<a href="https://scienceexchange.caltech.edu/topics/neuroscience/neuroscience-experts/brain-machine-interfaces-richard-andersen">Brain–machine interfaces</a> (BMIs) have enabled a handful of test participants who are unable to move or speak to communicate simply by thinking. An implanted device picks up the neural signals associated with a particular thought and converts them into control signals that are fed into a computer or a robotic limb. For example, a quadriplegic person is asked to think about moving a cursor on a computer screen. Once the BMI is trained to recognize the neurological activity as this intent, the person's thought is transmitted via the BMI to carry out the action of moving the cursor. BMIs currently in an experimental phase may also consist of robotic limbs that can execute manual tasks as instructed by a disabled person's thoughts alone.The hardware necessary for this incredible feat is a computer—either freestanding or within a robotic device—and an implant in the brain of the person who is using BMI technology to communicate their intent through their thoughts. At Caltech, researchers use implants that consist of arrays of 100 microelectrodes mounted on a 4x4 mm chip. The microelectrodes are typically 1.5 mm long and penetrate into the brain's cortex, where they can record the activity of individual neurons.Unfortunately, the performance of these microelectrode arrays is not consistent and degrades over time. To overcome this challenge, Caltech's&nbsp;<a href="https://www.ee.caltech.edu/people/azita">Azita Emami</a>, the Andrew and Peggy Cherng Professor of Electrical Engineering and Medical Engineering and director of the Center for Sensing to Intelligence (S2I), and her colleagues have used machine learning to effectively interpret the neuronal signals picked up by older implants."Not only do we observe day-to-day variations, but over time the performance of brain–computer interfaces degrade for a variety of reasons," Emami says. "There may be a small movement of the implant or its electrodes. The electrodes themselves may deteriorate or become encapsulated in brain tissue. Some people think that over time the neurons move away from the implant because they react to it as a foreign object in the brain. For whatever reason, the signals we receive become noisier."When a BMI is first set up, the microelectrode array produces a signal characterized by strong action potentials that appear like spikes in the recordings. Once this strong signal is no longer detected by the microelectrode array—that is, when the feedback from the array becomes noisier and neural spikes are no longer clearly detected—it is a far trickier task to link a pattern of neural activity from more distant neurons to a specific intent that can be successfully transmitted to a computer or other device. Researchers have tried to identify alternative signals, such as so-called threshold crossings or local field potentials recorded from distant neurons. One approach has been to use wavelets that measure small oscillations in neuronal activity. But the success of wavelets and other methods has been limited.Now Emami and her colleagues have found that by applying machine learning, BMIs can be trained to interpret data from neural activity even after the signal from an implant has become less clear.Benyamin Haghi, formerly a graduate student in the Emami lab explains: "Where before we relied on counting neural spikes, we have now created a neural network that automatically extracts information from the entire neural signal, from all the little dips and picks and changes in the signal, and converts this into the intent of the patient." Emami adds, "Over time the BMI has been trained on both a signal that is neural activity and a signal that looks like noise, and is therefore able to interpret the user's intent."Emami describes the experience of one participant, JJ, who lost mobility due to a vehicle accident. "When we first started working with him, his implant was three years old, and it had already degraded. We were thinking of removing the implant, but with our new algorithm, he's happy with continuing to use the system he already has. JJ can move a cursor very precisely on a grid, just as he did when the implant was new. He can play video games and control a computer environment that mimics driving."The team's algorithm is called FENet, for Feature Extraction Network. Remarkably, it can be trained on data from one patient and then used successfully in another. "This means that there is some fundamental type of information in the neural data that we are picking up," Emami says. Not only that, FENet can generalize across different brain regions and types of electrodes and be easily incorporated into existing BMIs.Richard Andersen, the James G. Boswell Professor of Neuroscience and leadership chair and director of the T&amp;C Chen Brain-Machine Interface Center, says "FENet has already extended our clinical study with JJ by two years. BMI research is a perfect field for interdisciplinary research, in this case melding the disciplines of engineering, computer science, and neuroscience."Today's BMIs require a manual connection from the electrode implant to a connector that then links with wires that lead to a microsystem that processes the raw data before sending it to a computer—the visual interface the patient manipulates. "This is quite a cumbersome system," Emami says. "Our goal now, after the creation of FENet to better interpret brain signals, is to miniaturize the system so that one day it can be a wearable or an implant that communicates wirelessly with the computer."<hr>This research was published in Nature Biomedical Engineering under the title "<a href="https://rdcu.be/d2D2L">Enhanced control of a brain-machine interface by tetraplegic participants via neural-network-mediated feature extraction</a>." Co-authors include Andersen; Emami; Haghi; Albert Yan Huang from the Emami lab; members of professional staff Tyson Aflalo and Spencer Kellis, postdoc Jorge A. Gamez de Leon, and graduate student Charles Guan from the Andersen lab; and Nader Pouratian from UCLA Neurosurgery. Funding was provided by the National Institutes of Health, Caltech's S2I, the T&amp;C Chen Brain-Machine Interface Center at Caltech, the Boswell Foundation, the Braun Foundation, and the Heritage Medical Research Institute.

Brain–machine interfaces (BMIs) have enabled a handful of test participants who are unable to move or speak to communicate simply by thinking.

Improving Brain-Machine Interfaces with Machine Learning

<a href="https://seas.harvard.edu/person/jia-liu">Jia Liu</a>, Assistant Professor of Bioengineering;&nbsp;<a href="https://seas.harvard.edu/person/na-li">Na Li</a>, Winokur Family Professor of Electrical Engineering and Applied Mathematics; and&nbsp;<a href="https://seas.harvard.edu/person/petros-koumoutsakos">Petros Koumoutsakos</a>, Herbert S. Winokur, Jr. Professor of Computing in Science and Engineering at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), have been awarded an Emerging Frontiers in Research and Innovation (EFRI) grant from the National Science Foundation. The $2 million grant will support research into developing a bio-symbiotic system that mimics the scalability, adaptability, and efficiency of biological neural networks capable of performing long-term, complex computational tasks.The research is a collaboration between SEAS, the Wyss Institute for Biologically Inspired Engineering at Harvard University, and the University of Texas at Austin.The grant is part of the Emerging Frontiers in Research and Innovation (EFRI): Biocomputing through EnGINeering Organoid Intelligence program, which supports research and development of organoid intelligence systems and biocomputing research.At SEAS, Liu’s group brings together researchers from a range of disciplines to develop advanced bioelectronics that integrate nanoelectronics, soft electronics, genetic and genomic engineering, and machine learning to interface with biological systems. Previously, Liu’s group demonstrated the integration of brain organoids with soft and flexible electronics as cyborg organoids for recording and controlling brain organoid development. With the EFRI grant, Liu and his team will test the cyborg brain organoid’s ability to perform computational tasks, with the goal of inspiring new AI algorithms, enhancing data processing efficiency, and advancing machine learning beyond current capabilities.“Current artificial intelligence and machine learning technologies do not match the adaptability and efficiency of biological systems,” said Liu. “Our goal is to develop a seamlessly integrated biological and electronics system for long-term 3D neural network computing, truly merging nature and machine intelligence together.""NSF's investment will lead to biological computing with superior power and efficiency by harnessing the mechanisms behind complex biological behavior for smart systems," said Susan Margulies, NSF Assistant Director for Engineering. "Advances in biocomputing will open new opportunities for artificial intelligence, biotechnology, and more sustainable computing."

Liu and team receive NSF EFRI grant for biocomputing research

Harnessing scalability, adaptability, and efficiency of biological neural networks through flexible brain-computer interfaces

Brain-machine interfaces (BMIs) have emerged as a promising solution for restoring communication and control to individuals with severe motor impairments. Traditionally, these systems have been bulky, power-intensive, and limited in their practical applications. Researchers at EPFL have developed the first high-performance, Miniaturized Brain-Machine Interface (MiBMI), offering an extremely small, low-power, highly accurate, and versatile solution. Published in <a href="https://ieeexplore.ieee.org/document/10643873" target="_blank" rel="noopener">the latest issue of the IEEE Journal of Solid-State Circuits</a> and presented at the International Solid-State Circuits Conference, the MiBMI not only enhances the efficiency and scalability of brain-machine interfaces but also paves the way for practical, fully implantable devices. This technology holds the potential to significantly improve the quality of life for patients with conditions such as amyotrophic lateral sclerosis (ALS) and spinal cord injuries.<blockquote>This advancement brings us closer to practical, implantable solutions.<footer>Mahsa Shoaran</footer></blockquote>The MiBMI's small size and low power are key features, making the system suitable for implantable applications. Its minimal invasiveness ensures safety and practicality for use in clinical and real-life settings. It is also a fully integrated system, meaning that the recording and processing are done on two extremely small chips with a total area of 8mm2. Thisis the latest in a new class of low-power BMI devices developed at Mahsa Shoaran’s Integrated Neurotechnologies Laboratory (<a href="https://www.epfl.ch/labs/inl/" target="_blank">INL</a>) at EPFL’s IEM and Neuro X institutes.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzI1NTgzMDExNzczLTI4ODE1MWE4LmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">Miniaturized Brain-Machine Interface (MiBMI) - 2024 EPFL / Lundi13 - CC-BY-SA 4.0“MiBMI allows us to convert intricate neural activity into readable text with high accuracy and low power consumption. This advancement brings us closer to practical, implantable solutions that can significantly enhance communication abilities for individuals with severe motor impairments,” says Shoaran.<h3 skip="true">Brain-to-text conversion involves decoding neural signals generated when a person imagines writing letters or words. In this process, electrodes implanted in the brain record neural activity associated with the motor actions of handwriting. The MiBMI chipset then processes these signals in real-time, translating the brain’s intended hand movements into corresponding digital text. This technology allows individuals, especially those with locked-in syndrome and other severe motor impairments, to communicate by simply thinking about writing, with the interface converting their thoughts into readable text on a screen.</h3><blockquote id="isPasted">The chip converts handwriting activity into text with an impressive 91% accuracy.<footer>Mohammed Ali Shaeri</footer></blockquote>“While the chip has not yet been integrated into a working BMI, it has processed data from previous live recordings, such as those from the Shenoy lab at Stanford, converting handwriting activity into text with an impressive 91% accuracy,” says lead author Mohammed Ali Shaeri. The chip can currently decode up to 31 different characters, an achievement unmatched by any other integrated systems. “We are confident that we can decode up to 100 characters, but a handwriting dataset with more characters is not yet available,” adds Shaeri.Current BMIs record the data from electrodes implanted in the brain and then send these signals to a separate computer to do the decoding. The MiBMI chips records the data but also processes the information in real time—integrating a 192-channel neural recording system with a 512-channel neural decoder. This neurotechnological breakthrough is a feat of extreme miniaturization that combines expertise in integrated circuits, neural engineering, and artificial intelligence. This innovation is particularly exciting in the emerging era of neurotech startups in the BMI domain, where integration and miniaturization are key focuses. EPFL's MiBMI offers promising insights and potential for the future of the field.<blockquote>Our goal is to develop a versatile BMI that can be tailored to various neurological disorders, providing a broader range of solutions for patients.<footer>Mahsa Shoaran</footer></blockquote>To be able to process the massive amount of information picked up by the electrodes on the miniaturized BMI, the researchers had to take a completely different approach to data analysis. They discovered that the brain activity for each letter, when the patient imagines writing it by hand, contains very specific markers, which the researchers have named distinctive neural codes (DNCs). Instead of processing thousands of bytes of data for each letter, the microchip only needs to process the DNCs, which are around a hundred bytes. This makes the system fast, accurate, and with low-power consumption. This breakthrough also allows for faster training times, making learning how to use the BMI easier and more accessible.Collaborations with other teams at EPFL’s Neuro-X and IEM Institutes, such as with the laboratories of Grégoire Courtine, Silvestro Micera, Stéphanie Lacour, and David Atienza promise to create the next generation of integrated BMI systems. Shoaran, Shaeri and their team are exploring various applications for the MiBMI system beyond handwriting recognition. “We are collaborating with other research groups to test the system in different contexts, such as speech decoding and movement control. Our goal is to develop a versatile BMI that can be tailored to various neurological disorders, providing a broader range of solutions for patients,” says Shoaran.<hr>ReferencesM. A. Shaeri, U. Shin, A. Yadav, R. Caramellino, G. Rainer and M. Shoaran, "MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes," 2024 IEEE International Solid-State Circuits Conference (ISSCC), San Francisco, CA, USA, 2024, pp. 546-548,&nbsp;<a href="https://ieeexplore.ieee.org/document/10643873/" target="_blank" rel="noopener">doi: 10.1109/ISSCC49657.2024.10454533</a>.M. A. Shaeri, U. Shin, A. Yadav, R. Caramellino, G. Rainer, M. Shoaran, “A 2.46mm2 Miniaturized Brain-Machine Interface (MiBMI) Enabling 31-Class Brain-to-Text Decoding”, in IEEE Journal of Solid-State Circuits (JSSC), 2024,&nbsp;<a href="https://ieeexplore.ieee.org/document/10643873" target="_blank" rel="noopener">doi: 10.1109/JSSC.2024.3443254</a>

Researchers from EPFL have developed a next-generation miniaturized brain-machine interface - 2024 EPFL / Lundi13 - CC-BY-SA 4.0

Researchers from EPFL have developed a next-generation miniaturized brain-machine interface capable of direct brain-to-text communication on tiny silicon chips.

An entire brain-machine interface on a chip

Brain-computer interfaces, or BCIs, hold immense potential for individuals with a wide range of neurological conditions, but the road to implementation is long and nuanced for both the invasive and noninvasive versions of the technology. <a href="https://engineering.cmu.edu/directory/bios/he-bin.html" target="_blank">Bin He</a> of Carnegie Mellon University is highly driven to improve noninvasive BCIs, and his lab uses an innovative electroencephalogram (EEG) wearable to push the boundaries of what’s possible. For the first time on record, the group successfully integrated a novel focused ultrasound stimulation to realize bidirectional BCI that both encodes and decodes brain waves using machine learning in a study with 25 human subjects. This work opens up a new avenue to significantly enhance not only the signal quality, but also, overall nonivasive BCI performance by stimulating targeted neural circuits.Noninvasive BCI is lauded for its merits of being cheap, safe, and virtually applicable to everyone, but because signals are recorded over the scalp versus inside the brain, low signal quality presents some limitations. The He group is exploring ways to improve the effectiveness of noninvasive BCIs and, over time, has used deep learning approaches to decode what an individual was thinking and then facilitate control of a cursor or robotic arm.<iframe width="640" height="360" src="https://www.youtube.com/embed/EoKHw6bdoZs?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true">&nbsp;&nbsp;</iframe>In their latest research, published in <a href="https://www.nature.com/articles/s41467-024-48576-8.epdf?sharing_token=l63IL0ujKJwg9YW5RiuvmdRgN0jAjWel9jnR3ZoTv0N_uPWXwoTWkVO_kYNoQfCvZ9eb-1HxHFUK4NM7oxILM0CfREmenUY3abynvgquHLRn_7RPWL1rZyFXqJCnBSQm10CXtHA62yRq2BlSAcVQCigM1TtLWtL1uYkOTXn0ObY%3D" rel="noopener" target="_blank">Nature CommunicationsOpens in new window</a>,&nbsp;the He group demonstrated that through precision noninvasive neuromodulation using focused ultrasound, the performance of a BCI could be improved for communication.“This paper reports a breakthrough in noninvasive BCIs by integrating a novel focused ultrasound stimulation to realize bidirectional BCI functionality,” explained Bin He, professor of <a href="https://www.cmu.edu/bme/" rel="noopener" target="_blank">biomedical engineeringOpens in new window</a> at Carnegie Mellon University. “Using a communication prosthetic, 25 human subjects spelled out phrases like ‘Carnegie Mellon’ using a BCI speller. Our findings showed that the addition of focused ultrasound neuromodulation significantly boosted the performance of EEG-based BCI. It also elevated theta neural oscillation that enhanced attention and led to enhanced BCI performance.”<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1718173646497-ezgif-2-339b99d07b.gif" style="width: 100%px;" class="fr-fic fr-dib">This video shows the BCI speller progress used by study subjects to spell “Carnegie Mellon.”For context, a BCI speller is a 6x6 visual motion aide containing the entire alpabet that is commonly used by nonspeakers to communicate. In He’s study, subjects donned an EEG cap and just by looking at the letters, were able to generate EEG signals to spell the desired words. When a focused ultrasound beam was applied externally to the V5 area (part of the visual cortex) of the brain, the performance of the noninvasive BCI greatly improved among subjects. The neuromodulation-integrated BCI actively altered the engagement of neural circuits to maximize the BCI performance, compared to previous uses, which consisted of pure processing and decoding recorded signals.“The BRAIN Initiative has supported more than 60 ultrasound projects since its inception. This unique application of noninvasive recording and modulation technologies expands the toolkit, with a potentially scalable impact on assisting people living with communication disabilities,” said Dr. Grace Hwang, program director at the Brain Research Through Advancing Innovative Neurotechnologies® initiative (The BRAIN Initiative®) at the National Institutes of Health (NIH).&nbsp;<blockquote>The BRAIN Initiative has supported more than 60 ultrasound projects since its inception. This unique application of noninvasive recording and modulation technologies expands the toolkit, with a potentially scalable impact on assisting people living with communication disabilities.<cite>Grace Hwang, program director, National Institutes of Health</cite></blockquote>Following this discovery, the He lab is further investigating the merits and applications of focused ultrasound neuromodulation to the brain, beyond the visual system, to enhance noninvasive BCIs. They also aim to develop more compact-focused ultrasound neuromodulation device for better integration with EEG-based BCIs, and to integrate AI to continue to enhance the overall system performance.“This is my lifelong interest, and I will never give up,” emphasized He. “Working to improve noninvasive technology is difficult, but I strongly believe that if we can find a way to make it work, everyone will benefit. I will keep working, and someday, noninvasive lifesaving technology will be available for every household.This work was supported by NIH’s The BRAIN Initiative and the Helping to End Addiction Long-term® Initiative (NIH HEAL Initiative®), the National Institute of Neurological Disorders and Stroke, the National Institute of Biomedical Imaging and Bioengineering, the National Center for Complementary and Integrative Health, and by the National Science Foundation.Other collaborators on the Nature Communications paper include the first author Joshua Kosnoff, BME Ph.D. student, Kai Yu, BME research scientist; and Chang Liu, former BME master’s student.

To strengthen the case for noninvasive BCIs, Carnegie Mellon researchers have demonstrated that through precision neuromodulation using focused ultrasound, the performance of a BCI could be improved for communication.

Breakthrough approach enables bidirectional BCI functionality

In this episode, we discuss how a team from Carnegie Mellon University spearheading non-invasive brain computer interface solutions has had a significant breakthrough in improving their accuracy.

Podcast: All The Brain Chip Implant Benefits & None of The Risks

This article was discussed in our <a href="https://www.wevolver.com/profile/the.next.byte" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/96f146e3-d84e-4bc9-8fa4-af61e7ce87ed?dark=false" class="fr-draggable" data-gtm-yt-inspected-9="true" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>The full article will continue below.<hr>Pursuing a viable alternative to invasive brain-computer interfaces (BCIs) has been a continued research focus of Carnegie Mellon University’s <a href="https://www.cmu.edu/bme/helab/index.html" rel="noopener" target="_blank">He Lab</a>. In 2019, the group used a noninvasive BCI to successfully demonstrate, for the first time, that a mind-controlled robotic arm had the ability to continuously track and follow a computer cursor. As technology has improved, their AI-powered deep learning approach has become more robust and effective. In new work published in PNAS Nexus, the group demonstrates that humans can control continuous tracking of a moving object all by thinking about it, with unmatched performance.Noninvasive BCIs bring a host of advantages, in contrast to their invasive counterparts (think Neuralink or Synchron). These include increased safety, cost-effectiveness, and an ability to be used by numerous patients, as well as the general population. Amid these benefits, however, noninvasive BCIs face challenges because their recordings are less accurate and difficult to interpret.<iframe width="640" height="360" src="https://www.youtube.com/embed/EoKHw6bdoZs?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true" data-gtm-yt-inspected-9="true" id="917213689" title="Bin He: Neuroengineering and Dynamic Brain Mapping">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</iframe>In He’s <a href="https://academic.oup.com/pnasnexus/article-lookup/doi/10.1093/pnasnexus/pgae145" rel="noopener" target="_blank" id="isPasted">recent study</a>, a group of 28 human participants were given a complex BCI task to track an object in a two-dimensional space all by thinking about it. During the task, an electroencephalography (EEG) method recorded their activity, from outside the brain. Using AI to train a deep neural network, the He group then directly decoded and interpreted human intentions for continuous object movement using the BCI sensor data. Overall, the work demonstrates the excellent performance of non-invasive BCI for a brain-controlled computerized device.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzE0NTE3ODg4OTAzLTA0MzAtZW0tbm9uaW52YXNpdmUtYmNpLnBuZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib">Source:&nbsp;He Lab“The innovation in AI technology has enabled us to greatly improve the performance versus conventional techniques, and shed light for wide human application in the future,” expressed <a href="https://engineering.cmu.edu/directory/bios/he-bin.html" rel="noopener" target="_blank" id="isPasted">Bin He</a>, professor of <a href="https://www.cmu.edu/bme/" rel="noopener" target="_blank">biomedical engineering</a> at Carnegie Mellon University.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1714517922793-0430-em-2-noninvasive-bci.gif" style="width: 100%px;" class="fr-fic fr-dib">This video shows the cursor and target trajectories throughout a single 60-second trial with lag adjusted. The randomly moving target is represented by a yellow circle, while the cursor that the subject controlled using the BCI system is shown as a blue circle. A human subject controlled the blue cursor using the deep learning-based BCI decoder just by thinking about it. Source:&nbsp;He LabMoreover, the capability of the group’s AI-powered BCI suggests a direct application to continuously controlling a robotic device.“We are currently testing this AI-powered noninvasive BCI technology to control sophisticated tasks of a robotic arm,” said He. “Also, we are further testing its applicability to not only able-body subjects, but also stroke patients suffering motor impairments.” In a few years, this may lead to AI-powered assistive robots becoming available to a broad range of potential users.To this end, motor-impaired patients who are suffering from spinal cord injury, stroke, or other movement impairment, but do not want to receive an implant, stand to benefit immensely from research in this vein. “We keep pushing noninvasive neuroengineering solutions that can help everybody,” added He.This work was supported in part by the National Institute of Neurological Disorders and Stroke, National Center for Complementary and Integrative Health, and National Institute of Biomedical Imaging and Bioengineering.Other collaborators on the PNAS Nexus paper include the first authors Dylan Forenzo, BME Ph.D. student, and Hao Zhu, former BME Masters student; Jenn Shanahan, former lab technician; and Jaehyun Lim, BME undergraduate student.

Achieving a noteworthy milestone to advance noninvasive brain-controlled interfaces, researchers used AI technology to improve the decoding of human intention and control a continuously moving virtual object all by thinking about it, with unmatched performance.

Refined AI approach improves noninvasive BCI performance

<h2 id="isPasted">Introduction&nbsp;</h2>The field of organoid intelligence is recognized as groundbreaking. In this field, scientists utilize human brain cells to enhance computer functionality. They cultivate tissues in laboratories that mimic real organs, particularly the brain. <a id="_anchor_2" href="#_msocom_2" language="JavaScript" name="_msoanchor_2" target="_blank"></a>These brain organoids can perform brain-like functions and are being developed by <a href="https://www.frontiersin.org/journals/science/articles/10.3389/fsci.2023.1017235/full" target="_blank">Dr. Thomas Hartung and his team at the Johns Hopkins Bloomberg School of Public Health</a>.For nearly two decades, scientists have used organoids to conduct experiments without harming humans or animals. Hartung, who has been cultivating brain organoids from human skin samples since 2012, aims to integrate these organoids into computing. This approach promises more energy-efficient computing than current supercomputers and could revolutionize drug testing, improve our understanding of the human brain, and push the boundaries of computing technology.The conducted research highlights the potential of biocomputing to surpass the limitations of traditional computing and AI. Despite AI's advancements, it still falls short of replicating the human brain's capabilities, such as energy efficiency, learning, and complex decision-making. The human brain's capacity for information storage and energy efficiency remains unparalleled by modern computers. Hartung's work with brain organoids, inspired by Nobel Prize-winning stem cell research, aims to replicate cognitive functions in the lab. This research could open new avenues for understanding the human brain by allowing ethical experimentation. The team envisions scaling up the size of brain organoids and developing communication tools for input and output, enabling more complex tasks.A significant innovation by Hartung's team is a brain-computer interface device, resembling an EEG cap for organoids, facilitating signal transmission to and from the brain organoids. This development lays the groundwork for a synergistic relationship between AI and organoid intelligence, exploring each other's capabilities and potentially leading to technological advancements previously unimagined.<h2>Understanding Organoids</h2>Brain organoids are tiny, lab-created clusters of brain cells that simulate the brain's structure and function. Starting from skin cells, scientists reprogram these cells into stem cells and eventually into brain cells, creating organoids around 500 micrometers in size with fewer than 100,000 cells. Although these organoids are much smaller than a human brain, they offer a model for studying brain functions and developing computing systems.Organoid-based bio computers have several advantages over traditional silicon computers. While computers excel at number crunching, the human brain is far superior in handling complex computations efficiently, such as learning to differentiate objects with minimal data. For example, the AI system AlphaGo required training on <a href="https://www.frontiersin.org/journals/science/article-hubs/organoid-intelligence-a-new-biocomputing-frontier/explainer" target="_blank">160,000 game scenarios to master Go</a>, something far beyond human capability in terms of time and energy consumption. Moreover, the brain's energy efficiency and data storage capabilities are unmatched by current technology, with an estimated storage capacity of 2,500 terabytes.Despite not being miniature brains, brain organoids share important functional and structural characteristics with the human brain, suggesting their potential to achieve similar computational abilities. A study showing neurons in a dish learning to play Pong illustrates this potential. However, for organoid intelligence to be fully realized, brain organoids need to be larger and more complex, with millions of cells and a diversity of cell types that are important for learning and memory.Despite not being miniature brains, brain organoids share important functional and structural characteristics with the human brain, suggesting their potential to achieve similar computational abilities. A study showing neurons in a dish learning to play Pong illustrates this potential. However, for organoid intelligence to be fully realized, brain organoids need to be larger and more complex, with millions of cells and a diversity of cell types that are important for learning and memory.Improvements in brain organoid technology are essential for advancing organoid intelligence that includes increasing the size and cell diversity of organoids and improving their ability to express genes that improve learning and memory. Additionally, developing a way to nourish larger organoids, potentially through artificial blood vessels created by microfluidic technology, is considered to be challenging task. This technology would not only support the organoids' health but also enable communication through chemical signals, enabling more sophisticated biological computing systems.<h2>Mechanisms of Organoid Intelligence</h2>The integration of stem cell biology, bioengineering, and AI is forming novel approaches in the field of organoid research. This synergy is maximizing development of the stem cell, precision engineering techniques, and the analytical advantage of AI to transform the comprehension of how organisms develop, how diseases arise, and how we can devise new treatment methods. Machine learning algorithms have confirmed how closely organoids resemble actual human organs by analysing complex data sets, such as <a href="https://www.sciencedirect.com/science/article/pii/S2590093523000711" target="_blank">comparing organoid RNA sequencing data to that of real organs</a>, which helps in accurately identifying cell types and understanding gene regulatory networks within organoids.One study on brain organoids utilized <a href="https://www.sciencedirect.com/science/article/pii/S1934590919303376" target="_blank">UMAP (Uniform manifold approximation and projection) to analyse RNA sequencing data</a> at different developmental stages, uncovering the composition of cell subtypes over time. This analysis, combined with data from neonatal electroencephalogram (EEGs) of preterm infants, highlighted the developmental parallels between cortical organoids and fetal human brains, especially in terms of electrophysiological network changes. Furthermore, the creation of the brain and organoid manifold alignment (BOMA) algorithm has facilitated the comparison of <a href="https://www.sciencedirect.com/science/article/pii/S2667237523000206?via%3Dihub" target="_blank">gene expression between real brains and organoids</a>, uncovering patterns of gene expression over time and across species, thereby validating the use of brain organoids for neural system research.Moreover, machine learning extends beyond analysing genetic data. For example, <a href="https://cmbl.biomedcentral.com/articles/10.1186/s11658-022-00347-3" target="_blank">robust support-vector machines (RSVM) algorithms</a> have been used to categorize salivary gland organoids based on changes detected in Raman spectral data during organoid formation. Similarly, a study on cardiac organoids demonstrated that a random forest approach could effectively sort heart cells from non-heart cells in single-cell RNA sequencing data from heart organoids derived from <a href="https://www.nature.com/articles/s42003-022-03346-4" target="_blank">human induced pluripotent stem cells (hiPSCs)</a>. This model could also distinguish between heart tissues created from 3D organoids, traditional 2D cultures, and organoids from genetically mutated hiPSC lines, showcasing the nuanced differences in cell types between ventricular and atrial cardiomyocytes.It should be emphasized that deep learning has significantly contributed development in the field of organoid research by improving the analysis of images and videos captured through <a href="https://www.sciencedirect.com/science/article/pii/S2590093523000711" target="_blank">microscopy</a>. These visual data sources contain information about organoid development, including changes in morphology, size, and function, to understand their response to environmental changes. The amount of data generated, however, has created a bottleneck in interpreting these experimental results, necessitating the adoption of deep learning techniques for <a href="https://www.sciencedirect.com/science/article/abs/pii/S0010482521002845?via%3Dihub" target="_blank">efficient organoid image analysis and tracking</a>. Significant efforts have been made to develop tools that are using deep learning to automate the quantification of changes in organoids, addressing the challenges of manual screening which is both time-consuming and error-prone.A specific dataset designed for high-throughput organoid image analysis employs Convolutional Neural Networks (CNN) architecture, Residual Network (ResNet), a type of CNN architecture designed to handle very deep networks, both enabling the tracking of organoid growth and morphological changes with high speed and accuracy. <a href="https://ieeexplore.ieee.org/document/7780459" target="_blank">This breakthrough</a> demonstrates deep learning's potential to improve organoid research. Open-source tools like <a href="https://journals.biologists.com/dev/article/148/18/dev199611/272032/MOrgAna-accessible-quantitative-analysis-of" target="_blank">MOrgAna</a> combine deep learning with traditional machine learning to analyse brain organoid images, using deep architectures to differentiate organoid pixels from noise, thereby improving upon existing quantification methods.<h2>Applications of Organoid Intelligence</h2>Integrating organoids with AI aims to refine models of human organ functionality and disease, offering a new approach for developing and advancing the drug discovery, diagnostics, and therapeutic innovations. The role of AI is in improving organoid technology across <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511344/" target="_blank">five aspects</a>: swift identification of construction methods, cost-effective analysis of complex image data, efficient multi-omics data interpretation, accurate preclinical assessments, and practical applications. These areas provide a comprehensive view of AI's potential to accelerate and refine the development of AI-improved and supported organoids.Rapid identification of construction methods is important for selecting the best procedures for organoid creation, while cost-effective analysis of complex image data allows for deeper insights into organoid structure and function from various perspectives. Streamlined interpretation of multi-omics data enables a thorough understanding of organoid biology, encompassing gene expression, proteomics, and metabolomics. Accurate preclinical assessments and applications are essential for predicting AI's real-world clinical utility.Practical strategies for applying these concepts will maximize AI's impact on developing organoid technology. More specifically, a machine learning framework has been developed to forecast the efficacy of <a href="https://www.nature.com/articles/s41467-020-19313-8" target="_blank">anti-cancer drugs using organoid models</a>, highlighting the predictive biomarkers in cancer treatment optimization. However, current machine learning techniques often face challenges in extracting reliable biomarkers from preclinical models. To address this, a novel network-based strategy utilizing <a href="https://www.nature.com/articles/s41565-023-01323-4" target="_blank">pharmacogenomic data from 3D organoid cultures</a> has been introduced, demonstrating precise drug response predictions in colorectal and bladder cancers. This approach underscores the potential of AI in refining cancer therapies through better biomarker identification.A notable <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10511344/#bib176" target="_blank">study introduces IRENE</a>, a transformer-based model that improves multimodal inputs for clinical diagnostics, showing superior performance in diagnosing and predicting clinical outcomes compared to traditional models. This study indicates the growing importance of machine learning in disease modelling and treatment development, poised to significantly impact clinical diagnostics and personalized medicine as our technological capabilities and understanding of organoids and disease processes continue to evolve.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1710925021949-organs.jpg" style="width: 100%px;" class="fr-fic fr-dib">AI interfacing enables efficient organoid construction, multiscale image analysis, and precise preclinical evaluation. IMage credit: Long Bai et al.<h2>Challenges and Future Directions</h2>The integration of AI with organoid technology presents several ethical considerations and societal implications. One of the primary ethical concerns revolves around the use of human stem cells for creating organoids. This includes questions about consent, especially when cells are derived from embryos or patients who may not be fully aware of the potential future uses of their cells. Societal implications include issues of accessibility and equity. As organoid technologies advance, there's a risk that treatments developed using these cutting-edge methods could be expensive and only available to a privileged few, exacerbating existing healthcare disparities. Moreover, the potential for creating patient-specific organoids for precision medicine raises privacy concerns regarding genetic data and the need for robust data protection measures.From a technical perspective, several challenges and limitations need to be addressed. One of the main challenges is the complexity of human organs, which are difficult to replicate in full of current organoid technology. While organoids can mimic certain aspects of organ structure and function, they often lack the complete cellular diversity and organ-level functions, such as blood supply and immune system interactions.Addressing these challenges requires a multidisciplinary approach that combines recent findings in stem cell biology, tissue engineering, computational sciences, and ethics. Future research should focus on improving the fidelity and complexity of organoids to more closely mimic human organs, including the development of vascularized and innervated organoids that can better replicate organ functions.<h2>Conclusion&nbsp;</h2>AI-enabled organoids have the potential to unlock deeper understandings of the biological systems represented by various organoid models. An important part of this advancement is the effective creation and maintenance of organoids, areas where AI, especially through machine learning algorithms, shows considerable promise. AI's ability to process and analyse amounts of omics data provides valuable feedback on organoid functionality and construction techniques. This leads to more effective and higher-quality organoid production, facilitating a smoother progression from experimental research to practical clinical use.

Engineers at UC San Diego demonstrated for the first time that human brain organoids implanted in mice have established functional connectivity to the animals’ cortex.  Picture by: David Baillot | UC San Diego Jacobs School of Engineering | Flickr

Organoid intelligence is a pioneering field where scientists use human brain cells to improve computer functions.

Diving into Organoid Intelligence

This article originally appeared in <a href="https://engineering.wustl.edu/news/2023/Brain-machine-interfaces-for-insects-to-study-principles-of-odor-guided-navigation.html" target="_blank" rel="noopener noreferrer">McKelvey School of Engineering</a> and has been republished with permission.<hr><iframe width="640" height="360" src="https://www.youtube.com/embed/dBNOGbF-e7E?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe>The locust follows a pleasant scent on a treadmill. (Credit: Raman lab)The inviting smell of a freshly baked cookie immediately triggers a motor response to search for the source of that smell. Often the cookie can be easily found.This everyday event that we perform without a thought is an amazing feat that combines our superior ability to smell the cookie and computational prowess to determine the directions to move toward the cookie. Robots that possess similar capabilities are yet to be developed as the basic biological principles that are needed to perform this task are yet to be fully understood. &nbsp;The ability to study neural responses in behaving insects is essential to understanding the robust solutions biological systems have developed for several engineering problems. For example, locusts have survived for at least 8 million years — longer than humans have been on this planet — in nearly all parts of the earth. Their many remarkable capabilities include seeing, hearing and smelling things that humans cannot sense, jumping multiple times their body length, the uncanny ability to fly up to 100 kilometers a day, and switching between solitary and menacing swarms phases as needed for survival. They use these capabilities to find food, avoid predators, propagate their species, and do all things necessary to keep them alive. Researchers in the McKelvey School of Engineering at Washington University in St. Louis have long sought to understand the power of the locusts’ sensing, computing and locomotory capabilities.Barani Raman, professor of biomedical engineering in the McKelvey School of Engineering, is leading a multidisciplinary team to study how the locust brain transforms sensory input into behavior with a four-year, $4.3 million grant from the National Science Foundation’s Integrative Strategies for Understanding Neural and Cognitive Systems (NCS) program. The grant converges years of research in Raman’s lab with that of his longtime WashU collaborators, including Shantanu Chakrabartty, professor of electrical &amp; systems engineering, and Srikanth Singamaneni, professor of mechanical engineering &amp; materials science, as well as Alexandra Rutz, assistant professor of biomedical engineering, and Yehuda Ben-Shahar, professor of biology, also at WashU.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1697685410077-ezgif-5-9129b96817.jpg" style="width: 766px;" class="fr-fic fr-dib">Researchers in the McKelvey School of Engineering have long sought to understand the power of the locusts’ sensing, computing and locomotory capabilities. With a $4.3 million grant from the National Science Foundation, they will converge that research to develop a “cyborg,” or mobile robot or drone, that can mimic the locusts’ behaviors. (Credit: Raman lab)“Insects are an engineering marvel,” Raman said. “They possess diverse sensing modalities and locomotory responses yet contained in such a small package. We want to engineer tools to study the amazing capabilities of these relatively simpler organisms.”The research brings together Washington University’s strengths in neural engineering, integrated circuits, biomaterials, synthetic biology and genetic engineering to understand how the insects use olfactory cues to navigate toward an odor source, which could potentially be used for various applications, such as detecting gas left on in the kitchen or as the proverbial canary in the coal mine to test spaces for hazardous chemicals. In 2024, the team plans to launch the first-of-its-kind Center for Cyborg and Biorobotics Research (CyBoR) to formally conduct the research.&nbsp;The team plans to study the neural response in the locusts’ brains by having the locusts follow a specific odor on a specially built treadmill while walking on a foam ball and while flying in a wind tunnel. By studying their movements and neural activity in the brain in response to these odors, the team can process the information using a custom microchip to develop a “cyborg,” or mobile robot or drone, that can mimic the locusts’ behaviors. They also aim to augment the locusts’ ability to detect certain odors over others.“Nature already has endowed this organism with various capabilities, so why not understand and augment those capabilities using synthetic mechanisms?” asked Chakrabartty, whose expertise is in sensors and integrated circuits. “Ultimately, our goal is to design a completely synthetic system that has similar remarkable capabilities.”While the team has already joined forces to create recording instruments and nanomaterials to manipulate neural and behavioral responses, they will continue to improve on those as they develop the bio-hybrid and mobile robotic systems.&nbsp;“One of the important challenges in these studies is the limited stability of the neural recording and stimulation electrodes and interfaces over a long period,” said Singamaneni, whose expertise is in novel bio- and nanomaterials. “We aim to design and realize novel anti-inflammatory electrodes and biointerfaces that will enable stable long-term neural recording and stimulation.”Previously, <a href="https://engineering.wustl.edu/news/2016/Engineers-to-use-cyborg-insects-as-biorobotic-sensing-machines.html" target="_blank">the research team developed a miniature “backpack”</a> containing sensors that recorded the locusts’ brain activity when exposed to the odors. However, the existing backpack is too heavy for the locusts to wear while flying, so the team will work to reduce its weight.At Washington University, the research is underway in a state-of-the-art CyBoR facility that includes the habitat for the locusts, the treadmill, the wind tunnel, specialized microscopes, and space for visiting researchers as well as for visitors interested in learning about the insects. WashU students also will be included in the research as well as students from the community through various outreach programs.&nbsp;Other co-investigators include Alper Bozkurt, distinguished professor of electrical &amp; computer engineering at North Carolina State University, who will lead efforts on instrumentation for navigation control; Sawyer Fuller, assistant professor of mechanical engineering at the University of Washington, who will lead efforts to build miniaturized robots that incorporate both biological and engineering elements; and Nabil Imam, assistant professor of computational science and engineering at Georgia Tech, who will lead the development of neuromorphic algorithms and hardware in the bio-hybrid and robotic systems.

Barani Raman, professor of biomedical engineering in the McKelvey School of Engineering, is leading a multidisciplinary team to study how the locust brain transforms sensory input into behavior with a four-year, $4.3 million grant from the National Science Foundation’s Integrative Strategies for Understanding Neural and Cognitive Systems (NCS) program. 

Brain-machine interfaces for insects to study principles of odor-guided navigation

As a result of the development of various machines and robots, we have become able to effortlessly handle jobs that humans could not perform with muscle strength and motor skills. Moreover, owing to advances in sensors, AI, and other information processing technologies, we have also become able to use perceptual abilities that exceed the five human senses. In order for humans to freely use the extended abilities realized through such machinery, other technologies that create a closer relationship between machinery and humans are required.&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5Mjc1MDU1MDkxLW11cmF0YV9ibWk3LTAxLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 766px;" class="fr-fic fr-dib">Figure 1: Detecting brain activity to freely manipulate a machine&nbsp;<h2 id="isPasted">Scanning brain activity to create the ultimate HMI</h2>When a human is operating a machine, the Human&ndash;Machine Interface (HMI) becomes the point of contact between both sides. Devices such as the steering wheel and pedals of an automobile, the keyboard and mouse of a PC, and the touch panel of a smartphone are able to operate a machine as an HMI. Recently, voice recognition technologies have progressed to the point where it has become possible to operate a machine such as an AI speaker using natural language as an HMI. Moreover, if you utilize the Virtual Reality (VR) technologies applied in games, etc., you can experience events within the virtual world created by the computer with a sense of immersion and feeling that you are actually there. The relationship between human and machine is steadily becoming closer due to the progress of technology.However, with existing HMIs, a considerable degree of skill is required to communicate motions and operations you are thinking about to the machine. Freely operating a car as if it were part of your body like a racer or bus driver requires training that takes a lot of time. Moreover, when operating a machine through a tool such as a steering wheel, a slight delay inevitably occurs in the operation. To realize the kind of operation that feels as if human and machine have merged, technologies that further reduce the distance between both are needed.So how much will the distance between human and machine be reduced in the future? The development of a technology to unconsciously move machinery like the limbs of your own body, which can be described as the ultimate form of HMI, is being advanced. That technology (Figure 1) is called &quot;BMI&quot; (Brain&ndash;Machine Interface). BMI has already reached the stage of practical implementation in some applications. This article introduces the trends in BMI evolution and the impact of this technology.<h2>Visualizing what someone is thinking through advances in sensing and information processing</h2>The development of sensing technologies to detect brain activity and information processing technologies to interpret (decode) the status of brain activity is becoming increasingly active. Thus, we are becoming able to realize an advanced BMI.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5Mjc1MDg0Mjc4LW11cmF0YV9ibWk3LTAyZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib">Figure 2: Realization of BMI through the progress of sensing and information processing technologies&nbsp;Until now, investigating brain activity required the use of large-scale medical equipment to measure brainwave changes and distribution within the head. Brainwaves are minute electrical signals that record from the top of the head, etc. the electrical potential occurring in the neuronal projections that make up the brain. To accurately measure brainwaves, you need to use 12 or more EEG recording devices to precisely measure electrical potential fluctuations of several microvolts.Sensing technologies that accurately read brainwaves in a non-invasive manner without implementing any surgical interventions such as embedding electrodes in the head have advanced rapidly in recent years. Because brainwaves are observed as small voltage fluctuations called &quot;microvolts,&quot; it was difficult to accurately measure them due to the impact of electrical noise generated by the muscles and electromagnetic waves in the surrounding environment. Owing to the development of technologies that remove noise, it has become possible to take highly precise brainwave measurements with miniature sensors.In addition to the method of reading minute electric potential fluctuations, sensor miniaturization and increasing precision is advancing in methods that measure the magnetic fields generated due to brain activity and technologies that shine near-infrared light on the head to detect the states of light scattering and reflection to estimate changes in the volume of blood flow within the brain. Moreover, the use of a technology called fMRI (functional magnetic resonance imaging) equipment*1 to identify active areas of the brain with pinpoint high precision and measure the state of brain activity with greater detail is also advancing. By making effective use of these technologies according to different purposes, we have become able to know the state of brain activity from many angles.*1 fMRI (functional magnetic resonance imaging) is a technology that measures the electromagnetic waves generated by the deoxygenated hemoglobin within the blood flowing in the brain to detect the activity of any location in the brain.Moreover, technologies that interpret (decode) the biometric information that reflects brainwaves and other brain activity have also advanced, and we have become able to visualize cognitive information such as what a person is looking at or listening to. The technologies have developed to the point where today if you collect accurate brain activity information and effectively analyze it, you can even reproduce a fuzzy image on a monitor of the content of a dream that someone is having during sleep or images that a person is seeing with their eyes from brain activity information. Moreover, it is also possible to determine emotions and other mental states or the comprehension of studied material.In recent years, it has become clear how the activity of brain cells and the various parts of the human brain relate to each other while operating by accurately modeling brain function based on deep-learning theory, which has become utilized in various fields as a core technology of artificial intelligence (AI), and the analysis precision has rapidly increased.<h2>Wide range of BMI application fields, from entertainment to welfare and education</h2>Because it has become comparatively easy to measure brain activity, attempts are being made to apply the information obtained through measurement to various applications.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjc5Mjc1MTEyNzI5LW11cmF0YV9ibWk3LTAzLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 766px;" class="fr-fic fr-dib">Applying BMI to extend human capabilities&nbsp;For example, headphones that utilize brainwave sensors and are equipped with functions that can determine the degree to which a person is concentrating and in a relaxed state have been practically implemented. Games and other applications in which the character movement and story development change according to a person&#39;s mental state can be realized. When it comes to entertainment content, it is meaningless if the viewer or player becomes bored. However, no matter how interesting the content is, some people may find it boring. If you can provide content that triggers emotions by various means according to the mental state of each individual, then it will become something that will satisfy everyone.In addition, there are also initiatives to utilize BMIs to support the lifestyles of those with disabilities through machines. Equipment that can distinguish between several hundred different types of messages from brainwaves have already been practically implemented, and it is hoped that it can be utilized as a means of communication for those with severe motor impairments that make speech and writing difficult. The arrival of an era in which prosthetic hands and legs can be manipulated like real limbs through the utilization of BMIs may not be far off.Moreover, there are also initiatives underway to detect a person&#39;s brain response to received stimulation and apply that to impactful education and training to increase learning effectiveness. Systems that improve English listening capabilities in a short period of time by analyzing how English conversation audio is being heard and understood within the student&#39;s head and providing feedback are being developed.BMI is an essential technology for minimizing the distance between human and machine as much as possible to merge both together. As the technology develops, it will become possible to freely manipulate massive and micro machines, machines placed in remote or dangerous locations, as well as computers with advanced computing power as if they were your own limbs. BMI will likely become a technology that extends human potential and supports the future.<hr id="isPasted"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjg1MDg2NjcyNTM4LTE2ODUwODY2NzI1MzgucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" class="fr-fic fr-dii">If you have any questions based on this article that you would like to discuss with an expert at Murata, don&#39;t hesitate to reach out. There&#39;s an entire team ready to support you with your question.<a class="button-main-action" href="https://www.murata.com/en-eu/contactform?&Detail=Wevolver%20request" rel="noopener noreferrer" target="_blank">GET IN TOUCH</a><hr>

As a result of the development of various machines and robots, we have become able to effortlessly handle jobs that humans could not perform with muscle strength and motor skills. Moreover, owing to advances in sensors, AI, and other information processing technologies, we have also become able to use perceptual abilities that exceed the five human senses.

Using BMI to operate machinery like your own limbs without any manipulation

In this episode, we talk about NeuralTree: a neural interface capable of detecting neural impulses associated with brain disorders and countering them.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/af523f9f-6e33-407d-8aec-98ea14b8fd45?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe><hr><h3 id="isPasted">EPISODE NOTES</h3>(0:46) - <a href="https://www.wevolver.com/article/a-neuro-chip-to-manage-brain-disorders" target="_blank">A Neuro-chip To Manage Brain Disorders</a><hr>As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.To learn more about this show, please visit our <a href="https://www.wevolver.com/profile/the.next.byte" target="_blank">shows page</a>. By following the page, you will get automatic updates by email when a new show is published. Be sure to give us a follow and review on <a href="https://podcasts.apple.com/nl/podcast/the-next-byte/id1548255861?l=en" rel="nofollow" target="_blank">Apple</a>, <a href="https://open.spotify.com/show/6lPPsRtegnivsRUEsQ09R7?si=IPMiah7xT_apJtPjqvXMlA" rel="nofollow" target="_blank">Spotify</a>, and most of your favorite podcast platforms!

In this episode, we talk about NeuralTree: a neural interface capable of detecting neural impulses associated with brain disorders and countering them.

Podcast: The Future of Neurology: Neural Interfaces Countering Neural Diseases

This article was discussed in our <a data-saferedirecturl="https://www.google.com/url?q=https://www.wevolver.com/profile/the.next.byte&source=gmail&ust=1672821999265000&usg=AOvVaw3lLKdHdEN0YvCEsaHI7hx9" href="https://www.wevolver.com/profile/the.next.byte" rel="noopener noreferrer" target="_blank">Next Byte podcast</a>.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/af523f9f-6e33-407d-8aec-98ea14b8fd45?dark=false" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> The full article will continue below.<hr>Mahsa Shoaran of the <a href="https://www.epfl.ch/labs/inl/" target="_blank">Integrated Neurotechnologies Laboratory</a> in the School of Engineering collaborated with St&eacute;phanie Lacour in the <a href="https://www.epfl.ch/labs/lsbi/" target="_blank">Laboratory for Soft Bioelectronic Interfaces</a> to develop NeuralTree: a closed-loop neuromodulation system-on-chip that can detect and alleviate disease symptoms. Thanks to a 256-channel high-resolution sensing array and an energy-efficient machine learning processor, the system can extract and classify a broad set of biomarkers from real patient data and animal models of disease in-vivo, leading to a high degree of accuracy in symptom prediction.&ldquo;NeuralTree benefits from the accuracy of a neural network and the hardware efficiency of a decision tree algorithm,&rdquo; Shoaran says. &ldquo;It&rsquo;s the first time we&rsquo;ve been able to integrate such a complex, yet energy-efficient neural interface for binary classification tasks, such as seizure or tremor detection, as well as multi-class tasks such as finger movement classification for neuroprosthetic applications.&rdquo;Their results were presented at the 2022 IEEE International Solid-State Circuits Conference and&nbsp;<a href="https://ieeexplore.ieee.org/document/9905664" rel="noopener" target="_blank">published</a> in the IEEE Journal of Solid-State Circuits, the flagship journal of the integrated circuits community.<h2>Efficiency, scalability, and versatility</h2>NeuralTree functions by extracting neural biomarkers &ndash; patterns of electrical signals known to be associated with certain neurological disorders &ndash; from brain waves. It then classifies the signals and indicates whether they herald an impending epileptic seizure or Parkinsonian tremor, for example. If a symptom is detected, a neurostimulator &ndash; also located on the chip &ndash; is activated, sending an electrical pulse to block it.Shoaran explains that NeuralTree&rsquo;s unique design gives the system an unprecedented degree of efficiency and versatility compared to the state-of-the-art. The chip boasts 256 input channels, compared to 32 for previous machine-learning-embedded devices, allowing more high-resolution data to be processed on the implant. The chip&rsquo;s area-efficient design means that it is also extremely small (3.48mm2), giving it great potential for scalability to more channels. The integration of an &lsquo;energy-aware&rsquo; learning algorithm &ndash; which penalizes features that consume a lot of power &ndash; also makes NeuralTree highly energy efficient.In addition to these advantages, the system can detect a broader range of symptoms than other devices, which until now have focused primarily on epileptic seizure detection. The chip&rsquo;s machine learning algorithm was trained on datasets from both epilepsy and Parkinson&rsquo;s disease patients, and accurately classified pre-recorded neural signals from both categories.&ldquo;To the best of our knowledge, this is the first demonstration of Parkinsonian tremor detection with an on-chip classifier,&rdquo; Shoaran says.<h2>Self-updating algorithms</h2>Shoaran is passionate about making neural interfaces more intelligent to enable more effective disease control, and she is already looking ahead to further innovations.&ldquo;Eventually, we can use neural interfaces for many different disorders, and we need algorithmic ideas and advances in chip design to make this happen. This work is very interdisciplinary, and so it also requires collaborating with labs like the Laboratory for Soft Bioelectronic Interfaces, which can develop state-of-the-art neural electrodes, or labs with access to high-quality patient data.&rdquo;As a next step, she is interested in enabling on-chip algorithmic updates to keep up with the evolution of neural signals.&ldquo;Neural signals change, and so over time the performance of a neural interface will decline. We are always trying to make algorithms more accurate and reliable, and one way to do that would be to enable on-chip updates, or algorithms that can update themselves.&rdquo;<hr>ReferencesU. Shin, C. Ding, B. Zhu, Y. Vyza, A. Trouillet, E. C. M. Revol, S. P. Lacour, M. Shoaran, &quot;NeuralTree: A 256-Channel 0.227-&mu;J/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC,&quot; in IEEE Journal of Solid-State Circuits (JSSC), vol. 57, no. 11, pp. 3243-3257, Nov. 2022, doi:&nbsp;<a href="https://ieeexplore.ieee.org/document/9905664" rel="noopener" target="_blank">10.1109/JSSC.2022.3204508</a>.U. Shin, L. Somappa, C. Ding, B. Zhu, Y. Vyza, A. Trouillet, S. P. Lacour, M. Shoaran, &ldquo;A 256- Channel 0.227&mu;J/class Versatile Brain Activity Classification and Closed-Loop Neuromodulation SoC with 0.004mm2-1.51&mu;W/channel Fast-Settling Highly Multiplexed Mixed-Signal Front-End&quot; in IEEE International Solid-State Circuits Conference (ISSCC), 2022, doi:&nbsp;<a href="http://ieeexplore.ieee.org/document/9731776" rel="noopener" target="_blank">10.1109/ISSCC42614.2022.9731776</a>.

EPFL researchers have combined low-power chip design, machine learning algorithms, and soft implantable electrodes to produce a neural interface that can identify and suppress symptoms of various neurological disorders.

A neuro-chip to manage brain disorders

Image credit: Wyss Institute at Harvard University

Article #8 of Improving Lives with Digital Healthcare Series: Advancements in electronics have transformed the healthcare sector. The focus is on improving the experiences of users by utilizing cutting-edge technologies like advanced drug delivery systems, wearables, surgical robots, and more.

The Next-Generation of Medical Technologies: Digitized, Miniaturized, and Connected

This is the fourth article in an&nbsp;<a href="https://www.wevolver.com/article/design-challenges-in-consumer-and-medical-wearables" rel="noopener noreferrer" target="_blank">8-part series</a>&nbsp;featuring articles on Improving Lives with Digital Healthcare. The series focuses on electronic systems that enable innovation in the healthcare industry. This series is sponsored by Mouser Electronics. Through the sponsorship, Mouser Electronics shares its passion for technologies that enable smarter and connected applications.Brain-Computer Interface (BCI) is a means to establish direct communication between a human brain and a computer to allow the machine to sense and be controlled by thoughts converted to electrical signals. The technology has its potential applications in education, gaming, entertainment, and above all, in medical science. BCI devices can help people with paralysis or severe motor disabilities control prosthetic devices.Artificial Intelligence (AI) and Machine Learning (ML) combined with Digital Signal Processing (DSP) enable greater accuracy and reliability in evaluating and developing BCI applications. In this article, we examine this field, and some of the critical electronic components within the signal chain required to measure brain waves, bodily functions, and how everything is put together.<h2 dir="ltr">Brain-Computer Interface and Electroencephalograms</h2>The human brain produces oscillating electronic voltages. The typical value of these voltages is minimal, measured in units on the order of millionths of volt (microVolts). The most common way to collect and analyze these brain-wave voltages is through an electroencephalogram (EEG). An EEG is an electrophysiological monitoring method to record electrical activity on the scalp. It captures signals directly related to the brain waves happening directly under the skull.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjU4NDAxMzUyODUwLTE2NTg0MDEzNTI4NTAucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="720" height="405" class="fr-fic fr-dii">Fig. 1: By understanding the brain&#39;s activity in different situations, it is possible to make robotic equipment that can be controlled by thoughts. Source: National Science FoundationBCI via an EEG can be unidirectional (one-way) or bidirectional (two-way). Bidirectional BCI allows information to flow both ways, thus opening the brain to feedback and future adjustments. EEGs can be invasive, semi-invasive, or non-invasive. Invasive EEGs involve directly locating and connecting devices to the human brain. Semi-invasive EEGs can be placed between the brain and the skull. Non-invasive BCI, on the other hand, is generally done by a cap placed on the skull with various electrodes.&nbsp;EEGs provide temporal resolution of 0.05 seconds and spatial resolutions on the order of 10 mm. Other techniques, besides the electrical technique employed by EEGs, can be used to gather data. These techniques can employ magnetic, metabolic, or other forms.Brain waves are classified into one of five general categories based on their frequency as shown in Table 1. Medical researchers break these frequencies into bands because each band represents a distinct way a brain operates in performing its functions. For example, critical activities such as memory and recollection happen especially, but not exclusively, in the theta band. Researchers use these bands to analyze what might be going on if appearing signals are too little, too great, or in an optimal amount.<div align="left" dir="ltr"><table><tbody><tr><td>Band</td><td>Frequency (Hz)</td></tr><tr><td>Delta</td><td>0.5-3</td></tr><tr><td>Theta</td><td>3-8</td></tr><tr><td>Alpha</td><td>8-12</td></tr><tr><td>Beta</td><td>12-38</td></tr><tr><td>Gamma</td><td>38-42</td></tr></tbody></table></div>Table 1: The five general categories of brain wave bands. Each frequency range is a nominal value and not necessarily absolute.An EEG acquires the brainwave signals and digitizes them. They are then signal-processed, where features can be extracted and translation algorithms perform classifications. They also can be printed out or recorded for future analysis.&nbsp;Signal outputs can be utilized to form device commands to provide instructions related to motor control, locomotion/movement, and environmental conditions or stimuli. BCI helps allow disabled people to have more control of their external environments.<h2 dir="ltr">Brain-Computer Interface and the Human Condition</h2>Because of human biology, our senses and our intelligence have fundamental limitations. It is conceivable that BCI and implants could enhance or provide new sensory information and augment biological capabilities.&nbsp;A primary consideration in merging man and machine is understanding how the machine and the human interact and exchange information between themselves on a real-time basis since they operate in two different domains. More work remains to be done to understand how the mutual coordination of individually-addressable neurons directing the brain can function in its environment while maintaining coordination with the digitally-addressable domain of the interface.&nbsp;<h2 dir="ltr">Brain-Computer Interface Research</h2>BCI is also being investigated to look at areas to help humanity, including restoring or enhancing human vision, motor recovery for disabled limbs, and brain mapping to assist in restoring and correcting various neurological damage and disorders. Brain mapping can lead to a greater understanding of seeing how human thought is realized to human action. It can lead to augmented human learning, new or enhanced human sensing, and new embedded autonomous neural systems. Let&rsquo;s look at two examples of where BCI can continue to evolve and lead.<h3 dir="ltr">Superhuman Cognition</h3>Billionaire Elon Musk is actively investigating BCI issues. He is one of the founders of Neuralink Corp., a neurotechnology company developing implantable brain-machine interfaces. Neuralink is working on building better tools for communicating with the brain. These founders believe that, with the right team, the applications for this technology are limitless. Neuralink is exploring the possibility of embedding into the brain ultrafine electronic threads that permit neural activity. One of Musk&rsquo;s stated goals is to achieve &ldquo;superhuman cognition.&rdquo;&nbsp;Inventor and futurist Ray Kurzweil believes that human intelligence is characterized by its remarkable ability to recognize patterns. He views human intelligence as an evolutionary, self-organizing hierarchical system operating in the context of a biological pattern-recognition machine. Musk is motivated to achieve superhuman cognition based upon his desire for humanity to negotiate more proficiently in understanding the emergence and spread of more powerful AI, which is becoming ever more adept in its excellence at pattern recognition.&nbsp;<h3 dir="ltr">New Senses</h3>When looking at how BCI can enhance human senses, one can consider the case of Neil Harbisson, cofounder of the Cyber Foundation. He has been recognized as the world&rsquo;s first cyborg. Born color-blind, Harbisson has permanently installed an antenna into his skull so that he can now literally hear color, substituting his auditory sense in place of his visual limitation. Harbisson is actively advocating a future in which humans incorporate technology into their bodies.<h3 dir="ltr">Sensor Hubs</h3>Supporting these efforts, sensor hubs are being employed in various ways to collect additional biometric information beyond BCI. Sensor hubs use multiple sensors and a microcontroller to collect and analyze numerous human parameters not directly accessed by brain waves. These can include collecting information about the human pulse heart rate, pulse blood oxygen saturation (SpO2), and estimated blood pressure.<h2 dir="ltr">Ensuring High-Quality Data</h2>Because brain signals are so small, the entire electronic signal chain is prioritized in its design to minimize noisy, spurious, or artifact signals. The patient might induce these sources through their physical movements, sweating, eye movements, heart rhythms, and the like. Electrical errors can result from 50Hz/60Hz noise, electrode-skin contact issues, and cable movements.Successful electronic component selection will focus on utilizing high-precision, low-noise, high-resolution signal chain products. Low-noise amplifiers (LNA), unity-gain buffers, and precision Analog-to-Digital Converters (ADCs) are chosen to prevent the introduction of unwanted signals into the chain while providing the ability to resolve data accurately and reliably. Differential amplifiers and bandpass filters are also employed to ensure only high-quality data is transmitted. Appropriate connectors and jumpers are needed to get accurate signals into and out of the brain.&nbsp;<h2 dir="ltr">Artificial Intelligence with Brain-Computer Interface</h2>AI and its subsets, including ML and deep learning (DL), are enabling approaches in EEG-based BCIs. DL can offer automatic classifications of EEG signals. This effort allows for the data to be utilized in various applications and other Convolutional Neural Networks (CNN) training. At present, human expertise supports AI approaches. The desire is to eliminate artifacts, increase the data quality, and continue to realize gains in AI technology so that measured brain wave signals can continue to exhibit exponential increases in their ability to be classified by AI through DL techniques.<h2 dir="ltr">Electronic Components by Molex Enabling Brain-Computer Interface Applications</h2><a href="https://www.mouser.com/new/molex/molex-025-premo-flex-jumpers/" target="_blank">Molex 0.25mm-Pitch Premo-Flex Jumpers</a> offer reliable connectivity in a compact design for tight packaging applications. The jumpers deliver durable, ultra-flexible, cost-effective solutions for printed circuit board (PCB) connections in virtually any industry. These FFC/FPC jumpers are available in standard lengths, pitches, and circuit sizes to accommodate a wide range of flexible interconnect requirements between two PCBs. Molex 0.25mm-Pitch Premo-Flex Jumpers are ideal for medical applications, including patient monitoring for signals, including the body&rsquo;s condition, surgical equipment, and telehealth. <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjU4NDAxMzUxNDY5LTE2NTg0MDEzNTE0NjkucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" class="fr-fic fr-dii">Fig. 2:&nbsp;<a href="https://www.mouser.com/new/molex/molex-025-premo-flex-jumpers/" rel="noopener noreferrer" target="_blank">Molex 0.25mm-Pitch Premo-Flex Jumpers</a><a href="https://www.mouser.com/new/molex/molexeasyon/" target="_blank">Molex 0.25mm Pitch Easy-On FPC Connectors</a> offer a 50 V voltage rating, 50 M&Omega; insulation resistance, and 150 V AC dielectric withstanding voltage. These connectors provide designers with a range of sizes and styles to accommodate other PCB components. Molex 0.25 mm Pitch Easy-On FPC Connectors are ideal for next-generation medical devices. <img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjU4NDAxMzUxMzcwLTE2NTg0MDEzNTEzNzAucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" width="720" height="144" class="fr-fic fr-dii">Fig. 3:&nbsp;<a href="https://www.mouser.com/new/molex/molexeasyon/" rel="noopener noreferrer" target="_blank">Molex 0.25mm Pitch Easy-On FPC Connectors</a><h2 dir="ltr">Conclusion</h2>EEG-based BCI relies on high-performance electronic signal chains. Careful consideration of all critical electronic components, including reliable connectivity in compact designs, are needed to accurately measure EEG and other bodily functions. AI and DL techniques enable dynamic EEG data to receive better interpretations and allow humanity to realize more benefits from BCI.This article was initially published by Mouser and Molex in an <a href="https://www.mouser.com/news/molex-improving-lives-with-digital-healthcare/molex-improving-lives-with-digital-healthcare.html" target="_blank">e-magazine</a>. It has been substantially edited by the Wevolver team and Electrical Engineer <a href="https://www.wevolver.com/profile/ravi.rao" target="_blank">Ravi Y Rao</a>. It&#39;s the fourth article from the Improving Lives with Digital Healthcare Series. Future articles will introduce readers to some more interesting applications of electronics in healthcare.<hr>Introductory article covered the f<a href="https://www.wevolver.com/article/biomedical-instruments-and-the-future-of-digital-healthcare/draft" rel="noopener noreferrer" target="_blank">undamentals of biomedical instruments and the ways in which digitizing them is transforming healthcare.&nbsp;</a>Article 1 explored the&nbsp;<a href="https://www.wevolver.com/article/design-challenges-in-consumer-and-medical-wearables" rel="noopener noreferrer" target="_blank">design challenges in Consumer and Medical wearables.</a>&nbsp;It showcased how technologies once limited to hospitals are now made available to everyone for monitoring personal health.Article 2&nbsp;was focused on the present state of robotic surgery. <a href="https://www.wevolver.com/article/connectivity-solutions-for-the-next-generation-of-surgical-robots" rel="noopener noreferrer" target="_blank">It explained how advancements in robotics and communication, combined with the expertise of surgeons, enable customized treatments for patients.&nbsp;</a>Article 3 presents an&nbsp;<a href="https://www.wevolver.com/article/sensing-and-energy-systems-for-minimally-invasive-cardiovascular-surgeries" rel="noopener noreferrer" target="_blank">overview of how new sensing, communication, and energy systems, engineered for the healthcare sector can be used to transform cardiovascular disease treatment procedures.</a>Article 4 examined&nbsp;<a href="https://www.wevolver.com/article/next-generation-medical-devices-for-brain-computer-interfaces" rel="noopener noreferrer" target="_blank">Brain-Computer Interfaces and how they help in enhancing human vision, motor recovery for disabled limbs, and more.</a>Article 5 featured an <a href="https://www.wevolver.com/article/designing-medical-devices-for-real-life-an-interview-with-molex-printed-circuit-solutions" rel="noopener noreferrer" target="_blank">informative webchat between Mike Depp, and Glen Capek from Molex, as they discussed trends in flexible electronics driving new solutions for medical wearables applications.&nbsp;</a>Article 6 discussed&nbsp;<a href="https://www.wevolver.com/article/immersive-technologies-in-medical-education" rel="noopener noreferrer" target="_blank">how immersive digital technologies like Augmented Reality (AR) and Virtual Reality (VR) make medical learning more engaging through lifelike experiences.</a>Article 7 <a href="https://www.wevolver.com/article/enabling-early-diagnosis-and-better-postoperative-care-with-remote-health-monitoring-devices" rel="noopener noreferrer" target="_blank">explained the role of wearable devices and their enabling technologies in revolutionizing continuous health monitoring.&nbsp;</a>Final article was&nbsp;<a href="https://www.wevolver.com/article/the-next-generation-of-medical-technologies-digitized-miniaturized-and-connected" rel="noopener noreferrer" target="_blank">a roundup of the entire series that tried to give readers a snapshot of the potential of medical technologies in the present times.</a><hr><h2 dir="ltr">About the sponsor: Mouser Electronics</h2>Mouser Electronics is a worldwide leading authorized distributor of semiconductors and electronic components for over 1,100 manufacturer brands. They specialize in the rapid introduction of new products and technologies for design engineers and buyers. Their extensive product offering includes semiconductors, interconnects, passives, and electromechanical components.<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjU4NDAxMzUxMTY4LTE2NTg0MDEzNTExNjgucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" class="fr-fic fr-dii"><h2 dir="ltr">References</h2>[1] Next-gen devices for Brain-Computer Interfaces, Mouser, [Online], Available from: <a href="https://www.mouser.com/blog/next-generation-medical-devices-bci" target="_blank">https://www.mouser.com/blog/next-generation-medical-devices-bci</a>

Article #4 of Improving Lives with Digital Healthcare Series: Transferring information directly between the human brain and a computer can unlock the possibilities of using machines as extended parts of the human body.

Next-Generation Medical Devices for Brain-Computer Interfaces

<a href="https://brooksrehab.org/" target="_blank">Brooks Rehabilitation</a> recently announced that the MagTrack study, a collaborative research endeavor with the&nbsp;<a href="https://www.ece.gatech.edu/" target="_blank">Georgia Tech School of Electrical and Computer Engineering</a>, has been successfully completed. Feedback from the Brooks clinical team and its patients has allowed Georgia Tech engineers to transform their early research prototype into a user-ready version that was tested by more than 17 power wheelchair users living with tetraplegia — a form of paralysis caused by spinal cord injury that affects the arms, hands, trunk, legs, and pelvic organs.The collaboration between the Brooks and Georgia Tech teams has created a path to a first-of-its-kind, innovative application for individuals living with disabilities. The teams — comprised of physicians, clinical therapists, and engineers — brought together multidisciplinary expertise in advanced science, technology, and clinical rehabilitation.“We met with the Georgia Tech team years ago, when we first heard of the research breakthroughs they were achieving for wheelchair users. Brooks is constantly looking for technology that is useful for our patient population living with spinal cord injuries and mobility impairments. To see where the MagTrack project has advanced even just since the early stages of this study is incredible,” said Geneva Tonuzi, medical director of the spinal cord injury program, the spinal cord injury and related disorders day treatment program, and Cyberdyne HAL Therapy at Brooks Rehabilitation.As a result of this engineering-clinical collaboration,&nbsp;<a href="https://magtrack.ece.gatech.edu/" target="_blank">MagTrack</a> was created as a cutting-edge assistive technology that enables power wheelchair users to control their connected devices (e.g., smartphone, computer) and drive their power wheelchairs using an alternative, multimodal controller. In addition, the assistive device is designed to be wearable, wireless, and adaptable to the user’s specific condition.The MagTrack study is earning the praise of patients and scientists alike and has been&nbsp;<a href="https://ieeexplore.ieee.org/document/9540352" target="_blank">published</a> in&nbsp;<a href="https://www.embs.org/tbme/" target="_blank">IEEE Transactions on Biomedical Engineering</a>. From the beginning, the MagTrack studies have tested the performance of the Head-Tongue Controller (HTC), an earlier version of the MagTrack technology, on its ability to perform complex human-machine interactions that will enhance users’ quality of life. The MagTrack’s HTC allows the user to perform a variety of complex tasks in a single controller through the use of tongue and head movements, which are detected by eyewear and a tiny tracer that is temporarily glued onto the tongue using Glustitch’s&nbsp;<a href="https://periacryl.com/" target="_blank">PeryAcryl</a> bio-compatible adhesive. Target-specific commands are generated from these motions using advanced data processing and machine learning models. This combination of input modalities allows the user to perform a variety of daily functions with customizable control, from performing complex computer tasks (e.g., mouse navigation, scrolling, drag-and-drop) to completing advanced driving maneuvers when connected to a power wheelchair.In the latest study, researchers connected the MagTrack technology to a single power wheelchair donated by Quantum Rehab, and recruited 17 patient volunteers from Brooks Rehabilitation to test the functionality and usability of the device by completing a set of both simple and advanced driving tasks. Results showed that new users of MagTrack can complete these tasks as fast, and sometimes even faster, with the MagTrack’s HTC rather than their personal, alternative controller. Since the study session lasted less than 3 hours — and in a power wheelchair that wasn’t their own — it is anticipated that participants would be more proficient, and thus perform better with MagTrack, if they were given more time to familiarize themselves with its multimodal capabilities and using their own power wheelchair."Working with all of the participants has been very rewarding,” said Jesse Milliken, speech-language pathologist in the spinal cord injury program at&nbsp;<a href="https://brooksrehab.org/" target="_blank">Brooks Rehabilitation Hospital</a>. “Each patient who came in was someone who has been directly affected by a spinal cord injury and who can truly benefit from this technology. It was amazing to see how their faces lit up when they saw they were able to control their wheelchair with such ease and comfort. They all said they can see this improving their day-to-day lives if it were available to them. It's been such an honor to be a part of this process and see the work and thought process behind such advanced technology." Patients from the&nbsp;<a href="https://brooksrehab.org/" target="_blank">Brooks</a> spinal cord injury program who have participated in phase three of the study call the advancement “exciting” and a “great system that can be used for so many things.” After experiencing the technology themselves, they believe it will “touch the lives of those who are able to use it.”To date, the head array and sip-and-puff are the most common alternative controllers recommended by physical therapists to individuals living with tetraplegia, while specialized switches and joystick technology are available for those with mobility in their upper extremities. These technologies were developed many decades ago for the basic need of controlling a power wheelchair. Since then, a lack of innovation in this field has hindered these assistive technologies from adapting to today’s technology. Furthermore, they are affixed to the wheelchair, which becomes inaccessible once the user is transferred to a bed, a couch, or any location away from the wheelchair. Therefore, there is a growing need for this population to have access to new, alternative controllers that will enable them to be active members of an interconnected digital world.“The trajectory of the MagTrack study shows an unprecedented possibility for the advancement of independent function as well as mobility for electric wheelchair users. Our team and partners are energized and motivated by the recent patient trials to continue to push this technology and its capabilities as far as possible. This technology can significantly improve people’s lives. We will continue to work to see these advances in assistive technology come to life,” said Georgia Tech’s Omer T. Inan, director of the Inan Research Lab, Linda J. and Mark C. Smith Chair in Bioscience and Bioengineering, associate professor in the School of Electrical and Computer Engineering, and adjunct associate professor in the Wallace H. Coulter Department of Biomedical Engineering.“MagTrack is an innovative assistive technology aimed for those living with physical paralysis to have access to more complex human-machine interactions, which will facilitate the control of more devices in their everyday life that they cannot easily use otherwise,” said Nordine Sebkhi, postdoctoral researcher at the Inan Research Lab in the School of Electrical and Computer Engineering. “The development of our wearable alternative controller eliminates the need for having multiple assistive technologies, replacing them with a single multimodal and integrated system.” Sebkhi is the co-creator of MagTrack and the technical lead in the development of this assistive technology.As a result of these studies,&nbsp;<a href="https://magtrack.ece.gatech.edu/" target="_blank">MagTrack</a> has been refined to offer a fully integrated, all-in-one experience so that a user can seamlessly switch between driving their wheelchair and controlling connected devices in their surroundings (e.g., smartphone, computer, automated door opener, smart TV). The system can be used anywhere since it is wearable, and its built-in wireless connectivity facilitates portability.The team at Georgia Tech is already working on a new version of&nbsp;<a href="https://magtrack.ece.gatech.edu/" target="_blank">MagTrack</a> that is not only more inconspicuous, but also includes detection of facial gestures that significantly augments its control capabilities. Thanks to a grant from the&nbsp;<a href="https://gra.org/" target="_blank">Georgia Research Alliance</a>, this new version of MagTrack will be tested in a focus group at the&nbsp;<a href="https://www.shepherd.org/" target="_blank">Shepherd Center</a> and in simulated testing in a home-like environment. In the coming year, the team plans to make MagTrack available to early adopters for at-home validation testing to further improve the technology before pursuing commercialization.This successful study is only a start since, at its core, MagTrack is a new type of body motion tracking. The Georgia Tech team is working on various designs of MagTrack to be used as a wearable articulograph for motor speech disorders, as a hand and joint tracking system for physical rehabilitation, and even as a finger tracking for VR/AR applications. The MagTrack team will be partnering with the&nbsp;<a href="https://gcmiatl.com/" target="_blank">Global Center for Medical Innovation</a> to assist in regulatory strategy and project planning to transition the technology from the lab to the market.

A squint is an example of a facial gesture that MagTrack can detect and issue a discrete command to control a connected device.

Clinical work begins with MagTrack, a cutting-edge assistive technology that enables power wheelchair users to control their connected devices and drive their power wheelchairs using an alternative, multimodal controller.

MagTrack Technology Opens Doors for Independent Operation of Smartphones, Computers, and Other Devices for Wheelchair Users

In this episode, we talk about an initiative from EPFL to allow those with spinal cord injuries to control robots for help with day-to-day tasks and MIT’s bug robots that are taking big strides for small scaled bio-robotics. As always, you can find these and other interesting &amp; impactful engineering articles on Wevolver.com.&nbsp;<iframe height="200px" width="100%" frameborder="no" scrolling="no" seamless="" src="https://player.simplecast.com/32c37b88-b3ef-47fb-9e84-e99e608e1ed2?dark=false" class="fr-draggable"></iframe> <hr>This podcast is sponsored by <a href="https://www2.mouser.com/" id="isPasted" rel="nofollow" target="_blank">Mouser Electronics</a>.&nbsp;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1642495419840-Mouser-sponsor-1-a.jpg" style="width: 766px;" class="fr-fic fr-dib"><hr><h3 id="isPasted">EPISODE NOTES</h3>(2:25) -&nbsp;<a href="https://www.wevolver.com/article/mind-controlled-robots-now-one-step-closer/" target="_blank">Mind-controlled Robots 🧠🦾</a>(12:25) -&nbsp;<a href="https://www.wevolver.com/article/giving-bug-like-bots-a-boost/" target="_blank">Bug Robots 🐞🤖</a>Episode 53 was brought to you by Mouser Electronics, Farbod &amp; Daniel’s favorite electronics distributor. Click <a href="https://resources.mouser.com/medical/drawing-a-blank-on-brain-computer-interfaces" target="_blank">here&nbsp;</a>to read the article giving an overview of brain-computer interface technologies.<hr>About the podcast:Every day, some of the most innovative universities, companies, and individual technology developers share their knowledge on Wevolver. To ensure we can also provide this knowledge for the growing group of podcast listeners, we started a collaboration with two young engineers, Daniel Scott Mitchell &amp; Farbod Moghaddam who discuss the most interesting content in this podcast series.&nbsp;To learn more about this show, please visit the <a href="https://www.wevolver.com/profile/the.next.byte" target="_blank">shows page</a>. By following the page, you will get automatic updates by email when a new show is published.Be sure to give us a follow and review on <a href="https://podcasts.apple.com/nl/podcast/the-next-byte/id1548255861?l=en" rel="nofollow" target="_blank">Apple</a> podcasts, <a href="https://open.spotify.com/show/6lPPsRtegnivsRUEsQ09R7?si=IPMiah7xT_apJtPjqvXMlA" rel="nofollow" target="_blank">Spotify</a>, and most of your favorite podcast platforms!Take a few seconds to leave us a review. It really helps!&nbsp;<a href="https://apple.co/2RIsbZ2" rel="nofollow" target="_blank">https://apple.co/2RIsbZ2</a>If you do it and send us proof, we’ll give you a shoutout on the show.

In this episode, we talk about an initiative from EPFL to allow those with spinal cord injuries to control robots for help with day-to-day tasks and MIT’s bug robots that are taking big strides for small scaled bio-robotics.

brain–machine interface (BMIs)

Profiles