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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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Electrode Artifacts Compared to Ocular Artifacts

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Dr. Rishabh Pathak

Electrode artifacts and ocular artifacts are distinct types of artifacts that can affect EEG recordings. 

1.     Electrode Artifacts:

oDescription: Electrode artifacts typically manifest as brief transients limited to specific electrode channels or low-frequency rhythms across scalp regions.

oCauses: These artifacts can result from electrode pops, poor electrode contact, electrode/lead movement, perspiration, salt bridge formation, or patient movements.

oLocalization: Electrode artifacts are often limited to the channels of one electrode, reflecting specific disturbances in signal acquisition.

oWaveform: Electrode artifacts, such as electrode pops, exhibit characteristic waveforms with rapid rises and slower falls, distinct from genuine EEG activity.

2.   Ocular Artifacts:

oNature: Ocular artifacts arise from eye movements, including slow roving eye movements that produce rhythmic activity with phase reversals.

oCharacteristics: These artifacts are involuntary, repeated horizontal ocular movements that can resemble perspiration artifacts in frequency and field distribution.

oField Reversal: Ocular artifacts demonstrate phase reversals due to the dipoles created by eye movements, distinguishing them from other artifact types.

oLocalization: Ocular artifacts typically affect frontal-temporal electrodes and exhibit a broad, bifrontal field, contrasting with the more localized nature of electrode artifacts.

3.   Differentiation:

oRhythmicity: Ocular artifacts exhibit regular rhythmicity and phase reversals due to eye movements, while electrode artifacts lack this specific pattern.

oField Distribution: The field distribution of ocular artifacts, especially the bifrontal nature, differs from the more localized effects of electrode artifacts.

oWaveform Comparison: Comparing the waveform characteristics, including rise and fall times, can help differentiate between electrode and ocular artifacts in EEG recordings.

Understanding the distinct features of electrode artifacts and ocular artifacts is crucial for accurate interpretation and identification of EEG disturbances. Proper recognition and differentiation of these artifacts contribute to the quality and reliability of EEG data analysis in clinical and research settings.

 

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