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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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Surface Electromyography Artifacts

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Dr. Rishabh Pathak

Surface electromyography (EMG) artifacts in EEG recordings are a common type of artifact caused by electrical activity in muscles near the recording electrodes.

1.     Description:

o    Surface EMG artifacts result from the electrical activity of muscles near the EEG electrodes, contaminating the EEG signal with muscle-generated electrical signals.

2.   Characteristics:

o High Amplitude: EMG artifacts often have higher amplitudes compared to brain-generated signals, making them easily distinguishable.

o    Frequency: Surface EMG artifacts typically exhibit higher frequencies, especially during muscle contractions.

o    Localization: These artifacts commonly occur in regions with underlying muscles, such as the frontalis and masseter muscles.

3.   Identification:

o    EMG artifacts can be identified by their distinct waveform characteristics, higher amplitudes, and frequency ranges that differ from typical EEG patterns.

o    The presence of sharp contours and less rhythmicity in the waveform can help differentiate EMG artifacts from brain-generated activity.

4.   Distinguishing Features:

o    EMG artifacts may co-localize with regions of maximum beta activity, resembling beta activity but with waveform differences.

o    The waveform of EMG artifacts is sharper and less rhythmic, especially when the high-frequency filter is set above 50 Hz.

o    EMG artifacts within the beta frequency range may appear as individual EMG potentials with durations of less than 20 milliseconds, separated by intervals that give them a beta frequency range appearance.

5.    Clinical Impact:

o    Proper identification and mitigation of surface EMG artifacts are crucial for accurate EEG interpretation and diagnosis.

o    Failure to recognize and address EMG artifacts can lead to misinterpretation of EEG findings and incorrect clinical decisions.

Understanding the characteristics and impact of surface EMG artifacts is essential for EEG technologists and clinicians to ensure the quality and reliability of EEG recordings for accurate clinical assessments and patient care.

 

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