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Energy Manifold Natural Gradient Descent: From Riemannian Optimization to Modern Neuroscience, NeuroAI and Quantum Physics

Dr. Rishabh Pathak
When Geometry, Energy, Artificial Intelligence and Neuroscience Converge Modern Artificial Intelligence is rapidly moving beyond the idea that learning simply means minimizing an error function. Increasingly, researchers are asking a deeper question: what is the structure of the space in which learning takes place? This question becomes particularly important when the system being modelled is constrained, nonlinear, dynamic, or governed by physical principles. A recent work titled “Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers” , by Zhangyong Liang and Huanhuan Gao, introduces Energy Manifold Natural Gradient Descent (EMNGD) , a mathematical framework that extends energy-based natural-gradient optimization from unconstrained Euclidean parameter spaces to constrained Riemannian parameter manifolds . At its core, the framework proposes a simple but powerful principle: An optimization algorithm should not only determine how to reduce error; it sh...
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Glossokinetic Artifacts

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Dr. Rishabh Pathak

Glossokinetic artifacts in EEG recordings are a type of artifact related to movements of the tongue muscles. 

1.     Description:

o    Glossokinetic artifacts are artifacts caused by movements of the tongue muscles, which can introduce electrical activity that contaminates the EEG signal.

2.   Characteristics:

o Location: Glossokinetic artifacts typically affect electrodes near the mouth or tongue region where the muscle movements are occurring.

o Amplitude: The amplitude of glossokinetic artifacts may vary depending on the intensity of tongue movements and the proximity to the recording electrodes.

3.   Identification:

o Glossokinetic artifacts can be identified by their association with specific muscle movements in the tongue region and their distinct waveform characteristics.

o These artifacts may manifest as sudden changes in the EEG signal coinciding with tongue movements.

4.   Clinical Relevance:

oRecognizing glossokinetic artifacts is important for distinguishing them from genuine brain activity during EEG interpretation.

o  Failure to identify and differentiate glossokinetic artifacts can lead to misinterpretation of EEG findings and incorrect clinical assessments.

5.    Artifact Mitigation:

oStrategies to mitigate glossokinetic artifacts include minimizing movements in the tongue region during EEG recordings and ensuring proper electrode placement to reduce artifact contamination.

oSignal processing techniques, such as artifact removal algorithms, can also help in reducing the impact of glossokinetic artifacts on EEG data quality.

Understanding the characteristics and implications of glossokinetic artifacts is essential for EEG technologists and clinicians to ensure accurate interpretation of EEG recordings and reliable clinical assessments. Proper identification and management of glossokinetic artifacts contribute to obtaining high-quality EEG data for effective diagnosis and treatment planning.

 

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