ISIR - Institut des Systèmes Intelligents et de Robotique’s Post

Discover how our #Enobio EEG device was used in this study, making significant strides in detecting #stress levels through #EEG, showcasing the power of integrating machine learning with electroencephalography to classify mental states effectively. 🧠 The study captured EEG signals from 25 subjects in relaxed and mentally tasked states. Advanced classifiers like the cubic SVM showcased remarkable accuracy, achieving up to 95.83% in distinguishing between these states. This #research not only highlights the efficacy of EEG and our Enobio device but also sets a new standard in mental health #diagnostics. 🏥 👉 Dive deeper here: https://lnkd.in/d6nsx27W Congrats to Rajendran V G, Jaya Prof.S.Jayalalitha, Kanagasabai Adalarasu, and Mathi Ramalingam, from SASTRA UNIVERSITY for their work on this amazing study! #Medtech #Electroceuticals #Technology #Healthtech #Healthcare #Science #Innovation #Study #ResearchArticle #Neuroscience #Neurotechnology #Electroencephalography Springer Nature Group

🧠 A team of researchers, led by Nicholas Card, Sergey Stavisky, and David Brandman, MD, PhD at the University of California, Berkeley, University of California, Davis, developed a brain-computer interface to accurately decode speech from neural signals with relatively little calibration. They’ve developed a brain-computer interface that’s helping a paralyzed man speak again by translating brain signals into speech in real-time and could mean a lot for those with severe paralysis. #BrainComputerInterface #Neurotech #Innovation #HealthcareTech #BCI #FutureOfHealthcare #MedicalBreakthrough #PatientEmpowerment #Neuroscience #TechForGood #DigitalHealth #Accessibility #AssistiveTech Nicoletta F. Prandi José Manuel Muñoz Sophie Valentine Harry Lambert Vasyl Mykytiuk Dr Romeo RACZ EngD MRSB Allan McCay Maite Sanz de Galdeano Arocena link below 🔗 👇 https://lnkd.in/eidrdnR6

If you learn to ride a bike as a child, then don’t ride for years, you’re likely to be able to pick it back up as an adult without missing a beat. And if you later learn a similar motor skill, like how to roller skate, the new memory doesn’t interfere with the old one.  How does our brain manage these feats? New research from Duke University School of Medicine shows that the process is more complex and requires more storage capacity than previously thought.  Using a novel technique for tracking learning and recording brain activity long term in mice, researchers led by Nuo Li, PhD, associate professor in the Duke Department of Neurobiology, found that when learning to perform an action, the brain stores not only the memory of the task, but also the specific context in which the action is performed. That means that the brain stores multiple copies of memory, even for the same action, he said. “This ability to continuously acquire new skills without forgetting old ones comes naturally to humans but is a major challenge for artificial intelligence.” - Nuo Li, PhD, neuroscientist at Duke University School of Medicine https://lnkd.in/eH-E_Xuj

So... doing a rough calculation, using my own neurons, if a fly brain has about 130K neurons, and 50 million interconnects, that's about 38 interconnects per neuron. Scale up to the human brain that has reportedly around 86 billion neurons, , except the human brain actually has far more interconnects (synapses) per neuron, with estimates up to 600 trillion interconnects, and all that with a power consumption of apparently around 20W, with whole human power consumption apparently around 140W. Looking at those sort of numbers and comparing with #AI, fear that it's somehow going to supplant humans is simply unfounded. It needs hundreds of MW of power to do some of the things a human can for a fraction of the energy consumption, besides, we can use a wide variety of energy sources, and even prepare them for consumption. (Try to get an AI to bake a loaf of bread, and then power itself for a day on it.) Remember how we were all told to switch to energy efficient LED light bulbs rather than incandescent bulbs and heat pumps rather than radiant heaters to save costs, and the planet? When it comes to neural networks, we are the energy efficient model (If we want to be. Executives who want to trip around the world in private jets aren't exactly energy efficient, and might be better replaced with an AI) What AI is good at doing is learning to recognise patterns in large volumes of data. It's pretty wasteful using it to write poetry or create art when humans are pretty good at doing this and actually enjoy it. There are use cases where there are large volumes of data with patterns that humans would find boring and monotonous to analyse, but where the data is useful. These scenarios are more likely to be specific, and more energy efficient than trying to replicate all human knowledge. They're where AI should be focused. Going back to the light bulb, if you want light, then an LED bulb is far more efficient than an incandescent one, however if you want heat and light, for example for an incubator to hatch chicken eggs, then an incandescent bulb is more useful than an LED one. The polarised opinions that #AI will either save the world or destroy it are both off the mark, although serious damage to the planet is a concern, but through excessive resource usage, not specifically because it's AI. Neither humans nor AI are even remotely near being able to engineer an artificial neural network with the energy efficiency of a biological one, and an AI may be even less likely to do so than humans, as AI is often good at making inferences from existing data, but if there's no existing data to go on, humans can think outside the square and try something totally novel, whereas an AI can't. The fly brain reveals how remarkable biological neural networks are.

In neuroscience and biomedical engineering, accurately modeling the complex movements of the human hand has long been a significant challenge.

【Medicine/Health】 Meta-Learning of Motor Skills in the Dorsal Premotor Cortex of the Brain Researchers at University of Tsukuba have discovered that the dorsal premotor cortex serves a "meta-learning" function, overseeing and regulating physical movements. Once believed to be limited to movement planning, this region has now been shown, through computational modeling and brain stimulation, to also facilitate the retention and forgetting of motor memories. Read more details here; https://lnkd.in/gnhx3y8V Original Paper; https://lnkd.in/gyBuY2Bm

Thrilled to announce the publication of our latest IEEE paper titled 'Classification of Neurological Disorders from EEG using Deep Learning Frameworks'. It's been an incredible journey with Hisana Thasneem ARDRA S S Lena Rose Joseph delving into the intersection of neuroscience and AI. A special shoutout to our mentor and Project guide, Prof. Jacob Mathew (Assistant Professor - Government Engineering College Barton Hill, Thiruvananthapuram), Project coordinator Prof.Dr.Rajeev Rajan (Associate Professor- Government Engineering College, Barton Hill, Thiruvananthapuram Engineering College, Barton Hill, Thiruvananthapuram). As an engineer, this accomplishment wouldn't have been possible without the invaluable support we received in navigating the intricate world of medical terminologies. Huge thanks to Dr. Thomas Iype (Retd. HOD Dept. of Neurology Government Medical College, Thiruvananthapuram), and Dr. Praveen Panicker (Dept. of Neurology Government Medical College, Thiruvananthapuram) for their guidance and expertise, which enabled us to bridge the gap between engineering and neuroscience. You can access the full paper and explore the research findings here : Open to discussing inquiries and exploring potential collaborations in the realm of patents. Feel free to reach out! #NeuroTech #DeepLearning #IEEEPublication #Neuroscience #Patents #Collaboration #Innovation

"The task of analyzing this enormous amount of data was made possible using machine learning, demonstrating the potential for AI technology to revolutionize neuroscience..." reads part of the article. Artificial intelligence really will revolutionise medicine. Check it out here 👇👇 https://lnkd.in/duibTWDS

In an age where the digital and organic worlds are increasingly intertwined, brain-machine interface (BMI) stands at the forefront of this revolutionary convergence. As we’ve explored in previous articles, Neuralink has made significant strides in this domain, promising to augment human capabilities and offer new remedies to neurological disorders. Yet, Neuralink is but one luminary in the expansive universe of neurotechnology. This article seeks to widen the lens beyond the well-publicized endeavors of Elon Musk’s enterprise, which we’ve discussed in detail (“Neuralink Technology,” FindLight Blog), and shed light on the broader spectrum of companies and technologies vying to connect the human brain directly to machines. The concept of BMIs echoes the themes we’ve touched upon in “Introduction to Bionics” and “Artificial Vision Aids“, where the fusion of human physiology with advanced technology is not just a staple of science fiction, but a tangible reality transforming lives. As 2024 unfolds, we are witnessing an acceleration in BMI innovation—a testament to the field’s dynamic and multifaceted nature. In this comprehensive review, we’ll traverse the current landscape of BMI technology, spotlighting the frontrunners, the scope of their focus, and the potential trajectories to watch in this thrilling nexus of neuron and network. Contents in this article: 1. The Rise of Brain Machine Interface Technology 2. Beyond Neuralink: Key Players in Brain Machine Interface Development 3. Diverse Applications of Brain Machine Interfaces 4. Emerging Trends in Brain Machine Interface Research 5. Companies to Watch in the BMI Space 6. Challenges and Ethical Considerations in BMI 7. Development 8. Conclusion 9. References and Further Reading #BCI #neuralink #brain #engineering #application #convergence #ethics https://lnkd.in/gnYH7ANw