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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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Distinguishing Features of Muscles Artifacts

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Dr. Rishabh Pathak

Muscle artifacts in EEG recordings can arise from various sources, including movements of facial muscles, tongue muscles, or other muscle groups. 

1.     Location:

o Muscle artifacts typically affect electrodes located near the muscle groups generating the artifact. For example, facial muscle artifacts may be prominent in electrodes overlying the face, while glossokinetic artifacts may impact electrodes near the mouth or tongue region.

2.   Waveform:

o  Muscle artifacts often exhibit high-frequency, fast activity on EEG recordings. The waveform may appear as sharp spikes or fast oscillations, reflecting the rapid muscle contractions that produce the artifact.

3.   Onset and Offset:

o  Muscle artifacts typically have abrupt beginnings and endings without preceding or following EEG changes. This sudden onset and offset distinguish muscle artifacts from genuine brain activity, which usually shows more gradual transitions.

4.   Amplitude:

o Muscle artifacts can have variable amplitudes depending on the intensity of muscle contractions and the proximity of the electrodes to the muscle source. Higher muscle activity may result in larger artifact amplitudes.

5.    Rhythmicity:

o Some muscle artifacts, such as photomyogenic artifacts triggered by visual stimuli, may exhibit rhythmic patterns corresponding to the frequency of the muscle contractions. This rhythmicity can help differentiate muscle artifacts from other types of EEG activity.

6.   Association with Movement:

o  Muscle artifacts are often associated with specific movements or muscle contractions. For example, glossokinetic artifacts are linked to tongue movements, while facial muscle artifacts correspond to facial expressions or movements.

7.    Response to Stimulation:

o  Certain muscle artifacts, like photomyogenic artifacts, may be elicited or modulated by external stimuli, such as flashing lights during photic stimulation. Understanding how these artifacts respond to stimuli can aid in their identification and differentiation from intrinsic brain activity.

8.   Interference with EEG:

o  Muscle artifacts can obscure genuine EEG signals due to their higher amplitudes and distinct waveform characteristics. Identifying and mitigating muscle artifacts are essential for accurate EEG interpretation and clinical decision-making.

Recognizing these distinguishing features of muscle artifacts is crucial for EEG technicians and clinicians to differentiate between genuine brain activity and artifact-induced signals. Proper identification and management of muscle artifacts contribute to obtaining high-quality EEG data for reliable clinical assessments and accurate diagnosis.

 

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