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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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Muscles Artifacts

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Dr. Rishabh Pathak

Muscle artifacts in EEG recordings can arise from various sources, such as facial muscles, scalp muscles, or body movements, and can significantly impact the quality of the EEG signal. 

1.     Facial Muscle Artifact:

o    Description: High amplitude, fast activity across bilateral anterior regions due to facial muscle contraction.

o    Characteristics:

§  Distribution: Reflects the locations of the muscles generating the artifact.

§  Activity Onset/Offset: Begins and ends abruptly without preceding or following EEG changes.

o    Frequency: Typically, high-frequency activity.

2.   Muscle and Movement Artifact:

o Description: Combination of high-frequency muscle artifact and low-frequency movement artifact.

o    Characteristics:

§  Sequence: Evident movement artifact precedes bursts of muscle artifact.

§  Location: Muscle artifact occurs between PLEDs on the right side with a maximum at the C4 electrode.

o Frequency: Low-frequency movement artifact and high-frequency muscle artifact.

3.   Key Points:

o  Source: Muscle artifacts can originate from various muscle groups, including facial muscles, scalp muscles, or body movements.

o Amplitude: Muscle artifacts often have high amplitudes compared to brain-generated activity.

oFrequency: Muscle artifacts typically exhibit higher frequencies, especially during muscle contractions.

o Localization: The distribution of muscle artifacts on EEG channels can provide clues to their source.

o Impact: Muscle artifacts can obscure underlying brain activity and affect the interpretation of EEG recordings.

Understanding the characteristics and sources of muscle artifacts is crucial for EEG technologists and clinicians to differentiate them from genuine brain activity. Proper identification and mitigation of muscle artifacts contribute to obtaining high-quality EEG recordings for accurate clinical interpretation and diagnosis.

 

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