#braincomputerinterfaced

[preponias]

Brain-computer interface device

#braincomputerinterfacedevice #braincomputerinterfaces #braincomputerinterfaced #braincomputer #paralysedpeople #paralysis #brainsignals #amyotrophiclateralsclerosis #Neurologicaldisorder #dailycurrentaffairs #currentaffairs #GeneralKnowledge #upsc #upscprelims2024 #upscprelims #upsc2023 #infographic

[taqato-alim]

Analysis of: "From Brain to AI and Back" (academic lecture by Ambuj Singh)

The term "document" in the following text refers to the video's subtitles.

Here is a summary of the key discussions:

The document describes advances in using brain signal recordings (fMRI) and machine learning to reconstruct images viewed by subjects.

Challenges include sparseness of data due to difficulties and costs of collecting extensive neural recordings from many subjects.

Researchers are working to develop robust models that can generalize reconstruction capabilities to new subjects with less extensive training data.

Applications in medical diagnosis and lie detection are possibilities, but risks of misuse and overpromising on capabilities must be carefully considered.

The genre of the document is an academic lecture presenting cutting-edge neuroscience and AI research progress to an informed audience.

Technical content is clearly explained at an advanced level with representative examples and discussion of challenges.

Ethical implications around informed consent, privacy, and dual-use concerns are acknowledged without overstating current capabilities.

While more information is needed, the presentation style and framing of topics skews towards empirical science over opinion or fiction.

A wide range of stakeholders stand to be impacted, so responsible development and governance of emerging neural technologies should involve multidisciplinary input.

Advancing both basic scientific understanding and more human-like machine learning is a long-term motivation driving continued innovation in this important field.

Here is a summary of the key points from the document:

The speaker discusses advances in using brain signal recordings (fMRI) to reconstruct images that a person is viewing by training AI/machine learning models.

An example is shown where the top row is the actual image viewed and the bottom row is the image reconstructed from the person's brain signals.

Larger datasets with brain recordings from multiple subjects are allowing better models to be developed that may generalize to new subjects.

Challenges include the sparseness of brain signal data due to the difficulty and costs of collecting it from many subjects.

A model is presented that maps brain signals to a joint embedding space of images and text, allowing reconstruction of novel images from new brain signals.

Examples are shown where the reconstructed images match fairly well or not as well depending on image details and semantics.

Issues around ethics, risks of misuse, and questions of explaining and improving the models are discussed.

Ongoing work aims to address challenges around transferring models between subjects and measuring reconstruction performance.

Based on the content and style of the document, it appears to be an academic lecture or presentation.

Key evidence points include:

The document consists primarily of a speaker talking and presenting slides/examples to an audience, as indicated by phrases like "Let me just start with this" and an applause at the end.

Technical topics from neuroscience and machine learning/AI are discussed in detail, such as fMRI brain recordings, reconstructing images from brain signals, modeling approaches, dataset descriptions, and challenges/questions in the field.

Academic concepts like human subjects studies, building models, transferring learning, and measuring performance are referred to.

The presentation of information is intended to educate the audience on the state of the field and cuttting edge research, not just entertain.

There are no narratives, characters or creative storytelling elements - it adheres strictly to explaining complex technical content.

Therefore, based on the instructive/educational style and focus on explaining advanced technical research, I would classify the genre of this document as an academic lecture/presentation intended to inform an audience about progress in the domain of neuroscience and artificial intelligence.

Here are some of the key stakeholders that could be affected by the content and topics discussed in this document:

Neuroscience researchers - Directly interested and impacted by advances reported from their field of study. Progress informs future research directions.

AI/machine learning researchers - Models and challenges discussed could inspire new research at the intersection of AI and neuroscience. Transfer of model techniques.

Medical/clinical communities - Potential applications to diagnosis, personalized medicine if models can accurately reconstruct brain states. Raises ethical issues.

Government research agencies - Fund future related research based on understanding of opportunities and challenges conveyed. Evaluates societal impacts.

General public - Increased understanding of science helps engagement but topics like brain reading raise public concerns around privacy, consent if translated beyond research.

Research participants - Summary does not disclose identities but motivates others to contribute data for progress. Well-being depends on responsible use of data.

Technology companies - Commercial opportunities from real-world applications but also reputational risks if misused. Data ownership questions.

Academic institutions - Collaborations, education of next generations of researchers on cutting-edge topics is enabled through open dissemination of knowledge.

Overall the document provides value to a wide range of stakeholders by communicating the state and potential implications of this multidisciplinary area of neuroscience and AI research. Responsible stewardship is important given several ethical considerations.

Here is a summary of the key points about advances in using brain signal recordings from the document:

Researchers are using fMRI to record brain signals as people view images, in order to reconstruct the viewed images from the brain activity patterns alone.

Machine learning models, especially neural networks, are allowing researchers to train mappings from brain signals to representations of semantic content like images and text.

Examples show the capability of reconstructing fairly accurate images that a person viewed, based solely on their brain activity recordings during viewing.

Larger datasets with multiple subjects are important to develop models that may generalize beyond individual subjects. However, fMRI data is very difficult and expensive to collect at scale.

Advances now allow reconstruction of more complex stimuli like full stories a person reads, not just static images.

Ongoing work aims to better understand brain learning processes and apply those principles to more human-like machine learning architectures.

Analyzing brain recordings also has potential applications for medical diagnosis, lie detection, personalized medicine as models improve.

Ethics must be carefully considered to ensure technologies for reading neural signals are developed and applied responsibly and avoid misuse or privacy violations.

So in summary, the document outlines significant progress using machine learning on brain recordings to reconstruct sensory experiences, with applications and open questions that can further both neuroscience and AI.

Here is a summary of the key points about the sparseness of brain signal data:

Collecting fMRI brain signal recordings from large numbers of subjects is extremely challenging and costly due to the resources and time required.

Each recording session with a single subject takes considerable time, as the number of images/stimuli that can be presented is limited within a session.

Collecting data from a single subject sufficient for training complex models may require recording sessions over multiple days or weeks.

As a result, publicly available datasets typically contain recordings from only a small number of subjects, often under 10.

The sparseness of data makes it difficult to develop robust models that will generalize well to new subjects that lack extensive recordings.

Researchers aim to better leverage smaller datasets by training models that can reconstruct novel stimuli from just a subset of training data per subject.

If successful, this could enable more efficient data collection, such as obtaining usable models from a subject after a single shorter session rather than many long sessions.

However, overcoming the sparseness of brain signal data remains a major challenge holding back progress in the field. Larger collaborative datasets are needed.

So in summary, sparse data severely limits progress but also motivates techniques to optimize learning from limited samples per subject.

Here is a summary of key points regarding risks of misuse from the document:

Advances in reconstructing cognitive states like images viewed from brain recordings raise potential dual-use concerns if technologies are not regulated.

Applications in medical diagnosis and lie detection could help many, but lead to issues if used without consent or for non-medical surveillance.

Overly ambitious claims about capabilities need to be avoided to prevent misleading expectations that could fuel inappropriate usage assumptions.

Privacy and security of sensitive neural data must be prioritized to prevent potential misuse for profiling, identification or manipulation without oversight.

Commercial pressures may incentivize premature deployment before understanding implications, so continued research is important.

Public education about limitations and complexities involved can help curb fears that fuel misuse if capabilities are overestimated.

Multidisciplinary cooperation ensures societal and ethical issues are considered proactively rather than reactively addressed later.

Governance frameworks may be needed to steer applications toward public benefit rather individual or state abuse of personal mental information.

The speaker acknowledges misuse risks to inform discussion and underscore the importance of ongoing examination of new neural technologies through multidisciplinary lens including ethics.

Based on the content and style of presentation, my evaluation of the document status is:

Science vs Fiction: Clearly grounded in science as it discusses ongoing empirical research studies using neuroimaging and machine learning techniques. No fictional or hypothetical elements.

Empirical vs Anecdotal: Empirical in nature, rooted in dataset collection from human subjects and quantitative modeling/evaluation, not personal experience. While data quantities are limited, research follows scientific method.

Fact vs Opinion: Primarily presents technical details and research findings as established facts from the literature. Does not advance strong personal opinions beyond realistic discussion of challenges. Maintains an objective tone.

Objective vs Subjective: Remains objective in explaining research accomplishments and questions objectively without emotive language or bias. Any subjective experience like dataset limitations are clearly labeled as such. Presentation aims for factual information transfer.

In summary, while farther research is still ongoing, the document presents the current state of a scientific research domain factually based on empirical evidence and quantitative analysis published in the field. It does not intermingle non-factual elements or stray from an objective reporting of the topic at hand. The status therefore skews heavily toward science, empiricism, fact and objectivity.

I did not identify any logical fallacies in the content or arguments presented in this document.

The speaker objectively summarizes established research results and proposals, without making assertions that require logical scrutiny.

Some key reasons why logical fallacies are not present include:

No causal claims are made without proper evidence or empirical support from published studies. Progress is tied to concrete research efforts.

No arguments are constructed through flawed reasoning or non sequiturs. Concepts are explained clearly and build upon each other logically.

Comparisons to other approaches are fair without strawman misrepresentations of opposing ideas. Related work is acknowledged.

Limitations and open challenges are candidly outlined rather than ignored or obscured through fallacious defense.

No emotional, exaggerated or politically motivated language is used that could constitute emotional appeals. Tone remains factual.

No false dilemmas or false attributions of complexity are presented. Discussions appropriately scope multi-factor research questions.

No hasty generalizations are made from limited evidence. Data constraints are clearly communicated.

Overall, through objectively summarizing established neuroscience and AI research domains and techniques, without engaging in argument or debate, I did not find any logical fallacies employed in this academic information-sharing document. The focus appears to be factual reporting of progress and issues, avoiding fallacious reasoning.

Here is an evaluation of the ethics addressed and considered in the document:

Informed consent is implied but not explicitly discussed for research participants. Given the sensitivity of brain data, proper protocols were likely followed.

Privacy and anonymity of participants is a concern, but cannot be fully assessed without more details on the dataset and review process.

Potential dual-use issues around brain reading/reconstruction technologies are identifed by discussing applications but also worries about misuse or lack of oversight. This shows awareness of ethical implications.

Limitations and challenges and openly discussed, avoiding overpromising on capabilities. This establishes credibility and sets appropriate expectations.

Societal impacts and usage beyond research (e.g. diagnostics) are flagged as requiring careful consideration of risks like surveillance, discrimination if not regulated properly.

No claims are made without empirical evidence, showing results are driven by facts rather than desires which can bias judgment. Objectivity helps ethical analysis.

Multidisciplinary collaboration is emphasized , suggesting diverse viewpoints were incorporated into the research process.

Overall, while full review details are not provided, the document demonstrates an awareness of important ethical considerations around privacy, consent and responsible development for these sensitive types of neural data and technologies. A balanced assessment of opportunities and risks is conveyed.

Here are the usual evaluation criteria for an academic lecture/presentation genre and my evaluation of this document based on each criteria:

Clarity of explanation: The concepts and technical details are explained clearly without jargon. Examples enhance understanding. Overall the content is presented in a clear, logical manner.

Depth of technical knowledge: The speaker demonstrates thorough expertise and up-to-date knowledge of the neuroscience and AI topics discussed, including datasets, modeling approaches, challenges and future directions.

Organization of information: The presentation flows in a logical sequence, with intro/overview, detailed examples, related work, challenges/future work. Concepts build upon each other well.

Engagement of audience: While an oral delivery is missing, the document seeks to engage the audience through rhetorical questions, previews/reviews of upcoming points. Visuals would enhance engagement if available.

Persuasiveness of argument: A compelling case is made for the value and progress of this important multidisciplinary research area. Challenges are realistically discussed alongside accomplishments.

Timeliness and relevance: This is a cutting-edge topic at the forefront of neuroscience and AI. Advances have clear implications for the fields and wider society.

Overall, based on the evaluation criteria for an academic lecture, this document demonstrates strong technical expertise, clear explanations, logical organization and timely relevance to communicate progress in the domain effectively to an informed audience. Some engagement could be further enhanced with accompanying visual/oral presentation.

mjsMlb20fS2YW1b9lqnN

#Neuroscience #Brainimaging #Neurotechnology #FMRI #Neuroethics #BrainComputerInterfaces #AIethics #MachineLearning #NeuralNetworks #DeepLearning #DataPrivacy #InformationSecurity #DigitalHealth #MentalHealth #Diagnostics #PersonalizedMedicine #DualUseTech #ResearchEthics #ScienceCommunication #Interdisciplinary #Policymaking #Regulation #ResponsibleInnovation #Healthcare #Education #InformedConsent #Youtube

[jointhebrigade]

Neuralink has started human clinical trials for their Brain-Computer Interface (BCI) device called N1. The company just secured another $43 million in funding, bringing their total capital raised to more than $320 million. The PRIME Study (short for Precise Robotically Implanted Brain-Computer Interface) is for patients that have limited or no ability to use both hands due to cervical spinal cord injury or amyotrophic lateral sclerosis (ALS). Neuralink says it plans to perform 11 surgeries in 2024, 27 in 2025, 79 in 2026 and more than 22,000 by 2030. Once approved by the FDA, each implant surgery is expected to cost around $40,000 including exams, the device and labor. The company forecasts annual revenue as high as $100 million within five years (2028). In the short-term Neuralink is aiming to treat serious brain diseases, but the eventual goal is to provide human enhancement and achieve "symbiosis with artificial intelligence.” 😳

#shorts Neuralink neurotechnology braincomputerinterfaces BCI Neuroprosthetics augmentedhuman cyborg Robot Robotics Robots AI artificialintel

[jjbizconsult]

Neuralink Stock: The Future of Brain-Computer Interface Technology is Here!

#NeuralinkStock #BrainComputerInterface #ElonMusk #BCI #FutureofTech #MedicalTechnology #Neuroscience #Paralysis #ALS #Quadriplegia

[jitzbala]

Unlock Human Potential: The Future with Neuralink

Neuralink represents a convergence of science fiction and reality, pushing the boundaries of human potential and sparking a new era of exploration at the intersection of technology and consciousness.

Hey Everyone! Ever felt trapped in the clutches of the infamous ‘Writer’s Block’? Well, I’ve been there, but leave it to Elon Musk to jolt me out of that slumber. Today, the man behind Tesla, SpaceX, and PayPal announced something mind-bending – Neuralink. Trust Musk to bring the extraordinary to our mundane lives. Pic: Generated for the blog using AI Ever wondered about controlling your…

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#BCI #BlogPost #BrainComputerInterface #Elon Musk #FutureTech #HumanEnhancement #Neuralink #NeuralinkRevolution #Sci-Fi #ScienceFictionReality #ShareTheBlog #StayInformed

[todayonglobe]

Paralyzed Individuals Can Now Speak Using AI

Read more:👇

#Ai #artificialintelligence #ParalyzedIndividuals #BrainComputerInterfaces #Brainreadingdevices #todayonglobenews #todayonglobe #tognews #tog #news #dailynews #dailynewsupdate #trendingnews #breakingnews #latestnews

[gardarandotcom]

(via Mercedes-Benz VISION AVTR: A Concept Car Inspired by the Future of Mobility)

#sustainable #connected #intelligent #conceptcar #futureofmobility #biomimicry #selfdriving #braincomputerinterface #crabwalk #visionavtr #mercedesbenz

[aserougi]

Here’s my guess: Neuralink will unveil a vision implant at today’s “show and tell”

t.ly/m_Jb http://dlvr.it/Sdd4rr t.ly/9DBh

#Braincomputerinterface #Humansandtechnology

[moremedtech]

Brainwave-reading implant lets paralyzed man spell out 1,100 words

Brainwave-reading implant lets paralyzed man spell out 1,100 words. A paralyzed man who cannot speak or type was able to spell out over 1,000 words using a neuroprosthetic device that translates his brain waves into full sentences, US researchers said. "Anything is possible," was one of the man's favorite phrases to spell out, said the first author of a new study on the research, Sean Metzger of the University of California San Francisco (UCSF). Last year the team of UCSF researchers showed that a brain implant called a brain-computer interface could translate 50 very common words when the man attempted to say them in full. In the new study, published in the journal Nature Communications, they were able to decode him silently miming the 26 letters of the phonetic alphabet. "So if he was trying to say 'cat', he would say charlie-alpha-tango," Metzger told AFP A spelling interface then used language-modeling to crunch the data in real time, working out possible words or errors. The researchers were able to decode more than 1,150 words, which represent "over 85 percent of the content in natural English sentences", the study said. They simulated that this vocabulary could be extended to more than 9,000 words, "which is basically the number of words most people use in a year," Metzger said. The device decoded around 29 characters a minute, with an error rate of six percent. That worked out to be around seven words a minute. The man is referred to as BRAVO1, as the first participant of the Brain-Computer Interface Restoration of Arm and Voice trial. Now in his late 30s, he suffered a stroke when he was 20 that left him with anarthria—the inability to speak intelligibly, though his cognitive function remained intact. He normally communicates by using a pointer attached to a baseball cap to poke at letters on a screen. In 2019, the researchers surgically implanted a high-density electrode on the surface of his brain, over the speech motor cortex. Via a port embedded in his skull, they have since been able to monitor the different electrical patterns produced when he tries to say varying words or letters.

'Unique advantage'

Metzger said that BRAVO1 "really enjoyed using this device because he's able to communicate quickly and easily with us". One of the best parts of the study was when BRAVO1 was told to spell out "whatever he wants", Metzger said. "I got to learn a good amount about him," said Metzger. Among BRAVO1's surprising comments was that "he really didn't like the food where he lives," Metzger added. Last year a brain-computer interface developed at Stanford University was able to decode 18 words a minute when a participant imagined handwriting. But Metzger said their speech-based approach has a "unique advantage". The 50 commonly used words—which the participant silently speaks in full—could be used for many interactions, while rarer words could be spelled out, offering the "best of both worlds", he said. The research, which still needs to be confirmed in other participants, is still some way off from becoming available to the thousands of people who lose the ability to talk due to strokes, accidents or disease every year. Patrick Degenaar, a neuroprosthetics professor at the UK's Newcastle University who was not involved in the research, hailed the "very impressive results". Because neuroprosthetic surgery is "highly invasive and has risks", such a device would likely only be used by a very small number of people in the immediate future, he told AFP. https://youtu.be/z4bpgXCAokw More information: Sean L. Metzger et al, Generalizable spelling using a speech neuroprosthesis in an individual with severe limb and vocal paralysis, Nature Communications (2022). DOI: 10.1038/s41467-022-33611-3 Journal information: Nature Communications Read the full article

#Brain #braincomputerinterface #medtech #medtechnews #neurology #neuroprosthetic

[trispagyrist]

I’m not sure what to make of it…. I was recently awarded beta access to the Dall.E AI image generation system. This particular image came up from one of my prompts. It is indeed odd that it looks a little like me, I did not give it inputs based on my image. My hope is that I can use this system to develop concepts which I can then reproduce in traditional media (ie a painting or sculpture). That way it will truly be a collaboration. Man and Machine taking another step towards integration of Mind. It’s an exciting and strange time to be alive. #aigeneratedart #dalleaiart #integration #braincomputerinterface https://www.instagram.com/p/CghSmBSuegc/?igshid=NGJjMDIxMWI=

#aigeneratedart #dalleaiart #integration #braincomputerinterface

[teslainthegongyt]

Mastering Computer Control with Neuralink Chip: Mind-Blowing Innovations!

Witness the incredible power of the Neuralink chip as Noel demonstrates how he controls the cursor on his computer with his mind alone. Say goodbye to physical limitations and join the future of technology! #NeuralinkChip #MindControl #ComputerInnovation #BrainComputerInterface #CuttingEdgeTech #FutureTechnology #DigitalAdvancement #Neuroscience #TechInventions #HumanMachineInterface from Tesla…

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#august2023 #automativemuseum #cybertruck #electriccar #mostwanted #petersen #Teslabot #TeslaInTheGong #teslamodely #teslaupdates #topupgrades

[globalinsightblog]

🧠🔌 Bridging Minds and Machines: Exploring the Cutting-Edge Brain-Computer Interface Market! 🔌🧠

Embark on a journey into the realm of neuroscience and technology, where the boundaries between mind and machine blur, and the possibilities are as limitless as the human imagination! 🚀💭

🔍 Unlocking the Power of the Mind

Imagine controlling computers, prosthetics, and even virtual environments with nothing but your thoughts. That's the promise of brain-computer interfaces (BCIs), a revolutionary technology that allows direct communication between the brain and external devices. From restoring mobility to enhancing cognitive abilities, the potential applications are as vast as the human mind itself. 💡🌐

🌈 The Rise of Neurotech

As advancements in neuroscience and computing converge, the brain-computer interface market is experiencing unprecedented growth. From startups to tech giants, innovators around the globe are racing to develop the next generation of BCIs, pushing the boundaries of what's possible and redefining the way we interact with technology. 🌟💻

🚀 Empowering Humanity

Beyond its technological marvels, the true promise of brain-computer interfaces lies in their ability to empower individuals with disabilities, unlock new forms of expression, and enhance human capabilities in ways previously unimaginable. Whether it's enabling paralyzed individuals to walk again or allowing artists to create with their minds, BCIs are transforming lives and shaping the future of human-computer interaction. 💪🎨

💬 Join the Conversation

How do you envision the future of brain-computer interfaces shaping our relationship with technology and each other? Share your thoughts, questions, and dreams for a world where minds and machines work in harmony. Let's spark a dialogue and imagine the possibilities together! 💬🌈

#BrainComputerInterface #Neurotech #TechTrends #FutureTech #MindOverMatter 🧠🚀

[powerixnews]

10 UNBELIEVABLE Mysteries Elongated Skulls, Sentient AI, Cloned Dogs! #...

Uncover the shocking truth behind ancient elongated skulls in Peru, mysterious disappearances in the Bermuda Triangle, and hidden secrets beneath Vatican City. Is alien encounter real? Or is there an earthbound explanation? Plus, witness the rise of a sentient AI and the potential for a robot uprising. Explore the breakthrough in brain-computer interfaces, the ethical implications of animal cloning, and a town at war with squirrels. Stay till the end to discover the mystery of a million-dollar treasure hidden in an attic wall! #Mysteries #AlienEncounter #SentientAI #ClonedDogs #BermudaTriangle #VaticanCity #RobotUprising #BrainComputerInterface #AnimalCloning #TreasureHunt #SquirrelWar

#youtube

[icnnetwork]

#ChinaNews चीन इंसानी दिमाग को गुलाम बनाने की कोशिश में लगा,बना रहा है ब्रेन चिप पढ़िए रिपोर्ट में…

#BrainComputerInterface #elonmusk

#neuralink #ChinaTech #icnewsnetwork

#indiacorenews #icnewsnetwork #google news #breaking news #update #news

[rodspurethoughts]

Brain-Computer Interface Helps Immobilized Patients Control Devices with Thoughts

Exciting news! Researchers at Aalto University are developing a brain-computer interface to help immobilized patients control devices with their thoughts. #braincomputerinterface #healthtech

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#Bittium Biosignals #brain-computer interface #EEG technology #Finnish Research Impact Foundation. #immobilized patients #mTMS #multi-locus transcranial magnetic stimulation #neurological conditions #real-time data streaming

[dineshbus-blog]

In-depth Case Study About “Neuroprosthesis”

Neuroprosthesis is the process of using direct electric stimulation to enable proper functioning of the nervous system. Neuroprosthetic devices supplements the input or the output signals to the neural system, enabling the individual to carry out proper functioning and physical activities. Some of the purposes which involve the use of neuroprosthesis include, techniques for bladder and bowel control, deep brain stimulation, and restoration of mobility and respiration to paralyzed individuals.

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Rise in the number of individuals suffering from neurological disorders and increasing incidence of amputations due to road accidents and injuries are expected to fuel the growth of the neuroprosthesis market during the forecast period. Moreover, increasing applications of neuroprosthesis is anticipated to offer significant growth opportunities in the market.

North America is expected to contribute to the largest share in the neuroprosthesis market in the coming years, due to increasing cases of neurological disorders in the region. Also, Asia Pacific is anticipated to witness steady growth during the forecast period, owing to the growing investment from the leading manufacturers in the growing economies of the region such as China and Japan.

The report also includes the profiles of key neuroprosthesis companies along with their SWOT analysis and market strategies. In addition, the report focuses on leading industry players with information such as company profiles, components and technique offered, financial information of last 3 years, key development in past five years. The report also provides exhaustive PEST analysis for all five regions namely; North America, Europe, APAC, MEA and South & Central America after evaluating political, economic, social and technological factors effecting the neuroprosthesis market in these regions.

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Some of the key players operating in the neuroprosthesis Study:

1. Abbott 2. Boston Scientific Corporation 3. Cochlear Ltd. 4. LivaNova PLC 5. MED EL 6. Medtronic 7. NeuroPace, Inc. 8. Nevro Corp. 9. Second Sight Medical Products, Inc. 10. Sonova

Source: The Insight Partners

#assistivetechnology #neuralinterface #neuroprosthesis #braincomputerinterface #electricstimulation