Thrilled to announce that I presented my cutting-edge research on Brain-Computer Interface for Emotion Classification at the Graduate Research Expo at Northeastern University! 🧠💻🔬 As an MS Data Analytics Scholar and Google Certified Python Expert, I had the privilege of showcasing my work alongside PhD candidates, engaging with brilliant minds in the field of neurotechnology and machine learning. My research introduces a novel Hybrid Ensemble Stacking Model (HESM) for EEG Classification, leveraging network analysis and advanced hyperparameter tuning techniques. Key innovations of our model: • Developed a computationally efficient HESM that outperforms traditional deep learning methods in EEG-based emotion classification • Implemented an adaptive sliding window approach to capture temporal dynamics in EEG signals • Utilized grid search and cross-validation for robust hyperparameter optimization • Integrated diverse base classifiers (SVM, Linear SVC, MLP, Logistic Regression) to enhance generalization and model interpretability Our HESM addresses critical challenges in BCI technology: - Individual variability in emotional EEG signatures - Complex, high-dimensional nature of EEG data - Real-time processing requirements for practical applications This research has significant implications for adaptive e-learning systems, potentially revolutionizing personalized education through emotion-aware content delivery. I'm grateful for the opportunity to contribute to the advancement of BCI technology and its real-world applications. Special thanks to my advisor, Prof. Sivarit Sultornsanee for their invaluable guidance. Excited to continue pushing the boundaries of EEG signal processing and emotion recognition! Let's connect if you're interested in neurotechnology, machine learning, or adaptive learning systems. Link to video presentation: https://lnkd.in/ehqKgKZs #GraduateResearch #BrainComputerInterface #MachineLearning #EEG #EmotionRecognition #HyperparameterTuning #EnsembleLearning #AdaptiveLearning #NeuroTech #DataScience #AI #NortheasternUniversity
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Did You Know? The human brain contains about 86 billion neurons, which send signals at a speed of up to 120 meters per second. 💡 Science Connection: Our 3D brain model helps learners visualize and understand how the brain’s structure supports its incredible functions. Hands-on learning transforms abstract facts into real understanding. #Humanbrain
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Is the future of brain-computer interfaces a promise of liberation or an invasive peek into our thoughts? 🧠 ➡️ Beyond the high-profile endeavors of Neuralink, companies like Synchron, Paradromics, and Precision Neuroscience are making significant strides in the development of brain-computer interfaces (BCIs). ➡️ These devices, ranging from non-invasive to deeply integrated, aim to transform lives, particularly for those with severe physical impairments. ➡️ Synchron's approach uses a stent-delivered electrode array, offering a less invasive installation that still captures essential data for basic commands. Meanwhile, Precision Neuroscience’s ultra-thin electrode arrays, akin to a piece of Scotch tape laid on the brain, represent a leap towards minimizing surgical risks. ➡️ These companies are not only competing in technological innovation but are also addressing profound ethical questions about the integration of advanced technology with the human brain. ❓ As these devices evolve, one must ponder: how will these innovations impact personal autonomy and privacy in a world increasingly driven by technology? Read the full story in the New York Times: https://lnkd.in/gDmQvUAd #BCI #Future #Privacy #Brain #Innovation ---- 💡 𝗜𝗳 𝘆𝗼𝘂 𝗲𝗻𝗷𝗼𝘆𝗲𝗱 𝘁𝗵𝗶𝘀 𝗰𝗼𝗻𝘁𝗲𝗻𝘁, 𝗯𝗲 𝘀𝘂𝗿𝗲 𝘁𝗼 𝗱𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗺𝘆 𝗻𝗲𝘄 𝗮𝗽𝗽 𝗳𝗼𝗿 𝗮 𝘂𝗻𝗶𝗾𝘂𝗲 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗯𝗲𝘆𝗼𝗻𝗱 𝘆𝗼𝘂𝗿 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 - you can have real-time insights, recommendations (a lot more than I share here) and conversations with my digital twin via text, audio or video in 28 languages! Join >5000 users who went before and go to app.thedigitalspeaker.com to sign up and take our connection to the next level! 🚀
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Researchers just created an artificial synapse which puts us one step closer to creating a brain-like computer. Theoretical and experimental physicists proved that a system mirroring the human brain could be built, marking a significant step forward in iontronic neuromorphic computing. #artificialsynapse #scienceandtechnology #neuromorphiccomputers #highspeedcomputing #computing #supercomputers #dataprocessing #chipmakers #futuretech #breakthrough #processors #cpu #gpu #aitechnology #aifuture #computing #artificialintelligence #genai #dataintelligence #neuromorphic #humanbrain #humantouch #aibusiness #computerscience #technologyadvancements #aiethics #ethicalai #artificialintelligencetechnology #ai #ai4good #aifuture #futuretrends #futuretechnology #futureofai #technologytrends #aiadoption #aiapplications #technologyinnovation #breakthrough #aievolution #aitrends #aiforall #aidevelopment #airace #airesearch #generativeai #llms #agi #llm #futuretechnology #superintelligence #humanbehaviour #expressivebehaviors #scientificresearch
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💡 Your Trusted Partner for Dissertation Success! 🎯 Tailored support to meet your academic needs. 📌 Explore cutting-edge MATLAB-based Artificial Intelligence (AI) topics: 🔹 Plant Disease Detection 🔹 Skin Disease Detection 🔹 Recommendation System 🔹 Renewable Energy Integration with Machine Learning 🔹 Epilepsy Detection from EEG Signals 🔹 Human Activity Recognition 🌐 Visit us at techsparks.co.in to learn more. ✨ Techsparks is always ready to help you! From topic selection to final submission, we've got you covered. #DissertationHelp #MATLAB #ArtificialIntelligence #Techsparks
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There are several fascinating implications for the future of large language models (LLMs) and AI in general: 1. Multimodal Capabilities: • The integration of multimodal retrieval—combining text, images, and other data types—demonstrates how LLMs are evolving beyond text-based capabilities to analyze and interact with complex datasets, such as those in neuroscience. 2. Impact on Neuroscience Research: • Using AI to analyze human brains at a cellular level, particularly across diverse demographics, shows how LLMs can drive precision in large-scale, intricate biological studies. This could revolutionize how researchers approach brain mapping and disease modeling. 3. Scalability and Personalization: • The ability to cater to diverse demographics suggests the potential for AI-driven models to offer personalized insights in medical research and applications, tailoring findings to individual or population-level variances. 4. Visual Question Answering: • Employing visual question answering highlights the growing sophistication of AI in interpreting and reasoning through complex visual data, paving the way for applications in other domains like radiology, genetics, and even space exploration. 5. Cross-Disciplinary Innovation: • The collaboration between AI and neuroscience underscores the power of cross-disciplinary approaches to solve complex challenges. It exemplifies how AI can unlock new frontiers in understanding human biology and beyond. 6. NVIDIA’s Role in Advancing LLMs: • As a leader in AI hardware and research, NVIDIA’s involvement suggests that the computational infrastructure required for such advanced applications is becoming more accessible and optimized for research institutions globally. 7. Broader Implications for AI in Science: • This use case hints at the broader potential of LLMs in scientific discovery, where they can serve as powerful tools for hypothesis generation, data interpretation, and collaboration. The intersection of LLMs, multimodal retrieval, and neuroscience points to a future where AI becomes integral to understanding and addressing some of humanity’s most complex biological and medical challenges. Thanks for sharing Sarbjeet Johal
A developer blogpost highlights how multi-modal retrieval is contributing to the advancement of neuroscience research at the Indian Institute of Technology Madras – IIT Madras Brain Centre. Leveraging AI technology, the center can now generate detailed analyses of entire human brains at a cellular level, catering to diverse demographics. The NVIDIA developers blog provides an insightful read on this innovative approach. Read more here: [NVIDIA Developers Blog - Advancing Neuroscience Research with Visual Question Answering and Multimodal Retrieval](https://lnkd.in/g6XnjmkJ) MP Nima Badiey
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🚀 Introducing: The Digital Brain aka BREN 🧠 I’m thrilled to share my latest article, where I dive deep into the fascinating intersection of neuroscience-inspired computing and artificial intelligence. This journey explores the integration of memory encoding, emotion detection, decision-making and cortical processing using Hyperdimensional Computing (HDC) and PyTorch. This piece is the culmination of my work modelling cognitive functions like the hippocampus, amygdala and visual cortex now unified into a comprehensive framework that aims to emulate a biological brain. 💡 Highlights Include: ▶ The mathematics and principles driving HDC-based cognition. ▶ Real-world applications in robotics, AI, mental health and more. ▶ Code snippets and walkthroughs for researchers and developers. ▶ The advantages of this approach over traditional methods. ▶ How this system mirrors human memory, emotion and reasoning. Whether you're in AI, cognitive science, or simply curious about bridging biology and technology, I hope this article sparks inspiration. 🌐 👉 Read it here #ArtificialIntelligence #HyperdimensionalComputing #CognitiveAI #Neuroscience #AIResearch #MemoryEncoding #EmotionDetection #DecisionMaking #DigitalBrain
Building a Digital Brain: Integrating Memory, Emotion and Decision-Making with Hyperdimensional…
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Need help with your dissertation? From topic selection to final submission, we provide comprehensive support to ensure your success. Avail MATLAB-Based Artificial Intelligence (AI) Topics: 🌿 Plant Disease Detection 🩺 Skin Disease Detection 🛍️ Recommendation System 🌞 Renewable Energy Integration with Machine Learning 🧠 Epilepsy Detection from EEG Signals 🚶 Human Activity Recognition Know more at techsparks.co.in. Let us help you achieve success in your AI thesis journey! 🚀 . . #ThesisSupport #ArtificialIntelligence #MATLAB #PlantDiseaseDetection #SkinDiseaseDetection #RecommendationSystem #RenewableEnergy #EpilepsyDetection #HumanActivityRecognition #ResearchHelp #AcademicSuccess #ThesisJourney
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I am excited to announce the publication of my latest research on developing machine learning models for an innovative emotion-classifying system using EEG signals. The study delves into various EEG features, such as time domain, frequency domain, auto-encoded, and deep features, leveraging the DENS dataset. Our investigation evaluates feature performance through Random Forest, XgBoost, and KNN, showcasing accuracies of 82.10% and 81.33% for valence and arousal, respectively. These results represent a notable improvement from previous findings and underscore the potential of EEG features in emotional state classification. Thank you DURGESH AMETA for the collaboration. #MachineLearning #DataScience #EmotionClassification #ResearchPublication.
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WTF* files: Organoid Intelligence Organoid intelligence (OI) is an innovative and multidisciplinary field that intersects computer science and biology. It focuses on the development of biological computing platforms. This field utilises 3D cultures of human brain cells, or brain organoids, to explore and create new computing paradigms. By leveraging these brain cells' natural processes and capabilities, OI aims to harness biological learning mechanisms, potentially offering advantages in automation, energy efficiency, and developing novel biocomputing models. This approach seeks to combine cutting-edge advancements in artificial intelligence (AI) with microphysiological systems (MPS) research, pushing the boundaries of synthetic biological intelligence and exploring the ultimate functionalities of organoid-based computing systems. #scottspeaks #futureliterate #perennialmindset #consilience #WTF *WTF = What's the future? Is the name of one of my keynotes, which includes the "Seven wonders of the future" and a protopian perspective on our possible futures, meaning the future will be better than today but not perfect!
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Well done Anuar Dorzhigulov for making it through the PhD defence. It's always good to see your students grow from one level to another. The only constant in life is the change. Every change brings about a new in you. The journey of Anuar from algorithms to mixed signal AI circuits is a good story of positive change. Interesting research that can open new directions in SNN AI circuits. All the very best for your future endeavours.
Congratulations to my student Anuar Dorzhigulov for successfully defending his Doctoral Dissertation! His dissertation is titled “Hybrid CMOS-NVM Integrated Circuits for Energy-Efficient Neuromorphic Computing and Edge-AI.” Anuar worked on several aspects of analog Neuromorphic chips, spanning devices, circuits, and systems. He published his work in Frontiers of Neuroscience and IEEE ISCAS. Notable accomplishments include a PyTorch-based device-aware framework for Spiking Neural Networks (SNNs), ConvNets on NVM crossbars with novel max-pooling and opamp re-use circuits, and VCO-based leaky integrate-and-fire neurons. Analog Neuromorphic and Edge-AI ICs have been in the spotlight for the promise of realizing low-power on-device AI, which is critically important for energy consumption and the carbon footprint of the existing data-center heavy AI computing chain. Best wishes to Anuar Dorzhigulov for his future publications and accomplishments! #UDECE #Chips #ICs #AI #EdgeAI #Neuromorphic
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