last sale price back into congruence with the rest of the chart. https://preview.redd.it/w03oyyh6fdka1.png?width=1333&format=png&auto=webp&v… Limit orders A limit order is a buy or sell order in which a user has a specific amount of one asset that they wish to trade for a specific amount of another asset. As I mentioned earlier these can be either maker or taker orders depending on how you place them Let’s say that we have $1,000 and we want to buy 0.05 BTC with that exact amount of dollars. A little basic math tells us that in order to buy 0.05 BTC with $1,000 the price for one BTC will need to be $20,000. Here’s the math behind that: [our dollar amount] / [our desired amount of BTC] = [price of BTC required for trade] / [one BTC] Since we are trying to figure out the price of BTC required for trade we can effectively make that variable X and then solve for said X. I hope you remember your high school algebra! $1,000 / 0.05 = X / 1 In this case X is equal to $20,000. If you get stuck on this it might be a good idea to do a quick refresher: https://www.khanacademy.org/math/algebra-basics This means that if I want to buy 0.05 BTC for $1,000 I need the price to come down from $24,880.40 to $20,000 and there needs to be at least 0.05 BTC available at $20,000. Since I don’t want to pay more than $20,000 but I also don’t want to sit at my computer or phone and wait for the price to drop I decide to use a limit order and leave it on the order book permanently as a GTC (good ‘til canceled) order. Here is an image of how I would place this order. The black boxes are me obfuscating some info I don’t want to share on here: https://preview.redd.it/r7c843t3cdka1.png?width=975&format=png&auto=webp&v=… If I fill in the price and BTC amount I want to purchase the total USD amount will be filled in automatically. You must have the USD amount in the Total box available and unlocked in your trading account in order to place this trade. When I press the green Buy BTC button the trade will be placed. The TIF (time in force) option specifies how long your order will remain on the order book. Binance.us gives these common options: GTC, or Good ‘til Canceled – this means that the order will stay on the order book in perpetuity, or until it is filled or canceled. If I had used the $1,000 example buy at $20,000 per BTC above as a GTC order my $1,000 will remain locked meaning I cannot use it for other trades or withdraw it from the exchange. If I had placed a GTC order for 1 BTC at $24,882.06 (assuming we are still using the order book above) I would have purchased 0.156284 BTC for $3,888.67 and the remainder or my order (in this case $20,993.39) would remain on the order book as a buy order for the remaining 0.843716 BTC at a price of $24,882.06. The $20,993.39 would be locked, unavailable for other trades or withdrawals. The trade that was executed for $3,888.67 would be considered a taker order and the other $20,993.39 would be considered a maker order. IOC, or Immediate or Cancel – this means that my order will be executed immediately. If it cannot be filled in full it will fill as much of the order as possible and then cancel the rest of the order. If I had used the $1,000 example buy at $20,000 per BTC above as an IOC order, since the current price of BTC is $24,880 none of my $1,000 order would be filled and my order would be canceled. If I had placed an IOC order for 1 BTC at $24,882.06 (assuming we are still using the order book above I would have purchased 0.156284 BTC for $3,888.67 and the rest of the order would be canceled. the remainder of my $24,882.06 (in this case $20,993.39) would remain in my account balance and it would be unlocked, available for other trades or withdrawal. The $3,888.67 I spent would be considered a taker order. FOK, or Fill or Kill – this means that if my entire order cannot be placed for the exact amount I specified, none of the order will be filled and it will be immediately canceled. If I had used the $1,000 example buy at $20,000 per BTC above, since the best available price for BTC is $24,882.06 none of my $1,000 order above would be filled and my order would be canceled. If I had placed a FOK order for 1 BTC at $24,882.06 (assuming we are still using the order book above) I would have purchased no BTC at all since there is not enough on the order book for me to buy 1 BTC at that price. The entirety of my $24,882.06 would remain in my account balance and it would be unlocked, available for other trades or withdrawal. Other order options There are some other options we can check here, namely Iceberg and Post Only. These options probably won’t be relevant to you as a newer trader but I’ll explain them anyway. Post only – this option means that your order will only be processed as a maker order and not a taker order. If you place an order as Post Only and it is impossible for it to be placed onto the order book without it being filled it will be immediately canceled. Iceberg – this option allows you to break a very large order up into smaller chunks to avoid disrupting a market with one giant buy or sell. As a newer trader it is unlikely that you will need this option, at least not during the beginning of your journey. Once you click this option it will allow you to specify a chunk size that will break your order up into multiple smaller, more obfuscated orders. Sell orders are identical to buy orders except you are interacting with the other side of the order book. I considered redoing all of these examples as sell orders but I think anyone still reading will understand the concept. What happens if I place a limit order that will clear multiple levels of an order book? Another great question! Thanks for asking, Mallet; you really ask the best questions. Let’s again assume this is our order book: https://preview.redd.it/6hgamiy6cdka1.png?width=577&format=png&auto=webp&v=… Let’s assume I want to buy 1 BTC and I’m willing to pay up to $24,883.66 for it but not a penny more. I fill out my form and press Buy BTC. https://preview.redd.it/z6zm4tt7cdka1.png?width=434&format=png&auto=webp&v=… As long as the order book has not changed I will immediately buy: 0.156284 BTC at $24,882.06 for $3,888.670.007498 BTC at $24,882.73 for $186.570.221031 BTC at $24,883.30 for $5,499.980.413122 BTC at $24,883.66 for $10,279.99 This means I will have purchased a cumulative total of 0.797935 BTC for a cumulative dollar amount of $19,855.21. The amount I have purchased here will be considered a taker order. I will then see the remainder my buy order appear on the order book for the remainder of my requested BTC (0.20207BTC) at a price of $24,883.66. If the remainder of my buy order is filled by someone else’s subsequent sell order this part of it will be considered a maker order. Conclusion That’s about it! At this point you hopefully have a clear understanding of how an order book works, how to determine the fees you will pay on trades and the difference between market and limit orders. Armed with this knowledge you might be ready to jump into one of my more advance articles: How to crypto 101: How to grid trade How to crypto 201: What is leverage? Stay tuned for my next post which will be How To Crypto 53: What is a conditional order and how do I use one to set up a stop loss? What is a stop loss, anyway? ***Edit: thank you for the awards, u/tommo_graham, u/rootpl and u/Bailszy! You absolutely didn't have to do that but they are much appreciated.

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Cryptocurrency: Attn: GovBankCorpPol - FYI, You Can't Stop Crypto
by grarpamp 26 Feb '23

26 Feb '23

https://www.youtube.com/watch?v=DjYbsq3FXfM https://v.redd.it/joqa2xfdl7ka1 https://gource.io/

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