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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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What are some key features of photomyogenic artifacts in EEG recordings?

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Dr. Rishabh Pathak


Photomyogenic artifacts in EEG recordings are characterized by several key features that help distinguish them from other types of artifacts and brain activity. Here are the main features:


1.      Origin:

oPhotomyogenic artifacts are caused by involuntary muscle contractions, particularly in response to photic stimulation (e.g., strobe lights). These contractions can occur in facial or neck muscles, leading to electrical activity that is recorded by the EEG.

2.     Waveform Characteristics:

o The waveforms of photomyogenic artifacts typically have a sharp contour and may appear less rhythmic compared to other types of muscle artifacts. They can resemble EMG activity but are distinct in their response to photic stimulation.

3.     Frequency Content:

o Photomyogenic artifacts often contain high-frequency components, usually above 20 Hz, which can overlap with the frequency range of beta activity. This high-frequency content is a distinguishing feature that sets them apart from slower brain wave activity.

4.    Location:

o These artifacts are primarily observed in the frontal region of the scalp, where the underlying muscle activity is most pronounced. They may also be seen in other areas depending on the muscle contractions involved.

5.     Response to Stimulation:

o Photomyogenic artifacts can be time-locked to the photic stimulation, meaning they occur in synchronization with the strobe light. However, they may not always show a consistent pattern in relation to the stimulus frequency, making them less predictable than a well-formed photic driving response.

6.    Amplitude Variability:

o The amplitude of photomyogenic artifacts can vary significantly, often depending on the intensity of the muscle contractions and the individual's response to the photic stimulus. This variability can complicate their interpretation.

7.     Distinction from Other Artifacts:

o Photomyogenic artifacts can be differentiated from other types of artifacts, such as electroretinograms (which are time-locked to the stimulus and have a different waveform) and EMG artifacts (which may not be time-locked and can have a different frequency profile).

8.    Clinical Relevance:

o Recognizing photomyogenic artifacts is crucial in clinical settings, as they can mimic or obscure true neurological activity, potentially leading to misinterpretation of EEG findings.

By understanding these key features, clinicians and EEG technologists can better identify and interpret photomyogenic artifacts in EEG recordings, ensuring more accurate assessments of brain activity.

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